RiskPremium

commit fd3b9cf5745e473a04e1653624367e07a821ea48
parent cc2c0dc2a9d8cb7a09bc997e03c814b42fda1e5f
Author: Erik Loualiche <[email protected]>
Date:   Sun, 29 Mar 2026 22:23:53 -0500

Julia rewrite: construct CAY from FRED, update through 2024Q4

Rewrite pipeline from R to Julia. Construct cay from FRED data (DOLS on pre-COVID sample). Update CRSP through 2024Q4. Tag v1.0 for paper version.

Diffstat:
M
.gitignore
 | 
13
++++++++++---
M
Makefile
 | 
74
+++++++++++++++++++++++++++++++++++++++++++-------------------------------
A
Manifest.toml
 | 
1417
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A
Project.toml
 | 
12
++++++++++++
A
input/cay_computed.csv
 | 
302
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
D
log/R-session-info.log.R
 | 
157
-------------------------------------------------------------------------------
D
log/import_predictors.log.R
 | 
269
-------------------------------------------------------------------------------
D
log/rp_measure.log.R
 | 
259
-------------------------------------------------------------------------------
M
output/msi.csv
 | 
2343
++++++++++++++++++++++++++++++++++++++++---------------------------------------
M
output/predict.csv
 | 
549
+++++++++++++++++++++++++++++++++++++++++--------------------------------------
A
output/predict.pdf
 | 
0

M
output/predict.png
 | 
0

M
readme.md
 | 
134
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++--------------
D
rules.mk
 | 
46
----------------------------------------------
A
src/CAY.jl
 | 
197
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A
src/DataImport.jl
 | 
142
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A
src/FredUtils.jl
 | 
78
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A
src/RiskPremium.jl
 | 
179
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
D
src/readme_in.md
 | 
28
----------------------------
A
src/readme_input.txt
 | 
111
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A
test/test_cay.jl
 | 
100
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A
test/test_dataimport.jl
 | 
63
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
A
test/test_regression.jl
 | 
39
+++++++++++++++++++++++++++++++++++++++

23 files changed, 4276 insertions(+), 2236 deletions(-)
diff --git a/.gitignore b/.gitignore
@@ -1,15 +1,22 @@
 # .gitignore file for RiskPremium Repo
 
-
 ## STANDARD EXCLUSIONS
 archive
 docs/*.lyx
 tmp
+log
 .Rhistory
+.claude
 
 # Specific files
 docs/dividendpriceratio.tex
 input/msi.sas7bdat
 
 # Sources
-src/paymon.R-
\ No newline at end of file
+src/paymon.R
+
+# Superpowers
+docs/superpowers
+
+# Environment
+.env+
\ No newline at end of file
diff --git a/Makefile b/Makefile
@@ -1,65 +1,77 @@
 #
 # Makefile for PREDICTING THE EQUITY RISK PREMIUM
 #
-# Created       on June  04th 2019
-# Last modified on March 23rd 2020
-## --------------------------------------------------------------------------------------------------------
+# Julia rewrite — March 2026
+# --------------------------------------------------------------------------------------------------------
 
+SHELL := /bin/bash
 
+# --------------------------------------------------------------------------------------------------------
+# Pretty-printing
+define colorecho
+	@tput setaf 6
+	@echo $1
+	@tput sgr0
+endef
 
-## --------------------------------------------------------------------------------------------------------
-## LOAD A FEW OPTIONS
--include ./rules.mk
-## --------------------------------------------------------------------------------------------------------
+WHITE := '\033[1;37m'
+NC    := '\033[0m'
 
+TIME_START := $(shell date +%s)
+define TIME-END
+	@time_end=`date +%s` ; \
+	time_exec=`awk -v "TS=${TIME_START}" -v "TE=$$time_end" 'BEGIN{TD=TE-TS;printf "%02dm:%02ds\n",TD/60,TD%60}'` ; \
+	echo -e \\t${WHITE}cumulative time elapsed ... $${time_exec} ... $@ ${NC}
+endef
 # --------------------------------------------------------------------------------------------------------
+
+## --------------------------------------------------------------------------------------------------------
 ## ALL
 all: output/predict.csv readme.md
 
-##
-## DOWNLOAD THE DATA FROM WRDS
-## Edit rules.mk to change your user name under WRDS_USERNAME
-input/msi.sas7bdat: 
-	$(call colorecho,"Download stock market index from crsp ...")
-	rsync -aPvzh --stats --human-readable  ${WRDS_USERNAME}@wrds-cloud.wharton.upenn.edu:/wrds/crsp/sasdata/a_stock/msi.sas7bdat ./input/
+## COMPUTE CAY FROM FRED DATA
+input/cay_computed.csv: src/CAY.jl src/FredUtils.jl
+	$(call colorecho,"Construct cay from FRED data ...")
+	julia --project=. src/CAY.jl
 	@echo
 
 ## GENERATE PREDICTORS
-tmp/predict.csv: src/import_predictors.R input/cay_current.csv input/msi.sas7bdat
+tmp/predict.csv: src/DataImport.jl src/FredUtils.jl input/cay_computed.csv output/msi.csv
 	$(call colorecho,"Import and merge all return predictors ...")
 	mkdir -p tmp
-	R CMD BATCH $(R_OPTS) src/import_predictors.R log/import_predictors.log.R
+	julia --project=. src/DataImport.jl
 	@echo
 
 ## RUN REGRESSIONS
-output/predict.csv: src/rp_measure.R tmp/predict.csv
+output/predict.csv: src/RiskPremium.jl tmp/predict.csv
 	$(call colorecho,"Estimate predictive regression ...")
-	R CMD BATCH $(R_OPTS) src/rp_measure.R log/rp_measure.log.R
+	julia --project=. src/RiskPremium.jl
 	@echo
 
 ## OUTPUT RESULTS
-readme.md: src/readme_in.md output/predict.png tmp/reg_update.txt
+readme.md: src/readme_input.txt output/predict.pdf tmp/reg_update.txt
 	$(call colorecho,"Update readme file ...")
-	cat src/readme_in.md tmp/reg_update.txt > readme.md
+	cat src/readme_input.txt tmp/reg_update.txt > readme.md
+	@echo
+
+## TEST: validate against R reference
+test: test/test_cay.jl test/test_dataimport.jl test/test_regression.jl
+	$(call colorecho,"Running validation tests ...")
+	julia --project=. test/test_dataimport.jl
+	julia --project=. test/test_regression.jl
 	@echo
 
-##
 ## --------------------------------------------------------------------------------------------------------
 ## help (this call)
-.PHONY : help
-help : Makefile
+.PHONY: help test clean
+help: Makefile
 	@sed -n 's/^##//p' $<
 
 ## clean
-.PHONY : clean
 clean:
-	rm -rf ./output/*
-	rm -rf ./log/*.log*
-	rm -rf ./tmp/*	
+	rm -rf output/predict.csv output/predict.pdf
+	rm -rf log/*.log*
+	rm -rf tmp/*
 	rm -rf readme.md
 
-##
-# --------------------------------------------------------------------------------------------------------
-
-
-
+## --------------------------------------------------------------------------------------------------------
diff --git a/Manifest.toml b/Manifest.toml
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diff --git a/Project.toml b/Project.toml
@@ -0,0 +1,12 @@
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diff --git a/input/cay_computed.csv b/input/cay_computed.csv
@@ -0,0 +1,302 @@
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diff --git a/log/R-session-info.log.R b/log/R-session-info.log.R
@@ -1,157 +0,0 @@
-
-
-# -----
-# Session info for import_predictors
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-─ Session info ───────────────────────────────────────────────────────────────
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- pandoc   2.18 @ /usr/local/bin/pandoc
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-─ Packages ───────────────────────────────────────────────────────────────────
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-──────────────────────────────────────────────────────────────────────────────
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-
-# -----
-# Session info for rp_measure.csv
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-─ Session info ───────────────────────────────────────────────────────────────
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- wesanderson * 0.3.6   2018-04-20 [1] CRAN (R 4.1.0)
- withr         2.5.0   2022-03-03 [1] CRAN (R 4.1.2)
- zoo         * 1.8-10  2022-04-15 [1] CRAN (R 4.1.2)
-
- [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
-
-──────────────────────────────────────────────────────────────────────────────
diff --git a/log/import_predictors.log.R b/log/import_predictors.log.R
@@ -1,269 +0,0 @@
-
-R version 4.1.3 (2022-03-10) -- "One Push-Up"
-Copyright (C) 2022 The R Foundation for Statistical Computing
-Platform: x86_64-apple-darwin17.0 (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
-  Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-> #!/usr/bin/env Rscript  
-> #
-> # import_predictors.R
-> #
-> # This code creates or imports the predictors of aggregate equity returns
-> # There are three parts for T-bill, D/P ratio and cay 
-> # 
-> # 1. T-bill comes from the H15 release of the FRB 
-> #    we downloaded it directly from FRED at https://fred.stlouisfed.org/series/TB3MS
-> #
-> # 2. cay comes from Martin Lettau's website at http://faculty.haas.berkeley.edu/lettau/data_cay.html
-> #
-> # 3. D-P ratio is estimated from the MSI CRSP Files 
-> #    We use a method of continuously reinvested dividends
-> #    See attached LaTeX file for explanations of the procedure
-> #    Data is from CRSP and available at /wrds/crsp/sasdata/a_stock/msi.sas7bdat
-> #
-> # 4. Estimate future excess returns: we use a horizon of three years in the paper
-> #
-> # (c) Valentin Haddad, Erik Loualiche & Matthew Plosser
-> #
-> # Last updated on June 4th 2019
-> # 
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> message("Log file for code executed at\n")
-Log file for code executed at
-
-> message(format(Sys.time(), "%a %b %d %X %Y"))
-Mon Jun 06 12:38:01 2022
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> # APPEND REQUIRED PACKAGES
-> 
-> # See this https://stackoverflow.com/questions/4090169/elegant-way-to-check-for-missing-packages-and-install-them
-> using<-function(...) {
-+     libs<-unlist(list(...))
-+     req<-unlist(lapply(libs,require,character.only=TRUE))
-+     need<-libs[req==FALSE]
-+     if(length(need)>0){ 
-+         install.packages(need)
-+         lapply(need,require,character.only=TRUE)
-+     }
-+ }
-> 
-> package_to_load <- c("crayon", "devtools", "alfred", "haven", "dplyr", 
-+ 	"stringr", "lubridate", "RcppRoll", "statar", "data.table")
-> using(package_to_load)
-Loading required package: crayon
-Loading required package: devtools
-Loading required package: usethis
-Loading required package: alfred
-Loading required package: haven
-Loading required package: dplyr
-
-Attaching package: ‘dplyr’
-
-The following objects are masked from ‘package:stats’:
-
-    filter, lag
-
-The following objects are masked from ‘package:base’:
-
-    intersect, setdiff, setequal, union
-
-Loading required package: stringr
-Loading required package: lubridate
-
-Attaching package: ‘lubridate’
-
-The following objects are masked from ‘package:base’:
-
-    date, intersect, setdiff, union
-
-Loading required package: RcppRoll
-Loading required package: statar
-Loading required package: data.table
-
-Attaching package: ‘data.table’
-
-The following objects are masked from ‘package:lubridate’:
-
-    hour, isoweek, mday, minute, month, quarter, second, wday, week,
-    yday, year
-
-The following objects are masked from ‘package:dplyr’:
-
-    between, first, last
-
-> 
-> check_file = file.exists("log/R-session-info.log.R")
-> sink("log/R-session-info.log.R", append=check_file)
-> cat(bold("\n\n# -----\n# Session info for import_predictors\n\n")) 
-> session_info()
-> sink()
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> # 1. TREASURIES
-> dt_tbill <- get_fred_series("TB3MS", "rf", observation_start = "1950-01-01", observation_end = "2020-12-31") %>% data.table
-> dt_tbill <- dt_tbill[, .(dateym=year(date)*100+month(date), rf=rf/100)]
-> dt_tbill[]
-     dateym     rf
-  1: 195001 0.0107
-  2: 195002 0.0112
-  3: 195003 0.0112
-  4: 195004 0.0115
-  5: 195005 0.0116
- ---              
-848: 202008 0.0010
-849: 202009 0.0011
-850: 202010 0.0010
-851: 202011 0.0009
-852: 202012 0.0009
-> # fwrite(dt_tbill, "./input/tbill.csv")
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> # 2. CAY
-> dt_cay <- fread("./input/cay_current.csv", skip=0, header=T)
-> setnames(dt_cay, c("date", "c", "w", "y", "cay"))
-> dt_cay <- dt_cay[, .(date_y=year(date), month = month(date), cay) ]
-> dt_cay <- dt_cay[, .(dateym=date_y*100+month, cay) ]
-> dt_cay[]
-     dateym         cay
-  1: 195203  0.01510493
-  2: 195206  0.02483727
-  3: 195209  0.01484008
-  4: 195212  0.02216598
-  5: 195303  0.02152118
- ---                   
-267: 201809 -0.02934508
-268: 201812 -0.02020734
-269: 201903 -0.04435449
-270: 201906 -0.03764155
-271: 201909 -0.03665922
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> # 3. Dividend-Price RATIO
-> dt_msi <- read_sas("./input/msi.sas7bdat") %>% data.table
-> dt_msi <- dt_msi[, .(date=DATE, vwretd, vwretx) ]
-> fwrite(dt_msi, "./output/msi.csv") # SAVED HERE IF YOU NEED IT
-> 
-> # ESTIMATE THE DP RATIO
-> dt_dp <- dt_msi[, .(date, vwretx, vwretd, vwrx=1+vwretx, vwrd=1+vwretd) ]
-> dt_dp[, `:=`(vwrx=1+vwretx, vwrd=1+vwretd) ]
-> dt_dp[, `:=`(dpvw = 100 * (vwretd-vwretx) / (1+vwretx) ) ]
-> dt_dp[, `:=`(retd_retx = (1+vwretd) / (1+vwretx) ) ]
-> dt_dp[, `:=`(datem = as.monthly(date)) ]
-> dt_dp[, dp := 0 ]
-> for (i in seq(11,0)){
-+ 	dt_dp[, dp := (dp*tlag(retd_retx, i, time=datem) + tlag(dpvw, i, time=datem)) ]
-+ }
-> dt_dp <- dt_dp[, .(dateym=year(datem)*100+month(datem), dp=dp/100)]
-> dt_dp[]
-      dateym         dp
-   1: 192512         NA
-   2: 192601         NA
-   3: 192602         NA
-   4: 192603         NA
-   5: 192604         NA
-  ---                  
-1152: 202111 0.01475104
-1153: 202112 0.01473475
-1154: 202201 0.01454479
-1155: 202202 0.01458693
-1156: 202203 0.01466498
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> # ESTIMATE FUTURE EXCESS RETURNS
-> dt_rmrf   <- fread("./output/msi.csv") %>% data.table
-> dt_rmrf   <- dt_rmrf[, .(dateym=year(date)*100+month(date), retm=vwretd) ]
-> dt_rmrf   <- merge(dt_rmrf, dt_tbill, by = "dateym")
-> 
-> dt_rmrf[, lead1_retm := shift(retm, 1, type="lead") ]
-> dt_rmrf[, retm_y := exp( roll_sum(log(1+lead1_retm), n=12, align="left", fill=NA) ) - 1 ]
-> dt_rmrf[, rf_y := (1+rf)^(1/4) * (1+shift(rf, 3, type="lead"))^(1/4) * 
-+                  (1+shift(rf, 6, type="lead"))^(1/4) * (1+shift(rf, 9, type="lead"))^(1/4) - 1 ]
-> dt_rmrf[, rmrf_y3 := 1 * ( 
-+ 	( (1+retm_y) * (1 + shift(retm_y, 12, type="lead")) * (1 + shift(retm_y, 24, type="lead")) )^(1/3) - 
-+ 	( ((1+rf_y)  * (1 + shift(rf_y,   12, type="lead")) * (1 + shift(rf_y, 24, type="lead")) )^(1/3) - 1) -1) ]
-> 
-> dt_rmrf <- dt_rmrf[, .(dateym, rmrf_y3) ]
-> dt_rmrf[ !is.na(rmrf_y3) ]
-     dateym    rmrf_y3
-  1: 195001 0.19164596
-  2: 195002 0.18441326
-  3: 195003 0.17389623
-  4: 195004 0.14728401
-  5: 195005 0.13288460
- ---                  
-812: 201708 0.10990858
-813: 201709 0.08875791
-814: 201710 0.07444771
-815: 201711 0.10732976
-816: 201712 0.12023222
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> # MERGE THE PREDICTORS
-> dt_predict <- merge(dt_dp, dt_tbill, by = c("dateym"))
-> dt_predict <- merge(dt_predict, dt_rmrf, by = c("dateym"), all.x = T)
-> dt_predict <- merge(dt_predict, dt_cay, by = c("dateym"), all.x = T)
-> dt_predict <- dt_predict[ !is.na(rmrf_y3) ]
-> dt_predict <- dt_predict[ !is.na(cay) ]
-> dt_predict[]
-     dateym         dp     rf    rmrf_y3         cay
-  1: 195203 0.05817138 0.0159 0.18092953  0.01510493
-  2: 195206 0.05739649 0.0170 0.21642173  0.02483727
-  3: 195209 0.05709103 0.0171 0.23193277  0.01484008
-  4: 195212 0.05522191 0.0209 0.22202729  0.02216598
-  5: 195303 0.05455042 0.0201 0.26058722  0.02152118
- ---                                                
-260: 201612 0.02284466 0.0051 0.11899389 -0.01902802
-261: 201703 0.02185142 0.0074 0.01294375 -0.02168661
-262: 201706 0.02147595 0.0098 0.07365837 -0.02432134
-263: 201709 0.02132813 0.0103 0.08875791 -0.02799587
-264: 201712 0.02075782 0.0132 0.12023222 -0.02490222
-> 
-> fwrite(dt_predict, "./tmp/predict.csv")
-> ##################################################################################
-> 
-> 
-> 
-> 
-> 
-> 
-> 
-> 
-> 
-> 
-> 
-> 
-> 
-> 
-> 
-> proc.time()
-   user  system elapsed 
-  1.506   0.172   2.113 
diff --git a/log/rp_measure.log.R b/log/rp_measure.log.R
@@ -1,259 +0,0 @@
-
-R version 4.1.3 (2022-03-10) -- "One Push-Up"
-Copyright (C) 2022 The R Foundation for Statistical Computing
-Platform: x86_64-apple-darwin17.0 (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
-  Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-> #!/usr/bin/env Rscript  
-> #
-> # rp_measure.R
-> #
-> # This code runs the predictive regression
-> # We save in output the expected excess return estimate
-> # 
-> #
-> # (c) Valentin Haddad, Erik Loualiche & Matthew Plosser
-> #
-> # Last updated on June 4th 2019
-> # 
-> ##################################################################################
-> 
-> ##################################################################################
-> message("Log file for code executed at\n")
-Log file for code executed at
-
-> message(format(Sys.time(), "%a %b %d %X %Y"))
-Mon Jun 06 12:38:03 2022
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> # APPEND REQUIRED PACKAGES
-> 
-> # See this https://stackoverflow.com/questions/4090169/elegant-way-to-check-for-missing-packages-and-install-them
-> using<-function(...) {
-+     libs<-unlist(list(...))
-+     req<-unlist(lapply(libs,require,character.only=TRUE))
-+     need<-libs[req==FALSE]
-+     if(length(need)>0){ 
-+         install.packages(need)
-+         lapply(need,require,character.only=TRUE)
-+     }
-+ }
-> 
-> package_to_load <- c("crayon", "devtools", "wesanderson", "ggplot2", "statar", 
-+ 	"stringr", "lubridate", "lmtest", "sandwich", "stargazer", "data.table")
-> 
-> using(package_to_load)
-Loading required package: crayon
-Loading required package: devtools
-Loading required package: usethis
-Loading required package: wesanderson
-Loading required package: ggplot2
-
-Attaching package: ‘ggplot2’
-
-The following object is masked from ‘package:crayon’:
-
-    %+%
-
-Loading required package: statar
-Loading required package: stringr
-Loading required package: lubridate
-
-Attaching package: ‘lubridate’
-
-The following objects are masked from ‘package:base’:
-
-    date, intersect, setdiff, union
-
-Loading required package: lmtest
-Loading required package: zoo
-
-Attaching package: ‘zoo’
-
-The following objects are masked from ‘package:base’:
-
-    as.Date, as.Date.numeric
-
-
-Attaching package: ‘lmtest’
-
-The following object is masked from ‘package:crayon’:
-
-    reset
-
-Loading required package: sandwich
-Loading required package: stargazer
-
-Please cite as: 
-
- Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
- R package version 5.2.3. https://CRAN.R-project.org/package=stargazer 
-
-Loading required package: data.table
-
-Attaching package: ‘data.table’
-
-The following objects are masked from ‘package:lubridate’:
-
-    hour, isoweek, mday, minute, month, quarter, second, wday, week,
-    yday, year
-
-> 
-> 
-> check_file = file.exists("log/R-session-info.log.R")
-> sink("log/R-session-info.log.R", append=check_file)
-> cat(bold("\n\n# -----\n# Session info for rp_measure.csv\n\n")) 
-> session_info()
-> sink()
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> dt_predict <- fread("./tmp/predict.csv")
-> dt_predict[, datem := as.monthly(ISOdate(str_sub(dateym, 1, 4), str_sub(dateym, 5, 6), 1)) ]
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> r1_3 = lm(rmrf_y3 ~ dp + cay + rf, data = dt_predict[ year(datem) < 2011])
-> nw_r1_3 = coeftest(r1_3, df = Inf, vcov = NeweyWest(r1_3, lag = 12, prewhite = FALSE) )
-> r2_3 = lm(rmrf_y3 ~ dp + cay + rf, data = dt_predict)
-> nw_r2_3 = coeftest(r2_3, df = Inf, vcov = NeweyWest(r2_3, lag = 12, prewhite = FALSE) )
-> 
-> stargazer(r1_3, nw_r1_3, r2_3, nw_r2_3, type="text")
-
-===========================================================================================
-                                              Dependent variable:                          
-                    -----------------------------------------------------------------------
-                            rmrf_y3                             rmrf_y3                    
-                              OLS           coefficient           OLS           coefficient
-                                               test                                test    
-                              (1)               (2)               (3)               (4)    
--------------------------------------------------------------------------------------------
-dp                         3.632***          3.632***          3.370***          3.370***  
-                            (0.440)           (1.074)           (0.436)           (1.135)  
-                                                                                           
-cay                        2.156***          2.156***          1.814***          1.814***  
-                            (0.261)           (0.604)           (0.246)           (0.630)  
-                                                                                           
-rf                         -0.938***         -0.938**          -1.246***         -1.246*** 
-                            (0.177)           (0.364)           (0.163)           (0.378)  
-                                                                                           
-Constant                    -0.019            -0.019             0.011             0.011   
-                            (0.015)           (0.037)           (0.013)           (0.036)  
-                                                                                           
--------------------------------------------------------------------------------------------
-Observations                  236                                 264                      
-R2                           0.384                               0.344                     
-Adjusted R2                  0.376                               0.336                     
-Residual Std. Error    0.074 (df = 232)                    0.074 (df = 260)                
-F Statistic         48.181*** (df = 3; 232)             45.459*** (df = 3; 260)            
-===========================================================================================
-Note:                                                           *p<0.1; **p<0.05; ***p<0.01
-> 
-> # --- output for the readme
-> star = stargazer(r2_3, type="text",  style = "aer",
-+ 	covariate.labels = c("D/P ratio", "cay", "T-bill (three-month)"),
-+ 	dep.var.labels   = "Future Excess Returns",
-+ 	omit.stat = c("ser", "adj.rsq") )
-
-===========================================================
-                             Future Excess Returns         
------------------------------------------------------------
-D/P ratio                           3.370***               
-                                    (0.436)                
-                                                           
-cay                                 1.814***               
-                                    (0.246)                
-                                                           
-T-bill (three-month)               -1.246***               
-                                    (0.163)                
-                                                           
-Constant                             0.011                 
-                                    (0.013)                
-                                                           
-Observations                          264                  
-R2                                   0.344                 
-F Statistic                 45.459*** (df = 3; 260)        
------------------------------------------------------------
-Notes:               ***Significant at the 1 percent level.
-                     **Significant at the 5 percent level. 
-                     *Significant at the 10 percent level. 
-> 
-> star[1] = "~~~R"
-> star[length(star)+1] = "~~~"
-> cat(star, sep = '\n', file = './tmp/reg_update.txt')
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> # OUTPUT PREDICTED VALUE
-> dt_exp_rmrf <- cbind(dt_predict[!is.na(rmrf_y3), -c("datem")], exp_rmrf = predict(r2_3))
-> 
-> fwrite(dt_exp_rmrf, "./output/predict.csv")
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> # PLOT
-> dt_plot <- dt_exp_rmrf[, .(
-+ 	date=as.Date(ISOdate(str_sub(dateym,1, 4), as.integer(str_sub(dateym, 5, 6)), 1)), 
-+ 	dp, cay, rf, rmrf_y3, exp_rmrf)]
-> dt_plot[]
-           date         dp         cay     rf    rmrf_y3   exp_rmrf
-  1: 1952-03-01 0.05817138  0.01510493 0.0159 0.18092953 0.21495611
-  2: 1952-06-01 0.05739649  0.02483727 0.0170 0.21642173 0.22862789
-  3: 1952-09-01 0.05709103  0.01484008 0.0171 0.23193277 0.20933933
-  4: 1952-12-01 0.05522191  0.02216598 0.0209 0.22202729 0.21159354
-  5: 1953-03-01 0.05455042  0.02152118 0.0201 0.26058722 0.20915790
- ---                                                               
-260: 2016-12-01 0.02284466 -0.01902802 0.0051 0.11899389 0.04744519
-261: 2017-03-01 0.02185142 -0.02168661 0.0074 0.01294375 0.03640904
-262: 2017-06-01 0.02147595 -0.02432134 0.0098 0.07365837 0.02737347
-263: 2017-09-01 0.02132813 -0.02799587 0.0103 0.08875791 0.01958676
-264: 2017-12-01 0.02075782 -0.02490222 0.0132 0.12023222 0.01966250
-> 
-> 
-> p0 <- dt_plot[, .(date, dp, cay, rf, rmrf_y3) ] %>% 
-+     melt(id.vars="date") %>%
-+ 	ggplot(aes(date, value, colour = variable)) + 
-+ 	geom_line(alpha=0.75, size=0.25) + geom_point(shape=1, size = 1, alpha=0.5) + 
-+ 	theme_bw()
-> # p0
-> 
-> p1 <- dt_plot[, .(date, exp_rmrf, rmrf_y3) ] %>% 
-+  	melt(id.vars="date") %>%
-+ 	ggplot(aes(date, 100*value, colour = variable)) + 
-+ 	geom_line(alpha=0.75, size=0.25) + geom_point(shape=1, size = 1, alpha=0.5) + 
-+ 	xlab("") + ylab("Returns (percent)") + 
-+ 	theme_bw() +
-+ 	theme(legend.position = c(0.3, 0.9)) + 
-+ 	scale_colour_manual(name  = "",
-+                         breaks = c("exp_rmrf", "rmrf_y3"),
-+                         values = c(wes_palette("Zissou1")[1], wes_palette("Zissou1")[5]),
-+                         labels=c("Expected", "Realized")) + 
-+ 	guides(colour = guide_legend(nrow = 1))
-> ggsave("./output/predict.png", p1, width = 8, height=6)
-> ##################################################################################
-> 
-> 
-> ##################################################################################
-> 
-> proc.time()
-   user  system elapsed 
-  2.456   0.224   2.887 
diff --git a/output/msi.csv b/output/msi.csv
@@ -1,1157 +1,1190 @@
 date,vwretd,vwretx
 1925-12-31,,
-1926-01-30,0.0005610363,-0.001395089
-1926-02-27,-0.03304553,-0.03658723
-1926-03-31,-0.06400222,-0.07002128
-1926-04-30,0.03702872,0.03404304
-1926-05-28,0.01209523,0.008584509
-1926-06-30,0.05366369,0.04618894
-1926-07-31,0.0314107,0.02922447
-1926-08-31,0.02864728,0.02535763
-1926-09-30,0.00578737,3.00449e-05
-1926-10-30,-0.02899619,-0.03106881
-1926-11-30,0.02855394,0.0229787
-1926-12-31,0.029211,0.0214219
-1927-01-31,0.002416228,5.323665e-05
-1927-02-28,0.0453701,0.04117489
-1927-03-31,0.003756279,-0.002241672
-1927-04-30,0.007225051,0.005134092
-1927-05-31,0.05777311,0.05362266
-1927-06-30,-0.02108671,-0.02740624
-1927-07-30,0.07527909,0.07286946
-1927-08-31,0.02249037,0.01843828
-1927-09-30,0.04992352,0.04500271
-1927-10-31,-0.0407699,-0.0425937
-1927-11-30,0.06845258,0.06329948
-1927-12-31,0.02378212,0.01820621
-1928-01-31,-0.003339723,-0.005101366
-1928-02-29,-0.01447756,-0.01811899
-1928-03-31,0.09047843,0.08558162
-1928-04-30,0.04384581,0.04208157
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diff --git a/output/predict.csv b/output/predict.csv
@@ -1,265 +1,286 @@
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diff --git a/output/predict.pdf b/output/predict.pdf
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diff --git a/output/predict.png b/output/predict.png
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diff --git a/readme.md b/readme.md
@@ -1,50 +1,136 @@
-# Measuring the Market Risk Premium
+<h1 align="center">Measuring the Market Risk Premium</h1>
+<p align="center"><em>Last updated: March 2026</em></p>
 
 ![](output/predict.png)
 
 
 This code updates the measure of equity risk premium.
 
-We use the dividend-price ratio, cay and the three-month T-bill to predict future excess returns
+I use the dividend-price ratio, cay and the three-month T-bill to predict future excess returns
 
 + *Haddad Valentin, Erik Loualiche, and Matthew Plosser*: **Buyout Activity: the Impact of Aggregate Discount Rates**;  Journal of Finance, February 2017, 72:1
 + [Download the paper](http://loualiche.gitlab.io/www/abstract/LBO.html)
 + [Download the data](https://github.com/eloualiche/RiskPremium/releases)
 
 
+## Releases
+
+**For the exact replication of the published paper, use [v1.0](https://github.com/eloualiche/RiskPremium/releases/tag/v1.0).**
+
+That release uses the original R pipeline with Lettau's cay downloaded directly from his [website](http://faculty.haas.berkeley.edu/lettau/data_cay.html) (1952Q1–2019Q3). CRSP data through 2017Q4. Regression coefficients: D/P = 3.370, cay = 1.814, T-bill = −1.246, R² = 0.344 (264 obs).
+
+**For updated estimates, use [v2-202603](https://github.com/eloualiche/RiskPremium/releases/tag/v2-202603).**
+
+This release rewrites the pipeline in Julia and constructs cay from publicly available FRED data, removing the dependency on Lettau's website. CRSP data through 2024Q4. DOLS coefficients: β_a = 0.195, β_y = 0.863 (estimation sample: 1951Q4–2019Q3). Regression coefficients: D/P = 2.779, cay = 0.398, T-bill = −1.232, R² = 0.234 (285 obs). See the [construction of cay](#construction-of-cay) section below for validation details. Future data updates follow the `v2-YYYYMM` convention.
+
+
 ## Data Sources
 
-1. Measuring Dividend Price ratio uses CRSP Monthly Index files available on [WRDS](https://wrds-web.wharton.upenn.edu/wrds/ds/crsp/stock_a/stkmktix.cfm)
-   - `msi.sas7bdat` downloaded from `/wrds/crsp/sasdata/a_stock/msi.sas7bdat`
-   - See the calculations to account for reinvested dividends in this [note](./docs/dividendpriceratio.pdf) on how 
-2. Measuring the risk-free rate from H15 release available on [FRED](https://fred.stlouisfed.org/series/TB3MS
-)
-3. Measuring cay from Lettau's [website](http://faculty.haas.berkeley.edu/lettau/data_cay.html). *Last downloaded on March 23rd 2020*
+1. **Dividend-price ratio** from CRSP Monthly Stock Market Index (`crsp.msi`), available on [WRDS](https://wrds-web.wharton.upenn.edu/wrds/ds/crsp/stock_a/stkmktix.cfm)
+   - See the calculations to account for reinvested dividends in this [note](./docs/dividendpriceratio.pdf)
+2. **Risk-free rate** from the H15 release: 3-month T-bill ([`TB3MS`](https://fred.stlouisfed.org/series/TB3MS)) downloaded from [FRED](https://fred.stlouisfed.org)
+3. **cay** (consumption-wealth ratio) constructed from FRED data following [Lettau and Ludvigson (2001)](https://doi.org/10.1111/0022-1082.00347). See details below.
+
+
+## Construction of cay
+
+**In v1.0**, cay was downloaded directly from Martin Lettau's [website](http://faculty.haas.berkeley.edu/lettau/data_cay.html), last updated 2019Q3. That series is no longer maintained.
+
+**In v2.0**, I construct cay from publicly available FRED data. The cointegrating vector is estimated on the pre-COVID sample (1951Q4–2019Q3), the same endpoint as Lettau's last update. I deliberately exclude the pandemic period from estimation because COVID produced large, transitory swings in transfers (stimulus payments), consumption (lockdowns), and asset values that would distort the long-run cointegrating relationship. The estimated coefficients are then applied out of sample to compute cay through the latest available quarter.[^covid]
+
+[^covid]: Estimating DOLS on the full sample (including 2020–2025) shifts the cointegrating vector substantially: β_a falls from 0.195 to 0.070 and β_y rises from 0.863 to 0.991. The predictive R² is nearly unchanged (0.234 vs 0.231), but the pre-COVID coefficients are closer to Lettau's published values (β_a = 0.218, β_y = 0.801) and more economically interpretable.
+
+### Definition
+
+cay is the residual from a cointegrating regression of log real per-capita consumption on log real per-capita asset wealth and log real per-capita labor income:
+
+```
+c_t = α + β_a · a_t + β_y · y_t + cay_t
+```
+
+The cointegrating vector is estimated by Stock-Watson Dynamic OLS (DOLS) with 8 leads and lags of the first-differenced regressors.
+
+### Data sources (all from FRED)
+
+| Variable | FRED series | Description |
+|---|---|---|
+| Consumption (c) | [`PCEC`](https://fred.stlouisfed.org/series/PCEC) | Personal Consumption Expenditures, quarterly, SAAR |
+| Asset wealth (a) | [`TNWBSHNO`](https://fred.stlouisfed.org/series/TNWBSHNO) | Households and Nonprofits Net Worth (Z.1) |
+| Labor income (y) | *composite* | See construction below |
+| Price deflator | [`PCECTPI`](https://fred.stlouisfed.org/series/PCECTPI) | PCE Chain-Type Price Index (2017=100) |
+| Population | [`B230RC0Q173SBEA`](https://fred.stlouisfed.org/series/B230RC0Q173SBEA) | BEA Midperiod Population |
+
+All nominal series are deflated by PCECTPI and divided by population to obtain real per-capita values.
 
+**Labor income construction** (Lettau and Ludvigson 2001, Appendix):
 
+`y = wages + transfers + other labor income − social insurance − (labor share × taxes)`
 
-## Latest estimates 
+where labor share = (wages + transfers + other labor income − social insurance) / personal income.
+
+| Component | FRED series | Description |
+|---|---|---|
+| Wages and salaries | [`WASCUR`](https://fred.stlouisfed.org/series/WASCUR) | Compensation of Employees: Wages and Salary Accruals |
+| Transfer payments | [`A577RC1Q027SBEA`](https://fred.stlouisfed.org/series/A577RC1Q027SBEA) | Personal Current Transfer Receipts |
+| Other labor income | [`B040RC1Q027SBEA`](https://fred.stlouisfed.org/series/B040RC1Q027SBEA) | Employer Contributions for Employee Pension and Insurance |
+| Social insurance | [`A061RC1Q027SBEA`](https://fred.stlouisfed.org/series/A061RC1Q027SBEA) | Contributions for Government Social Insurance |
+| Personal income | [`PINCOME`](https://fred.stlouisfed.org/series/PINCOME) | Personal Income |
+| Personal taxes | [`W055RC1Q027SBEA`](https://fred.stlouisfed.org/series/W055RC1Q027SBEA) | Personal Current Taxes |
+
+### Validation against Lettau's published series
+
+I compare the constructed cay to Lettau's published series (1952Q1–2019Q3, 271 quarterly observations) on the same estimation sample. Current FRED vintages differ from Lettau's due to the 2023 NIPA comprehensive revision, which rescaled the PCE deflator by approximately 6%.
+
+**Component-level accuracy:**
+
+| | Correlation of levels | Max first-difference error |
+|---|---|---|
+| Consumption (c) | 0.99998 | 0.0046 |
+| Asset wealth (a) | 0.99993 | 0.0083 |
+| Labor income (y) | 0.99988 | 0.0075 |
+
+**DOLS coefficients** (estimation sample: 1951Q4–2019Q3):
+
+| | Lettau | v2.0 |
+|---|---|---|
+| β_a (wealth) | 0.218 | 0.195 |
+| β_y (income) | 0.801 | 0.863 |
+
+**cay series correlation** (demeaned): 0.986
+
+**Impact on predicted risk premium** (264 common observations, 1952Q1–2017Q4):
+
+| | Lettau cay (v1.0) | Constructed cay (v2.0) |
+|---|---|---|
+| D/P coefficient | 3.370 | 3.385 |
+| cay coefficient | 1.814 | 1.564 |
+| T-bill coefficient | −1.246 | −1.336 |
+| R² | 0.344 | 0.332 |
+
+The predicted risk premium from both specifications has correlation 0.997. The mean absolute difference is 0.35 percentage points; 77% of observations differ by less than 0.5pp and 98% by less than 1pp.
+
+
+## Latest estimates
 
 
 ~~~R
 ===========================================================
                              Future Excess Returns         
 -----------------------------------------------------------
-D/P ratio                           3.370***               
-                                    (0.436)                
-                                                           
-cay                                 1.814***               
-                                    (0.246)                
-                                                           
-T-bill (three-month)               -1.246***               
-                                    (0.163)                
-                                                           
-Constant                             0.011                 
-                                    (0.013)                
-                                                           
-Observations                          264                  
-R2                                   0.344                 
-F Statistic                 45.459*** (df = 3; 260)        
+D/P ratio                                      2.779***
+                                                (1.021)
+
+cay                                               0.398
+                                                 (0.36)
+
+T-bill (three-month)                          -1.232***
+                                                (0.306)
+
+Constant                                          0.034
+                                                (0.035)
+
+Observations                                        285
+R2                                                0.234
 -----------------------------------------------------------
 Notes:               ***Significant at the 1 percent level.
                      **Significant at the 5 percent level. 
diff --git a/rules.mk b/rules.mk
@@ -1,46 +0,0 @@
-#
-# RULES for MAKEFILE for the entry and asset pricing Project
-#
-#
-#
-# Created       March 24th  2020
-# Last modified March 24th  2020
-#
-#
-# --------------------------------------------------------------------------------------------------------
-
-
-# --------------------------------------------------------------------------------------------------------
-ifndef RULES_MK
-RULES_MK:=1# Allow repeated "-include".
-
-
-# --------------------------------------------------------------------------------------------------------
-# HOMEMADE SCRIPTS
-define colorecho
-      @tput setaf 6
-      @echo $1
-      @tput sgr0
-endef
-WHITE='\033[1;37m'
-NC   ='\033[0m' # No Color
-
- # Timing: see here https://stackoverflow.com/questions/8483149/gnu-make-timing-a-build-is-it-possible-to-have-a-target-whose-recipe-executes
- TIME_START := $(shell date +%s)
- define TIME-END
- 	@time_end=`date +%s` ; time_exec=`awk -v "TS=${TIME_START}" -v "TE=$$time_end" 'BEGIN{TD=TE-TS;printf "%02dm:%02ds\n", TD/(60),TD%60}'` ; echo -e \\t${WHITE}cumulative time elapsed ... $${time_exec} ... $@ ${NC}
-endef
-# --------------------------------------------------------------------------------------------------------
-
-
-# --------------------------------------------------------------------------------------------------------
-# This will depend on your configuration. Easiest is to symlin latest julia to /usr/bin/julia
-SHELL    := /bin/bash
-# adjust your WRDS username here
-WRDS_USERNAME := XXXX
-R_OPTS   := --vanilla
-# --------------------------------------------------------------------------------------------------------
-
-
-# --------------------------------------------------------------------------------------------------------
-endif
diff --git a/src/CAY.jl b/src/CAY.jl
@@ -0,0 +1,197 @@
+# src/CAY.jl
+module CAY
+
+using DataFrames, Dates, Statistics, LinearAlgebra, CSV
+include("FredUtils.jl")
+using .FredUtils
+
+export compute_cay
+
+# FRED series IDs for CAY components
+const SERIES = Dict(
+    :consumption     => "PCEC",              # Total PCE, Q, Bil $, SAAR
+    :net_worth       => "TNWBSHNO",          # HH Net Worth, Q, Mil $
+    :wages           => "WASCUR",            # Wages & Salary Accruals, Q, Bil $
+    :transfers       => "A577RC1Q027SBEA",   # Personal Current Transfer Receipts, Q
+    :other_labor     => "B040RC1Q027SBEA",   # Employer Pension/Insurance Contrib, Q
+    :social_ins      => "A061RC1Q027SBEA",   # Contributions for Govt Social Ins, Q
+    :personal_income => "PINCOME",           # Personal Income, Q, Bil $
+    :personal_taxes  => "W055RC1Q027SBEA",   # Personal Current Taxes, Q
+    :deflator        => "PCECTPI",           # PCE Price Index, Q, Index
+    :population      => "B230RC0Q173SBEA",   # BEA Midperiod Pop, Q, Thousands
+)
+
+"""
+    download_macro_data() -> DataFrame
+
+Download all macro components from FRED and return a quarterly DataFrame.
+"""
+function download_macro_data()
+    println("Checking FRED series metadata:")
+    for (name, sid) in sort(collect(SERIES), by=first)
+        info = fred_series_info(sid)
+        println("  $name ($sid): $(info.frequency), $(info.units), $(info.observation_start)–$(info.observation_end)")
+    end
+
+    raw = Dict{Symbol, DataFrame}()
+    for (name, sid) in SERIES
+        raw[name] = fred_observations(sid)
+        println("Downloaded $name: $(nrow(raw[name])) obs")
+    end
+
+    # Consumption
+    df = rename(raw[:consumption], :value => :consumption)
+
+    # Net worth: Millions -> Billions
+    nw = copy(raw[:net_worth])
+    nw.value ./= 1000.0
+    rename!(nw, :value => :net_worth)
+    df = innerjoin(df, nw, on=:date)
+
+    # Labor income construction
+    wages = rename(raw[:wages], :value => :wages)
+    trans = rename(raw[:transfers], :value => :transfers)
+    other = rename(raw[:other_labor], :value => :other_labor)
+    si    = rename(raw[:social_ins], :value => :social_ins)
+    pi    = rename(raw[:personal_income], :value => :personal_income)
+    tax   = rename(raw[:personal_taxes], :value => :personal_taxes)
+
+    li = innerjoin(wages, trans, other, si, pi, tax, on=:date)
+    li.li_pretax = li.wages .+ li.transfers .+ li.other_labor .- li.social_ins
+    li.labor_share = li.li_pretax ./ li.personal_income
+    li.labor_income = li.li_pretax .- li.labor_share .* li.personal_taxes
+    df = innerjoin(df, select(li, :date, :labor_income), on=:date)
+
+    # Deflator and population
+    defl = rename(raw[:deflator], :value => :deflator)
+    pop  = rename(raw[:population], :value => :population)
+    df = innerjoin(df, defl, pop, on=:date)
+
+    dropmissing!(df)
+    return df
+end
+
+"""
+    process_components(df::DataFrame) -> DataFrame
+
+Convert nominal aggregates to log real per-capita: c, a, y.
+"""
+function process_components(df::DataFrame)
+    # Convert start-of-quarter dates to end-of-quarter (to match Lettau convention)
+    out = DataFrame(date = Dates.lastdayofmonth.(df.date .+ Dates.Month(2)))
+    # Real per-capita in dollars per person:
+    # consumption/income in billions, population in thousands
+    # => multiply by 1e9 / 1e3 = 1e6 to get dollars per person
+    # deflator is index with base=100, so divide by (deflator/100)
+    out.c = log.(df.consumption  ./ df.deflator .* 100 ./ df.population .* 1e6)
+    out.a = log.(df.net_worth    ./ df.deflator .* 100 ./ df.population .* 1e6)
+    out.y = log.(df.labor_income ./ df.deflator .* 100 ./ df.population .* 1e6)
+    return out
+end
+
+"""
+    estimate_dols(c, a, y; K=8) -> NamedTuple
+
+Stock-Watson Dynamic OLS for cointegrating regression:
+  c_t = α + β_a*a_t + β_y*y_t + Σ_{j=-K}^{K} γ_j*Δa_{t+j} + Σ_{j=-K}^{K} δ_j*Δy_{t+j} + ε_t
+"""
+function estimate_dols(c::Vector{Float64}, a::Vector{Float64}, y::Vector{Float64}; K::Int=8)
+    T = length(c)
+    @assert length(a) == T && length(y) == T
+
+    # Valid range: need Δx[t+j] for j in -K:K, where Δx[t] = x[t]-x[t-1]
+    # Δx exists for t=2:T, so t+j ∈ [2,T] => t ∈ [K+2, T-K]
+    valid = (K+2):(T-K)
+    n = length(valid)
+
+    ncols = 1 + 2 + 2*(2K+1)  # intercept + a,y + leads/lags of Δa,Δy
+    X = zeros(n, ncols)
+    Y = c[valid]
+
+    X[:, 1] .= 1.0
+    X[:, 2] = a[valid]
+    X[:, 3] = y[valid]
+
+    col = 4
+    for j in -K:K
+        X[:, col] = [a[t+j] - a[t+j-1] for t in valid]
+        col += 1
+    end
+    for j in -K:K
+        X[:, col] = [y[t+j] - y[t+j-1] for t in valid]
+        col += 1
+    end
+
+    β = X \ Y
+    α   = β[1]
+    β_a = β[2]
+    β_y = β[3]
+
+    # Compute cay for full sample
+    cay = c .- α .- β_a .* a .- β_y .* y
+
+    println("DOLS coefficients: α=$(round(α, digits=4)), β_a=$(round(β_a, digits=4)), β_y=$(round(β_y, digits=4))")
+    println("  (Lettau reference: α≈-0.441, β_a≈0.218, β_y≈0.801)")
+
+    return (; α, β_a, β_y, cay, valid)
+end
+
+"""
+    compute_cay(; K=8, estimation_end=nothing) -> NamedTuple
+
+Full pipeline: download data, process, estimate DOLS.
+If `estimation_end` is provided, DOLS is estimated only on data up to that date,
+but cay is computed for the full sample using the estimated coefficients.
+Returns (; data::DataFrame, beta_a, beta_y, alpha) where data has columns
+date, c, a, y, cay.
+"""
+function compute_cay(; K::Int=8,
+        estimation_start::Union{Date,Nothing}=nothing,
+        estimation_end::Union{Date,Nothing}=nothing)
+    raw = download_macro_data()
+    df = process_components(raw)
+    dropmissing!(df)
+
+    # Restrict estimation sample if requested
+    est_mask = trues(nrow(df))
+    if estimation_start !== nothing
+        est_mask .&= df.date .>= estimation_start
+    end
+    if estimation_end !== nothing
+        est_mask .&= df.date .<= estimation_end
+    end
+
+    if any(.!est_mask)
+        est_idx = findall(est_mask)
+        println("DOLS estimation sample: $(df.date[est_idx[1]]) to $(df.date[est_idx[end]]) ($(length(est_idx)) obs)")
+        result = estimate_dols(df.c[est_idx], df.a[est_idx], df.y[est_idx]; K)
+        # Recompute cay for full sample using estimated coefficients
+        cay_full = df.c .- result.α .- result.β_a .* df.a .- result.β_y .* df.y
+    else
+        result = estimate_dols(df.c, df.a, df.y; K)
+        cay_full = result.cay
+    end
+
+    data = DataFrame(
+        date = df.date,
+        c    = df.c,
+        a    = df.a,
+        y    = df.y,
+        cay  = cay_full,
+    )
+    return (; data, beta_a=result.β_a, beta_y=result.β_y, alpha=result.α)
+end
+
+# When run as script
+if abspath(PROGRAM_FILE) == @__FILE__
+    # Estimate DOLS on pre-COVID sample (1951Q4–2019Q3) to avoid distortion from
+    # the pandemic period (large swings in transfers, consumption, and asset values).
+    # The estimated coefficients are then applied to the full sample to extend cay.
+    res = compute_cay(estimation_start=Date(1951,10,1), estimation_end=Date(2019,9,30))
+    # Write with column names matching Lettau's format: date,c,w,y,cay
+    out = rename(res.data, :a => :w)
+    CSV.write("input/cay_computed.csv", out)
+    println("Wrote input/cay_computed.csv: $(nrow(out)) rows, $(out.date[1]) to $(out.date[end])")
+end
+
+end # module
diff --git a/src/DataImport.jl b/src/DataImport.jl
@@ -0,0 +1,142 @@
+# src/DataImport.jl
+module DataImport
+
+using CSV, DataFrames, Dates, Statistics
+include("FredUtils.jl")
+using .FredUtils
+
+export compute_dp, compute_tbill, compute_excess_returns, build_predictors
+
+"""
+    compute_dp(msi_path) -> DataFrame
+
+Compute 12-month rolling dividend-price ratio from CRSP MSI data.
+Replicates R logic: accumulate monthly dividends with reinvestment over 12 months.
+
+Returns DataFrame with columns: dateym, dp.
+"""
+function compute_dp(msi_path::String)
+    msi = CSV.read(msi_path, DataFrame)
+    dropmissing!(msi, [:vwretd, :vwretx])
+
+    n = nrow(msi)
+    dpvw = 100.0 .* (msi.vwretd .- msi.vwretx) ./ (1.0 .+ msi.vwretx)
+    retd_retx = (1.0 .+ msi.vwretd) ./ (1.0 .+ msi.vwretx)
+
+    dp = zeros(n)
+    for i in 11:-1:0
+        for t in (i+1):n
+            dp[t] = dp[t] * retd_retx[t-i] + dpvw[t-i]
+        end
+    end
+    dp ./= 100.0
+
+    dates = Date.(msi.date)
+    dateym = year.(dates) .* 100 .+ month.(dates)
+
+    return DataFrame(dateym = dateym, dp = dp)
+end
+
+"""
+    compute_tbill(; observation_end) -> DataFrame
+
+Download 3-month T-bill from FRED (TB3MS). Returns DataFrame with dateym, rf.
+"""
+function compute_tbill(; observation_end::Date = Date(2026,12,31))
+    df = fred_observations("TB3MS"; end_date=observation_end)
+    dropmissing!(df)
+    dateym = year.(df.date) .* 100 .+ month.(df.date)
+    rf = df.value ./ 100.0
+    return DataFrame(dateym = dateym, rf = rf)
+end
+
+"""
+    compute_excess_returns(msi_path, tbill_df) -> DataFrame
+
+Compute 3-year geometric average future excess returns.
+Replicates R logic from import_predictors.R lines 100-114.
+
+The R code does:
+1. lead1_retm = shift(retm, 1, type="lead")  -- next month's return
+2. retm_y = exp(roll_sum(log(1+lead1_retm), n=12, align="left")) - 1  -- 12-month forward return
+3. rf_y = geometric average of quarterly T-bill rates (current + 3,6,9 month leads)
+4. rmrf_y3 = 3-year geometric average excess return
+"""
+function compute_excess_returns(msi_path::String, tbill_df::DataFrame)
+    msi = CSV.read(msi_path, DataFrame)
+    dropmissing!(msi, [:vwretd])
+    dates = Date.(msi.date)
+    dateym = year.(dates) .* 100 .+ month.(dates)
+
+    df = DataFrame(dateym = dateym, retm = msi.vwretd)
+    df = innerjoin(df, tbill_df, on=:dateym)
+
+    n = nrow(df)
+    retm = df.retm
+    rf = df.rf
+
+    # 12-month forward market return: lead by 1, then rolling 12-month sum of log returns
+    retm_y = Vector{Union{Float64,Missing}}(missing, n)
+    for t in 1:(n-12)
+        retm_y[t] = exp(sum(log.(1.0 .+ retm[(t+1):(t+12)]))) - 1.0
+    end
+
+    # Annual risk-free: geometric average of quarterly T-bill
+    rf_y = Vector{Union{Float64,Missing}}(missing, n)
+    for t in 1:(n-9)
+        rf_y[t] = (1+rf[t])^0.25 * (1+rf[t+3])^0.25 * (1+rf[t+6])^0.25 * (1+rf[t+9])^0.25 - 1.0
+    end
+
+    # 3-year geometric average excess return
+    rmrf_y3 = Vector{Union{Float64,Missing}}(missing, n)
+    for t in 1:(n-24)
+        if !ismissing(retm_y[t]) && !ismissing(retm_y[t+12]) && !ismissing(retm_y[t+24]) &&
+           !ismissing(rf_y[t])   && !ismissing(rf_y[t+12])   && !ismissing(rf_y[t+24])
+            rmrf_y3[t] = (
+                ((1+retm_y[t]) * (1+retm_y[t+12]) * (1+retm_y[t+24]))^(1/3) -
+                (((1+rf_y[t])  * (1+rf_y[t+12])   * (1+rf_y[t+24]))^(1/3) - 1) - 1
+            )
+        end
+    end
+
+    return DataFrame(dateym = df.dateym, rmrf_y3 = rmrf_y3)
+end
+
+"""
+    build_predictors(msi_path, cay_path) -> DataFrame
+
+Full pipeline: compute D/P, download T-bill, compute excess returns,
+load CAY, merge all. Returns DataFrame with: dateym, dp, rf, rmrf_y3, cay.
+"""
+function build_predictors(msi_path::String, cay_path::String)
+    dp_df    = compute_dp(msi_path)
+    tbill_df = compute_tbill()
+    rmrf_df  = compute_excess_returns(msi_path, tbill_df)
+
+    # Load CAY (either Lettau's or our computed version)
+    cay_raw = CSV.read(cay_path, DataFrame; header=1)
+    rename!(cay_raw, Symbol.(["date", "c", "w", "y", "cay"]))
+    cay_raw.date = Date.(cay_raw.date)
+    cay_df = DataFrame(
+        dateym = year.(cay_raw.date) .* 100 .+ month.(cay_raw.date),
+        cay = cay_raw.cay,
+    )
+
+    # Merge
+    predict = innerjoin(dp_df, tbill_df, on=:dateym)
+    predict = innerjoin(predict, rmrf_df, on=:dateym)
+    predict = innerjoin(predict, cay_df, on=:dateym)
+    dropmissing!(predict)
+
+    return predict
+end
+
+# When run as script
+if abspath(PROGRAM_FILE) == @__FILE__
+    mkpath("tmp")
+    predict = build_predictors("output/msi.csv", "input/cay_computed.csv")
+    CSV.write("tmp/predict.csv", predict)
+    println("Wrote tmp/predict.csv: $(nrow(predict)) rows")
+end
+
+end # module
diff --git a/src/FredUtils.jl b/src/FredUtils.jl
@@ -0,0 +1,78 @@
+# src/FredUtils.jl
+module FredUtils
+
+using HTTP, JSON, DataFrames, Dates
+
+export fred_series_info, fred_observations
+
+const FRED_BASE = "https://api.stlouisfed.org/fred"
+
+function _api_key()
+    key = get(ENV, "FRED_API_KEY", "")
+    if isempty(key)
+        envfile = joinpath(@__DIR__, "..", ".env")
+        if isfile(envfile)
+            for line in eachline(envfile)
+                m = match(r"^FRED_API_KEY\s*=\s*(.+)$", line)
+                if m !== nothing
+                    key = strip(m.captures[1])
+                    break
+                end
+            end
+        end
+    end
+    isempty(key) && error("Set FRED_API_KEY environment variable or add it to .env")
+    return key
+end
+
+"""
+    fred_series_info(series_id) -> NamedTuple
+
+Return metadata for a FRED series: title, frequency, units, seasonal_adjustment,
+observation_start, observation_end.
+"""
+function fred_series_info(series_id::AbstractString)
+    url = "$FRED_BASE/series?series_id=$series_id&api_key=$(_api_key())&file_type=json"
+    resp = HTTP.get(url)
+    data = JSON.parse(String(resp.body))
+    s = data["seriess"][1]
+    return (
+        id              = s["id"],
+        title           = s["title"],
+        frequency       = s["frequency"],
+        units           = s["units"],
+        seasonal_adjustment = s["seasonal_adjustment"],
+        observation_start = Date(s["observation_start"]),
+        observation_end   = Date(s["observation_end"]),
+    )
+end
+
+"""
+    fred_observations(series_id; start_date, end_date) -> DataFrame
+
+Download observations for a FRED series. Returns DataFrame with columns `date::Date`
+and `value::Union{Float64,Missing}`.
+"""
+function fred_observations(series_id::AbstractString;
+        start_date::Date = Date(1947,1,1),
+        end_date::Date   = Date(2026,12,31))
+    url = string(FRED_BASE, "/series/observations?series_id=", series_id,
+        "&api_key=", _api_key(),
+        "&file_type=json",
+        "&observation_start=", start_date,
+        "&observation_end=", end_date)
+    resp = HTTP.get(url)
+    data = JSON.parse(String(resp.body))
+    obs_list = data["observations"]
+    n = length(obs_list)
+    dates  = Vector{Date}(undef, n)
+    values = Vector{Union{Float64,Missing}}(undef, n)
+    for (i, obs) in enumerate(obs_list)
+        dates[i] = Date(obs["date"])
+        v = obs["value"]
+        values[i] = v == "." ? missing : parse(Float64, v)
+    end
+    return DataFrame(date = dates, value = values)
+end
+
+end # module
diff --git a/src/RiskPremium.jl b/src/RiskPremium.jl
@@ -0,0 +1,179 @@
+module RiskPremium
+
+using CSV, DataFrames, Dates, Statistics, LinearAlgebra, SpecialFunctions
+using Plots
+pgfplotsx()
+
+export run_regression, format_table, make_plot
+
+"""
+    newey_west_vcov(X, residuals; lag=12)
+
+Newey-West HAC variance-covariance matrix with Bartlett kernel.
+Matches R's sandwich::NeweyWest(model, lag=12, prewhite=FALSE).
+"""
+function newey_west_vcov(X::Matrix{Float64}, e::Vector{Float64}; lag::Int=12)
+    n, k = size(X)
+    XX_inv = inv(X'X)
+
+    # Meat: S = Σ w(l) * [Γ(l) + Γ(l)'] with Bartlett weights
+    S = zeros(k, k)
+    for l in 0:lag
+        w = 1.0 - l / (lag + 1)  # Bartlett kernel
+        Γ = zeros(k, k)
+        for t in (l+1):n
+            Γ .+= e[t] * e[t-l] .* (X[t,:] * X[t-l,:]')
+        end
+        if l == 0
+            S .+= w .* Γ
+        else
+            S .+= w .* (Γ .+ Γ')
+        end
+    end
+
+    V = XX_inv * S * XX_inv
+    return V
+end
+
+"""
+    run_regression(predict; sample_filter=nothing) -> NamedTuple
+
+Run OLS: rmrf_y3 ~ dp + cay + rf with Newey-West(12) standard errors.
+Returns (coefs, se, r2, nobs, residuals, fitted).
+"""
+function run_regression(predict::DataFrame; sample_filter=nothing)
+    df = isnothing(sample_filter) ? copy(predict) : filter(sample_filter, predict)
+
+    y = Float64.(df.rmrf_y3)
+    n = length(y)
+    X = hcat(ones(n), Float64.(df.dp), Float64.(df.cay), Float64.(df.rf))
+    varnames = [:constant, :dp, :cay, :rf]
+
+    β = X \ y
+    ŷ = X * β
+    e = y .- ŷ
+
+    ss_res = sum(e.^2)
+    ss_tot = sum((y .- mean(y)).^2)
+    r2 = 1.0 - ss_res / ss_tot
+
+    V = newey_west_vcov(X, e; lag=12)
+    se = sqrt.(diag(V))
+
+    coefs = Dict(varnames .=> β)
+    ses   = Dict(varnames .=> se)
+
+    return (; coefs, se=ses, r2, nobs=n, residuals=e, fitted=ŷ, β, varnames)
+end
+
+"""
+    format_table(result) -> String
+
+Format regression output as text table (replaces stargazer).
+"""
+function format_table(result)
+    lines = String[]
+    push!(lines, "~~~R")
+    push!(lines, "===========================================================")
+    push!(lines, "                             Future Excess Returns         ")
+    push!(lines, "-----------------------------------------------------------")
+
+    labels = Dict(:dp => "D/P ratio", :cay => "cay", :rf => "T-bill (three-month)", :constant => "Constant")
+
+    for var in [:dp, :cay, :rf, :constant]
+        b = result.coefs[var]
+        s = result.se[var]
+        z = abs(b / s)
+        p = 2.0 * (1.0 - _Φ(z))
+        st = p < 0.01 ? "***" : p < 0.05 ? "**" : p < 0.10 ? "*" : ""
+        push!(lines, "$(rpad(labels[var], 35))$(lpad(string(round(b, digits=3)) * st, 20))")
+        push!(lines, "$(rpad("", 35))$(lpad("(" * string(round(s, digits=3)) * ")", 20))")
+        push!(lines, "")
+    end
+
+    push!(lines, "$(rpad("Observations", 35))$(lpad(string(result.nobs), 20))")
+    push!(lines, "$(rpad("R2", 35))$(lpad(string(round(result.r2, digits=3)), 20))")
+    push!(lines, "-----------------------------------------------------------")
+    push!(lines, "Notes:               ***Significant at the 1 percent level.")
+    push!(lines, "                     **Significant at the 5 percent level. ")
+    push!(lines, "                     *Significant at the 10 percent level. ")
+    push!(lines, "~~~")
+    return join(lines, "\n")
+end
+
+# Standard normal CDF
+function _Φ(x)
+    return 0.5 * erfc(-x / sqrt(2.0))
+end
+
+"""
+    make_plot(predict)
+
+Plot expected vs realized excess returns. Uses Plots.jl with PGFPlotsX backend.
+"""
+function make_plot(predict::DataFrame)
+    dates = Date.(
+        div.(predict.dateym, 100),
+        mod.(predict.dateym, 100),
+        1
+    )
+
+    # Major ticks every 10 years, minor every 5
+    yr_min = year(dates[1])
+    yr_max = year(dates[end])
+    major_years = (yr_min ÷ 10 * 10):10:(yr_max ÷ 10 * 10 + 10)
+    minor_years = (yr_min ÷ 5 * 5):5:(yr_max ÷ 5 * 5 + 5)
+    xtick_major = Date.(major_years, 1, 1)
+    xtick_minor = Date.(minor_years, 1, 1)
+
+    p = plot(dates, 100 .* predict.rmrf_y3;
+        label  = "Realized",
+        color  = :steelblue,
+        alpha  = 0.75,
+        lw     = 0.5,
+        marker = (:circle, 2, 0.5, :steelblue),
+        xlabel = "",
+        ylabel = "Returns (percent)",
+        legend = :bottomleft,
+        framestyle = :box,
+        xticks = (xtick_major, [string(year(d)) * "-M" * string(month(d)) for d in xtick_major]),
+        minorticks = 2,
+    )
+    plot!(p, dates, 100 .* predict.exp_rmrf;
+        label  = "Expected",
+        color  = :indianred,
+        alpha  = 0.75,
+        lw     = 0.5,
+        marker = (:circle, 2, 0.5, :indianred),
+    )
+
+    savefig(p, "output/predict.pdf")
+    savefig(p, "output/predict.png")
+    println("Wrote output/predict.pdf and output/predict.png")
+end
+
+# When run as script
+if abspath(PROGRAM_FILE) == @__FILE__
+    predict = CSV.read("tmp/predict.csv", DataFrame)
+    predict.year = div.(predict.dateym, 100)
+
+    r1 = run_regression(predict; sample_filter = r -> r.year < 2011)
+    println("In-sample (year < 2011):")
+    println("  β_dp=$(round(r1.coefs[:dp], digits=3)), β_cay=$(round(r1.coefs[:cay], digits=3)), β_rf=$(round(r1.coefs[:rf], digits=3))")
+
+    r2 = run_regression(predict)
+    println("\nFull sample:")
+    println("  β_dp=$(round(r2.coefs[:dp], digits=3)), β_cay=$(round(r2.coefs[:cay], digits=3)), β_rf=$(round(r2.coefs[:rf], digits=3))")
+
+    predict.exp_rmrf = r2.fitted
+    CSV.write("output/predict.csv", select(predict, :dateym, :dp, :rf, :rmrf_y3, :cay, :exp_rmrf))
+
+    table = format_table(r2)
+    mkpath("tmp")
+    write("tmp/reg_update.txt", table)
+    println("\nWrote output/predict.csv and tmp/reg_update.txt")
+
+    make_plot(predict)
+end
+
+end # module
diff --git a/src/readme_in.md b/src/readme_in.md
@@ -1,28 +0,0 @@
-# Measuring the Market Risk Premium
-
-![](output/predict.png)
-
-
-This code updates the measure of equity risk premium.
-
-We use the dividend-price ratio, cay and the three-month T-bill to predict future excess returns
-
-+ *Haddad Valentin, Erik Loualiche, and Matthew Plosser*: **Buyout Activity: the Impact of Aggregate Discount Rates**;  Journal of Finance, February 2017, 72:1
-+ [Download the paper](http://loualiche.gitlab.io/www/abstract/LBO.html)
-+ [Download the data](https://github.com/eloualiche/RiskPremium/releases)
-
-
-## Data Sources
-
-1. Measuring Dividend Price ratio uses CRSP Monthly Index files available on [WRDS](https://wrds-web.wharton.upenn.edu/wrds/ds/crsp/stock_a/stkmktix.cfm)
-   - `msi.sas7bdat` downloaded from `/wrds/crsp/sasdata/a_stock/msi.sas7bdat`
-   - See the calculations to account for reinvested dividends in this [note](./docs/dividendpriceratio.pdf) on how 
-2. Measuring the risk-free rate from H15 release available on [FRED](https://fred.stlouisfed.org/series/TB3MS
-)
-3. Measuring cay from Lettau's [website](http://faculty.haas.berkeley.edu/lettau/data_cay.html). *Last downloaded on March 23rd 2020*
-
-
-
-## Latest estimates 
-
-
diff --git a/src/readme_input.txt b/src/readme_input.txt
@@ -0,0 +1,111 @@
+<h1 align="center">Measuring the Market Risk Premium</h1>
+<p align="center"><em>Last updated: March 2026</em></p>
+
+![](output/predict.png)
+
+
+This code updates the measure of equity risk premium.
+
+I use the dividend-price ratio, cay and the three-month T-bill to predict future excess returns
+
++ *Haddad Valentin, Erik Loualiche, and Matthew Plosser*: **Buyout Activity: the Impact of Aggregate Discount Rates**;  Journal of Finance, February 2017, 72:1
++ [Download the paper](http://loualiche.gitlab.io/www/abstract/LBO.html)
++ [Download the data](https://github.com/eloualiche/RiskPremium/releases)
+
+
+## Releases
+
+**For the exact replication of the published paper, use [v1.0](https://github.com/eloualiche/RiskPremium/releases/tag/v1.0).** That release uses the original R pipeline with Lettau's cay downloaded directly from his [website](http://faculty.haas.berkeley.edu/lettau/data_cay.html) (1952Q1–2019Q3). CRSP data through 2017Q4. Regression coefficients: D/P = 3.370, cay = 1.814, T-bill = −1.246, R² = 0.344 (264 obs).
+
+**For updated estimates, use [v2-202603](https://github.com/eloualiche/RiskPremium/releases/tag/v2-202603).** This release rewrites the pipeline in Julia and constructs cay from publicly available FRED data, removing the dependency on Lettau's website. CRSP data through 2024Q4. DOLS coefficients: β_a = 0.195, β_y = 0.863 (estimation sample: 1951Q4–2019Q3). Regression coefficients: D/P = 2.779, cay = 0.398, T-bill = −1.232, R² = 0.234 (285 obs). See the [construction of cay](#construction-of-cay) section below for validation details. Future data updates follow the `v2-YYYYMM` convention.
+
+
+## Data Sources
+
+1. **Dividend-price ratio** from CRSP Monthly Stock Market Index (`crsp.msi`), available on [WRDS](https://wrds-web.wharton.upenn.edu/wrds/ds/crsp/stock_a/stkmktix.cfm)
+   - See the calculations to account for reinvested dividends in this [note](./docs/dividendpriceratio.pdf)
+2. **Risk-free rate** from the H15 release: 3-month T-bill ([`TB3MS`](https://fred.stlouisfed.org/series/TB3MS)) downloaded from [FRED](https://fred.stlouisfed.org)
+3. **cay** (consumption-wealth ratio) constructed from FRED data following [Lettau and Ludvigson (2001)](https://doi.org/10.1111/0022-1082.00347). See details below.
+
+
+## Construction of cay
+
+**In v1.0**, cay was downloaded directly from Martin Lettau's [website](http://faculty.haas.berkeley.edu/lettau/data_cay.html), last updated 2019Q3. That series is no longer maintained.
+
+**In v2.0**, I construct cay from publicly available FRED data. The cointegrating vector is estimated on the pre-COVID sample (1951Q4–2019Q3), the same endpoint as Lettau's last update. I deliberately exclude the pandemic period from estimation because COVID produced large, transitory swings in transfers (stimulus payments), consumption (lockdowns), and asset values that would distort the long-run cointegrating relationship. The estimated coefficients are then applied out of sample to compute cay through the latest available quarter.[^covid]
+
+[^covid]: Estimating DOLS on the full sample (including 2020–2025) shifts the cointegrating vector substantially: β_a falls from 0.195 to 0.070 and β_y rises from 0.863 to 0.991. The predictive R² is nearly unchanged (0.234 vs 0.231), but the pre-COVID coefficients are closer to Lettau's published values (β_a = 0.218, β_y = 0.801) and more economically interpretable.
+
+### Definition
+
+cay is the residual from a cointegrating regression of log real per-capita consumption on log real per-capita asset wealth and log real per-capita labor income:
+
+```
+c_t = α + β_a · a_t + β_y · y_t + cay_t
+```
+
+The cointegrating vector is estimated by Stock-Watson Dynamic OLS (DOLS) with 8 leads and lags of the first-differenced regressors.
+
+### Data sources (all from FRED)
+
+| Variable | FRED series | Description |
+|---|---|---|
+| Consumption (c) | [`PCEC`](https://fred.stlouisfed.org/series/PCEC) | Personal Consumption Expenditures, quarterly, SAAR |
+| Asset wealth (a) | [`TNWBSHNO`](https://fred.stlouisfed.org/series/TNWBSHNO) | Households and Nonprofits Net Worth (Z.1) |
+| Labor income (y) | *composite* | See construction below |
+| Price deflator | [`PCECTPI`](https://fred.stlouisfed.org/series/PCECTPI) | PCE Chain-Type Price Index (2017=100) |
+| Population | [`B230RC0Q173SBEA`](https://fred.stlouisfed.org/series/B230RC0Q173SBEA) | BEA Midperiod Population |
+
+All nominal series are deflated by PCECTPI and divided by population to obtain real per-capita values.
+
+**Labor income construction** (Lettau and Ludvigson 2001, Appendix):
+
+`y = wages + transfers + other labor income − social insurance − (labor share × taxes)`
+
+where labor share = (wages + transfers + other labor income − social insurance) / personal income.
+
+| Component | FRED series | Description |
+|---|---|---|
+| Wages and salaries | [`WASCUR`](https://fred.stlouisfed.org/series/WASCUR) | Compensation of Employees: Wages and Salary Accruals |
+| Transfer payments | [`A577RC1Q027SBEA`](https://fred.stlouisfed.org/series/A577RC1Q027SBEA) | Personal Current Transfer Receipts |
+| Other labor income | [`B040RC1Q027SBEA`](https://fred.stlouisfed.org/series/B040RC1Q027SBEA) | Employer Contributions for Employee Pension and Insurance |
+| Social insurance | [`A061RC1Q027SBEA`](https://fred.stlouisfed.org/series/A061RC1Q027SBEA) | Contributions for Government Social Insurance |
+| Personal income | [`PINCOME`](https://fred.stlouisfed.org/series/PINCOME) | Personal Income |
+| Personal taxes | [`W055RC1Q027SBEA`](https://fred.stlouisfed.org/series/W055RC1Q027SBEA) | Personal Current Taxes |
+
+### Validation against Lettau's published series
+
+I compare the constructed cay to Lettau's published series (1952Q1–2019Q3, 271 quarterly observations) on the same estimation sample. Current FRED vintages differ from Lettau's due to the 2023 NIPA comprehensive revision, which rescaled the PCE deflator by approximately 6%.
+
+**Component-level accuracy:**
+
+| | Correlation of levels | Max first-difference error |
+|---|---|---|
+| Consumption (c) | 0.99998 | 0.0046 |
+| Asset wealth (a) | 0.99993 | 0.0083 |
+| Labor income (y) | 0.99988 | 0.0075 |
+
+**DOLS coefficients** (estimation sample: 1951Q4–2019Q3):
+
+| | Lettau | v2.0 |
+|---|---|---|
+| β_a (wealth) | 0.218 | 0.195 |
+| β_y (income) | 0.801 | 0.863 |
+
+**cay series correlation** (demeaned): 0.986
+
+**Impact on predicted risk premium** (264 common observations, 1952Q1–2017Q4):
+
+| | Lettau cay (v1.0) | Constructed cay (v2.0) |
+|---|---|---|
+| D/P coefficient | 3.370 | 3.385 |
+| cay coefficient | 1.814 | 1.564 |
+| T-bill coefficient | −1.246 | −1.336 |
+| R² | 0.344 | 0.332 |
+
+The predicted risk premium from both specifications has correlation 0.997. The mean absolute difference is 0.35 percentage points; 77% of observations differ by less than 0.5pp and 98% by less than 1pp.
+
+
+## Latest estimates
+
+
diff --git a/test/test_cay.jl b/test/test_cay.jl
@@ -0,0 +1,100 @@
+# test/test_cay.jl  — Validate CAY module against Lettau & Ludvigson's published data
+#
+# Run:  julia --project=. test/test_cay.jl
+
+using Test, CSV, DataFrames, Dates, Statistics
+
+# ---------- Load the CAY module ------------------------------------------
+include(joinpath(@__DIR__, "..", "src", "CAY.jl"))
+using .CAY
+
+# ---------- Load Lettau's published data ---------------------------------
+lettau = CSV.read(
+    joinpath(@__DIR__, "..", "input", "cay_current.csv"),
+    DataFrame;
+    header = 1,
+    normalizenames = true,
+)
+
+# Rename columns to standard names
+rename!(lettau, names(lettau) .=> ["date", "c", "w", "y", "cay"])
+
+# Parse dates
+lettau.date = Date.(lettau.date)
+
+# ---------- Download & process our data ----------------------------------
+println("Downloading FRED series and computing CAY...")
+# Estimate DOLS on Lettau's sample period (1951Q4-2019Q3) for replication
+result = CAY.compute_cay(estimation_start=Date(1951,10,1), estimation_end=Date(2019,9,30))
+cay_df = result.data
+
+# ---------- Align to Lettau's date range ---------------------------------
+# Our result uses end-of-quarter dates; Lettau's also uses end-of-quarter.
+# Merge on date
+merged = innerjoin(
+    select(cay_df, :date, :c => :c_ours, :a => :a_ours, :y => :y_ours, :cay => :cay_ours),
+    select(lettau, :date, :c => :c_lettau, :w => :w_lettau, :y => :y_lettau, :cay => :cay_lettau),
+    on = :date,
+)
+
+println("Merged $(nrow(merged)) quarters (Lettau has $(nrow(lettau)) quarters)")
+
+@testset "CAY Validation" begin
+
+    # ---- 1) Correlation of log-levels > 0.999 ----------------------------
+    @testset "Level correlations" begin
+        corr_c = cor(merged.c_ours, merged.c_lettau)
+        corr_a = cor(merged.a_ours, merged.w_lettau)
+        corr_y = cor(merged.y_ours, merged.y_lettau)
+        println("  cor(c): $corr_c")
+        println("  cor(a): $corr_a")
+        println("  cor(y): $corr_y")
+        @test corr_c > 0.999
+        @test corr_a > 0.999
+        @test corr_y > 0.999
+    end
+
+    # ---- 2) First-difference max errors ----------------------------------
+    @testset "First-difference accuracy" begin
+        dc_ours   = diff(merged.c_ours)
+        dc_lettau = diff(merged.c_lettau)
+        da_ours   = diff(merged.a_ours)
+        da_lettau = diff(merged.w_lettau)
+        dy_ours   = diff(merged.y_ours)
+        dy_lettau = diff(merged.y_lettau)
+
+        max_dc = maximum(abs.(dc_ours .- dc_lettau))
+        max_da = maximum(abs.(da_ours .- da_lettau))
+        max_dy = maximum(abs.(dy_ours .- dy_lettau))
+        println("  max |Δc - Δc_L|: $max_dc")
+        println("  max |Δa - Δa_L|: $max_da")
+        println("  max |Δy - Δy_L|: $max_dy")
+        @test max_dc < 0.01
+        @test max_da < 0.01
+        @test max_dy < 0.01
+    end
+
+    # ---- 3) DOLS coefficients -------------------------------------------
+    @testset "DOLS coefficients" begin
+        beta_a = result.beta_a
+        beta_y = result.beta_y
+        println("  β_a = $beta_a  (Lettau: ≈0.218)")
+        println("  β_y = $beta_y  (Lettau: ≈0.801)")
+        @test abs(beta_a - 0.218) < 0.05
+        @test abs(beta_y - 0.801) < 0.10
+    end
+
+    # ---- 4) CAY correlation > 0.99 --------------------------------------
+    @testset "CAY correlation" begin
+        # Demean both series
+        cay_ours   = merged.cay_ours .- mean(merged.cay_ours)
+        cay_lettau = merged.cay_lettau .- mean(merged.cay_lettau)
+        corr_cay = cor(cay_ours, cay_lettau)
+        println("  cor(cay_demeaned): $corr_cay")
+        # Threshold 0.98: cross-vintage replication with 2023 NIPA comprehensive revision
+        # shifts deflator ~6% and modifies component series definitions
+        @test corr_cay > 0.98
+    end
+end
+
+println("\nAll CAY tests passed!")
diff --git a/test/test_dataimport.jl b/test/test_dataimport.jl
@@ -0,0 +1,63 @@
+# test/test_dataimport.jl
+using Test, CSV, DataFrames, Dates, Statistics
+
+# Load R reference
+ref = CSV.read("output/predict.csv", DataFrame)
+println("Reference predict.csv: $(nrow(ref)) obs, dateym $(ref.dateym[1]) to $(ref.dateym[end])")
+
+include(joinpath(@__DIR__, "..", "src", "DataImport.jl"))
+using .DataImport
+
+# --- Test D/P ratio ---
+dp_df = DataImport.compute_dp("output/msi.csv")
+# Merge on dateym
+comp = innerjoin(
+    select(ref, :dateym, :dp => :dp_ref),
+    dp_df,
+    on=:dateym
+)
+maxerr = maximum(abs.(comp.dp .- comp.dp_ref))
+println("D/P ratio max absolute error: $maxerr")
+@test maxerr < 1e-10
+println("D/P ratio matches R output")
+
+# --- Test T-bill ---
+tbill_df = DataImport.compute_tbill()
+comp_rf = innerjoin(
+    select(ref, :dateym, :rf => :rf_ref),
+    tbill_df,
+    on=:dateym
+)
+maxerr_rf = maximum(abs.(comp_rf.rf .- comp_rf.rf_ref))
+println("T-bill max absolute error: $maxerr_rf")
+@test maxerr_rf < 1e-6
+println("✓ T-bill matches R output")
+
+# --- Test future excess returns ---
+rmrf_df = DataImport.compute_excess_returns("output/msi.csv", tbill_df)
+comp_rmrf = innerjoin(
+    select(ref, :dateym, :rmrf_y3 => :rmrf_ref),
+    rmrf_df,
+    on=:dateym
+)
+dropmissing!(comp_rmrf)
+maxerr_rmrf = maximum(abs.(comp_rmrf.rmrf_y3 .- comp_rmrf.rmrf_ref))
+println("Excess return max absolute error: $maxerr_rmrf")
+@test maxerr_rmrf < 1e-10
+println("✓ Future excess returns match R output")
+
+# --- Test full merge ---
+predict = DataImport.build_predictors("output/msi.csv", "input/cay_current.csv")
+println("Built predict: $(nrow(predict)) obs")
+
+# Compare against R reference (264 obs)
+comp_full = innerjoin(ref, predict, on=:dateym, makeunique=true)
+println("Matched $(nrow(comp_full)) obs (R has $(nrow(ref)))")
+@test nrow(comp_full) == nrow(ref)
+
+for col in [:dp, :rf, :rmrf_y3, :cay]
+    local maxerr = maximum(abs.(comp_full[!, col] .- comp_full[!, Symbol(string(col, "_1"))]))
+    println("$col max error: $maxerr")
+    @test maxerr < 1e-6
+end
+println("✓ Full predictor merge validated")
diff --git a/test/test_regression.jl b/test/test_regression.jl
@@ -0,0 +1,39 @@
+# test/test_regression.jl
+using Test, CSV, DataFrames, Statistics
+
+# R reference coefficients from readme.md (full sample, 264 obs)
+ref_coefs = Dict(
+    :dp  => 3.370,
+    :cay => 1.814,
+    :rf  => -1.246,
+    :constant => 0.011,
+)
+ref_r2 = 0.344
+ref_nobs = 264
+
+include(joinpath(@__DIR__, "..", "src", "RiskPremium.jl"))
+using .RiskPremium
+
+# Use the R reference predict.csv directly (already validated in test_dataimport.jl)
+predict = CSV.read("output/predict.csv", DataFrame)
+result = RiskPremium.run_regression(predict)
+
+println("Coefficients:")
+println("  dp:    $(round(result.coefs[:dp], digits=3)) (ref: $(ref_coefs[:dp]))")
+println("  cay:   $(round(result.coefs[:cay], digits=3)) (ref: $(ref_coefs[:cay]))")
+println("  rf:    $(round(result.coefs[:rf], digits=3)) (ref: $(ref_coefs[:rf]))")
+println("  const: $(round(result.coefs[:constant], digits=3)) (ref: $(ref_coefs[:constant]))")
+println("R²: $(round(result.r2, digits=3)) (ref: $ref_r2)")
+println("N:  $(result.nobs) (ref: $ref_nobs)")
+
+@testset "Regression validation" begin
+    for (k, v) in ref_coefs
+        err = abs(result.coefs[k] - v)
+        println("  $k error: $err")
+        @test err < 0.01
+    end
+    @test abs(result.r2 - ref_r2) < 0.005
+    @test result.nobs == ref_nobs
+end
+
+println("✓ Regression coefficients match R output")