commit 4cbf5365743d5953354914b2b826f40129c2999e
parent 5c772597840b6dd5d1e5c1db49ef3a6d5d5ae21e
Author: Erik Loualiche <[email protected]>
Date: Tue, 4 Jun 2019 14:33:55 -0400
Clean citation
Diffstat:
5 files changed, 81 insertions(+), 12 deletions(-)
diff --git a/log/R-session-info.log.R b/log/R-session-info.log.R
@@ -128,3 +128,68 @@
withr 2.1.2 2018-03-15 CRAN (R 3.5.1)
zeallot 0.1.0 2018-01-28 CRAN (R 3.5.1)
zoo * 1.8-3 2018-07-16 CRAN (R 3.5.1)
+
+
+# -----
+# Session info for rp_measure.csv
+
+ setting value
+ version R version 3.5.1 (2018-07-02)
+ system x86_64, darwin15.6.0
+ ui X11
+ language (EN)
+ collate en_US.UTF-8
+ tz America/New_York
+ date 2019-06-04
+
+ package * version date source
+ assertthat 0.2.0 2017-04-11 CRAN (R 3.5.1)
+ backports 1.1.2 2017-12-13 CRAN (R 3.5.1)
+ base * 3.5.1 2018-08-05 local
+ colorspace 1.3-2 2016-12-14 CRAN (R 3.5.1)
+ compiler 3.5.1 2018-08-05 local
+ crayon * 1.3.4 2019-05-19 Github (gaborcsardi/crayon@84be620)
+ data.table * 1.12.3 2019-06-03 local
+ datasets * 3.5.1 2018-08-05 local
+ devtools * 1.13.6 2018-06-27 CRAN (R 3.5.1)
+ digest 0.6.15 2018-01-28 CRAN (R 3.5.1)
+ dplyr 0.8.0.9010 2019-03-31 Github (tidyverse/dplyr@6832c62)
+ ggplot2 * 3.1.0.9000 2019-03-31 Github (tidyverse/ggplot2@230e8f7)
+ glue 1.3.0 2018-07-17 CRAN (R 3.5.1)
+ graphics * 3.5.1 2018-08-05 local
+ grDevices * 3.5.1 2018-08-05 local
+ grid 3.5.1 2018-08-05 local
+ gtable 0.2.0 2016-02-26 CRAN (R 3.5.1)
+ lattice 0.20-35 2017-03-25 CRAN (R 3.5.1)
+ lazyeval 0.2.1 2017-10-29 CRAN (R 3.5.1)
+ lmtest * 0.9-36 2018-04-04 CRAN (R 3.5.1)
+ lubridate * 1.7.4 2018-04-11 CRAN (R 3.5.1)
+ magrittr 1.5 2014-11-22 CRAN (R 3.5.1)
+ matrixStats 0.54.0 2018-07-23 CRAN (R 3.5.1)
+ memoise 1.1.0 2017-04-21 CRAN (R 3.5.1)
+ methods * 3.5.1 2018-08-05 local
+ munsell 0.5.0 2018-06-12 CRAN (R 3.5.1)
+ parallel 3.5.1 2018-08-05 local
+ pillar 1.4.0.9002 2019-05-19 Github (r-lib/pillar@5ded57f)
+ pkgconfig 2.0.2 2019-05-19 Github (r-lib/pkgconfig@5453f79)
+ purrr 0.2.5 2018-05-29 CRAN (R 3.5.1)
+ R6 2.2.2 2017-06-17 CRAN (R 3.5.0)
+ Rcpp 1.0.1.3 2019-05-19 Github (RcppCore/Rcpp@6062d56)
+ rlang 0.3.4.9003 2019-05-19 Github (r-lib/rlang@6a232c0)
+ sandwich * 2.4-0 2017-07-26 CRAN (R 3.5.1)
+ scales 1.0.0.9000 2019-03-31 Github (r-lib/scales@c374014)
+ stargazer * 5.2.2 2018-05-30 CRAN (R 3.5.1)
+ statar * 0.6.5 2017-07-06 CRAN (R 3.5.1)
+ stats * 3.5.1 2018-08-05 local
+ stringi 1.2.4 2018-07-20 CRAN (R 3.5.1)
+ stringr * 1.3.1 2018-05-10 CRAN (R 3.5.1)
+ tibble 2.1.1.9000 2019-05-19 Github (tidyverse/tibble@71b8ff6)
+ tidyr 0.8.3.9000 2019-05-19 Github (tidyverse/tidyr@b6ec78b)
+ tidyselect 0.2.5.9000 2019-05-19 Github (tidyverse/tidyselect@19150c0)
+ tools 3.5.1 2018-08-05 local
+ utils * 3.5.1 2018-08-05 local
+ vctrs 0.1.0.9003 2019-05-19 Github (r-lib/vctrs@b1e6b81)
+ wesanderson * 0.3.6 2018-04-20 CRAN (R 3.5.1)
+ withr 2.1.2 2018-03-15 CRAN (R 3.5.1)
+ zeallot 0.1.0 2018-01-28 CRAN (R 3.5.1)
+ zoo * 1.8-3 2018-07-16 CRAN (R 3.5.1)
diff --git a/log/rp_measure.log.R b/log/rp_measure.log.R
@@ -36,7 +36,7 @@ Type 'q()' to quit R.
Log file for code executed at
> message(format(Sys.time(), "%a %b %d %X %Y"))
-Tue Jun 04 12:52:01 2019
+Tue Jun 04 14:33:17 2019
> ##################################################################################
>
>
@@ -197,7 +197,7 @@ Notes: ***Significant at the 1 percent level.
> # PLOT
> dt_plot <- dt_exp_rmrf[, .(date=as.Date(ISOdate(str_sub(dateym,1, 4), str_sub(dateym, 5, 6), 1)),
+ dp, cay, rf, rmrf_y3, exp_rmrf)]
-> dt_plot
+> dt_plot[]
date dp cay rf rmrf_y3 exp_rmrf
1: 1952-01-01 0.05812871 0.01646544 0.0157 0.17701996 0.201294617
2: 1952-02-01 0.05899675 0.02551783 0.0154 0.19964326 0.222836429
@@ -211,6 +211,7 @@ Notes: ***Significant at the 1 percent level.
255: 2015-03-01 0.02111608 -0.02656083 0.0003 0.08734601 0.025601134
256: 2015-04-01 0.02109137 -0.03519129 0.0002 0.08565438 0.007723565
>
+>
> p0 <- dt_plot[, .(date, dp, cay, rf, rmrf_y3) ] %>%
+ melt(id.vars="date") %>%
+ ggplot(aes(date, value, colour = variable)) +
@@ -230,12 +231,14 @@ Notes: ***Significant at the 1 percent level.
+ 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
- 2.500 0.207 2.776
+ 2.539 0.266 2.953
diff --git a/readme.md b/readme.md
@@ -3,12 +3,11 @@
-This code updates the measure of equity risk premium from the paper **Buyout Activity: the Impact of Aggregate Discount Rates** in the *Journal of Finance*
-
-Authors: Valentin Haddad, Erik Loualiche & Matthew Plosser.
+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)
diff --git a/src/readme_in.md b/src/readme_in.md
@@ -3,12 +3,11 @@
-This code updates the measure of equity risk premium from the paper **Buyout Activity: the Impact of Aggregate Discount Rates** in the *Journal of Finance*
-
-Authors: Valentin Haddad, Erik Loualiche & Matthew Plosser.
+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)
diff --git a/src/rp_measure.R b/src/rp_measure.R
@@ -78,7 +78,8 @@ fwrite(dt_exp_rmrf, "./output/predict.csv")
# PLOT
dt_plot <- dt_exp_rmrf[, .(date=as.Date(ISOdate(str_sub(dateym,1, 4), str_sub(dateym, 5, 6), 1)),
dp, cay, rf, rmrf_y3, exp_rmrf)]
-dt_plot
+dt_plot[]
+
p0 <- dt_plot[, .(date, dp, cay, rf, rmrf_y3) ] %>%
melt(id.vars="date") %>%
@@ -99,8 +100,10 @@ p1 <- dt_plot[, .(date, 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)
+##################################################################################
+##################################################################################
+
