commit cafb1aaf36fefe07113763dba8dfdf1476c12700
parent 72c9aecff94745aae83882a40c4a2f801870b780
Author: Erik Loualiche <eloualic@umn.edu>
Date: Tue, 27 Aug 2019 21:52:10 +0200
Align cay quarterly dates properly with other monthly variables
Diffstat:
10 files changed, 370 insertions(+), 358 deletions(-)
diff --git a/.gitignore b/.gitignore
@@ -9,4 +9,7 @@ tmp
# Specific files
docs/dividendpriceratio.tex
-input/msi.sas7bdat-
\ No newline at end of file
+input/msi.sas7bdat
+
+# Sources
+src/paymon.R+
\ No newline at end of file
diff --git a/Makefile b/Makefile
@@ -2,7 +2,7 @@
# Makefile for PREDICTING THE EQUITY RISK PREMIUM
#
# Created on June 04th 2019
-# Last modified on April 04th 2019
+# Last modified on June 14th 2019
## --------------------------------------------------------------------------------------------------------
@@ -14,10 +14,12 @@ all: output/predict.csv readme.md
## DOWNLOAD THE DATA FROM WRDS
input/msi.sas7bdat:
rsync -aPvzh --stats --human-readable XXXX@wrds-cloud.wharton.upenn.edu:/wrds/crsp/sasdata/a_stock/msi.sas7bdat ./input/
+ @echo
## GENERATE PREDICTORS
tmp/predict.csv: src/import_predictors.R input/cay_current.csv input/msi.sas7bdat
R CMD BATCH $(R_OPTS) src/import_predictors.R log/import_predictors.log.R
+ @echo
## RUN REGRESSIONS
output/predict.csv: src/rp_measure.R tmp/predict.csv
@@ -27,6 +29,7 @@ output/predict.csv: src/rp_measure.R tmp/predict.csv
## OUTPUT RESULTS
readme.md: src/readme_in.md
cat src/readme_in.md tmp/reg_update.txt > readme.md
+ @echo
##
## --------------------------------------------------------------------------------------------------------
diff --git a/log/R-session-info.log.R b/log/R-session-info.log.R
@@ -9,8 +9,8 @@
ui X11
language (EN)
collate en_US.UTF-8
- tz America/New_York
- date 2019-06-10
+ tz Europe/Paris
+ date 2019-08-27
package * version date source
alfred * 0.1.6 2018-04-06 CRAN (R 3.5.1)
@@ -19,14 +19,14 @@
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
+ crayon * 1.3.4 2019-07-10 Github (gaborcsardi/crayon@84be620)
+ data.table * 1.12.3 2019-07-02 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)
+ dplyr * 0.8.3.9000 2019-07-05 Github (tidyverse/dplyr@b1b79b9)
forcats 0.3.0 2018-02-19 CRAN (R 3.5.1)
- ggplot2 3.1.0.9000 2019-03-31 Github (tidyverse/ggplot2@230e8f7)
+ ggplot2 3.2.0.9000 2019-07-10 Github (tidyverse/ggplot2@125cc5f)
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
@@ -43,24 +43,24 @@
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)
+ pillar 1.4.2.9000 2019-07-10 Github (r-lib/pillar@a6aa768)
+ pkgconfig 2.0.2 2019-07-10 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)
+ Rcpp 1.0.1.3 2019-07-10 Github (RcppCore/Rcpp@d04eae8)
RcppRoll * 0.3.0 2018-06-05 CRAN (R 3.5.1)
- rlang 0.3.4.9003 2019-05-19 Github (r-lib/rlang@6a232c0)
- scales 1.0.0.9000 2019-03-31 Github (r-lib/scales@c374014)
+ rlang 0.4.0.9000 2019-07-10 Github (r-lib/rlang@a3d7e47)
+ scales 1.0.0.9000 2019-07-10 Github (r-lib/scales@7f6f4a5)
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)
+ tibble 2.1.3.9000 2019-07-10 Github (tidyverse/tibble@f0d0106)
tidyr 0.8.3.9000 2019-05-19 Github (tidyverse/tidyr@b6ec78b)
- tidyselect 0.2.5.9000 2019-05-19 Github (tidyverse/tidyselect@19150c0)
+ tidyselect 0.2.5.9000 2019-07-05 Github (tidyverse/tidyselect@013a51a)
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)
+ vctrs 0.2.0.9000 2019-07-10 Github (r-lib/vctrs@9c5b2cc)
withr 2.1.2 2018-03-15 CRAN (R 3.5.1)
zeallot 0.1.0 2018-01-28 CRAN (R 3.5.1)
@@ -74,8 +74,8 @@
ui X11
language (EN)
collate en_US.UTF-8
- tz America/New_York
- date 2019-06-10
+ tz Europe/Paris
+ date 2019-08-27
package * version date source
assertthat 0.2.0 2017-04-11 CRAN (R 3.5.1)
@@ -83,13 +83,13 @@
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
+ crayon * 1.3.4 2019-07-10 Github (gaborcsardi/crayon@84be620)
+ data.table * 1.12.3 2019-07-02 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)
+ dplyr 0.8.3.9000 2019-07-05 Github (tidyverse/dplyr@b1b79b9)
+ ggplot2 * 3.2.0.9000 2019-07-10 Github (tidyverse/ggplot2@125cc5f)
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
@@ -105,25 +105,25 @@
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)
+ pillar 1.4.2.9000 2019-07-10 Github (r-lib/pillar@a6aa768)
+ pkgconfig 2.0.2 2019-07-10 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)
+ Rcpp 1.0.1.3 2019-07-10 Github (RcppCore/Rcpp@d04eae8)
+ rlang 0.4.0.9000 2019-07-10 Github (r-lib/rlang@a3d7e47)
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)
+ scales 1.0.0.9000 2019-07-10 Github (r-lib/scales@7f6f4a5)
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)
+ tibble 2.1.3.9000 2019-07-10 Github (tidyverse/tibble@f0d0106)
tidyr 0.8.3.9000 2019-05-19 Github (tidyverse/tidyr@b6ec78b)
- tidyselect 0.2.5.9000 2019-05-19 Github (tidyverse/tidyselect@19150c0)
+ tidyselect 0.2.5.9000 2019-07-05 Github (tidyverse/tidyselect@013a51a)
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)
+ vctrs 0.2.0.9000 2019-07-10 Github (r-lib/vctrs@9c5b2cc)
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)
diff --git a/log/import_predictors.log.R b/log/import_predictors.log.R
@@ -48,7 +48,7 @@ Type 'q()' to quit R.
Log file for code executed at
> message(format(Sys.time(), "%a %b %d %X %Y"))
-Mon Jun 10 11:42:28 2019
+Tue Aug 27 21:49:24 2019
> ##################################################################################
>
>
@@ -93,7 +93,7 @@ The following objects are masked from ‘package:lubridate’:
The following objects are masked from ‘package:dplyr’:
- between, first, last
+ between, coalesce, first, last
>
> check_file = file.exists("log/R-session-info.log.R")
@@ -117,20 +117,22 @@ Packages ----------------------------------------------------------------------
> ##################################################################################
> # 2. CAY
> dt_cay <- fread("./input/cay_current.csv", skip=1, header=T)
-> dt_cay <- dt_cay[, .(dateym=as.integer(str_sub(date, 1, 6)), cay=cay) ]
+> dt_cay <- dt_cay[, .(date_y=as.integer(str_sub(date, 1, 4)),
++ quarter=as.integer(str_sub(date, 5, 6)), cay=cay) ]
+> dt_cay <- dt_cay[, .(dateym=date_y*100+quarter*3, cay) ]
> dt_cay
dateym cay
- 1: 195201 0.01646544
- 2: 195202 0.02551783
- 3: 195203 0.01633620
- 4: 195204 0.02542006
- 5: 195301 0.02543387
+ 1: 195203 0.01646544
+ 2: 195206 0.02551783
+ 3: 195209 0.01633620
+ 4: 195212 0.02542006
+ 5: 195303 0.02543387
---
-259: 201603 -0.02916950
-260: 201604 -0.02042846
-261: 201701 -0.02529660
-262: 201702 -0.02655350
-263: 201703 -0.02760623
+259: 201609 -0.02916950
+260: 201612 -0.02042846
+261: 201703 -0.02529660
+262: 201706 -0.02655350
+263: 201709 -0.02760623
> ##################################################################################
>
>
@@ -203,20 +205,21 @@ Packages ----------------------------------------------------------------------
> 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) & !is.na(cay) ]
+> dt_predict <- dt_predict[ !is.na(rmrf_y3) ]
+> dt_predict <- dt_predict[ !is.na(cay) ]
> dt_predict[]
dateym dp rf rmrf_y3 cay
- 1: 195201 0.05812871 0.0157 0.17701996 0.01646544
- 2: 195202 0.05899675 0.0154 0.19964326 0.02551783
- 3: 195203 0.05817138 0.0159 0.18092953 0.01633620
- 4: 195204 0.05847809 0.0157 0.21473395 0.02542006
- 5: 195301 0.05472504 0.0196 0.21110181 0.02543387
+ 1: 195203 0.05817138 0.0159 0.18092953 0.01646544
+ 2: 195206 0.05739649 0.0170 0.21642173 0.02551783
+ 3: 195209 0.05709103 0.0171 0.23193277 0.01633620
+ 4: 195212 0.05522191 0.0209 0.22202729 0.02542006
+ 5: 195303 0.05455042 0.0201 0.26058722 0.02543387
---
-252: 201404 0.02159773 0.0003 0.08441447 -0.02747550
-253: 201501 0.02090927 0.0003 0.12632351 -0.03462231
-254: 201502 0.02098770 0.0002 0.09072934 -0.03462943
-255: 201503 0.02111608 0.0003 0.08734601 -0.02656083
-256: 201504 0.02109137 0.0002 0.08565438 -0.03519129
+252: 201412 0.02094913 0.0003 0.09787760 -0.02747550
+253: 201503 0.02111608 0.0003 0.08734601 -0.03462231
+254: 201506 0.02111542 0.0002 0.09928718 -0.03462943
+255: 201509 0.02153820 0.0002 0.15268617 -0.02656083
+256: 201512 0.02191271 0.0023 0.07465740 -0.03519129
>
> fwrite(dt_predict, "./tmp/predict.csv")
> ##################################################################################
@@ -237,4 +240,4 @@ Packages ----------------------------------------------------------------------
>
> proc.time()
user system elapsed
- 1.670 0.187 2.484
+ 1.400 0.153 2.169
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"))
-Mon Jun 10 11:42:31 2019
+Tue Aug 27 21:49:26 2019
> ##################################################################################
>
>
@@ -129,24 +129,24 @@ Packages ----------------------------------------------------------------------
test test
(1) (2) (3) (4)
-------------------------------------------------------------------------------------------
-dp 2.932*** 2.932*** 2.788*** 2.788***
- (0.411) (1.022) (0.411) (1.077)
+dp 2.976*** 2.976*** 2.833*** 2.833***
+ (0.415) (1.016) (0.419) (1.081)
-cay 2.363*** 2.363*** 2.076*** 2.076***
- (0.244) (0.545) (0.234) (0.568)
+cay 2.561*** 2.561*** 2.270*** 2.270***
+ (0.242) (0.548) (0.233) (0.565)
-rf -0.852*** -0.852** -1.093*** -1.093***
- (0.161) (0.353) (0.151) (0.376)
+rf -0.861*** -0.861** -1.152*** -1.152***
+ (0.166) (0.346) (0.156) (0.378)
-Constant 0.001 0.001 0.022* 0.022
- (0.014) (0.036) (0.013) (0.036)
+Constant -0.001 -0.001 0.024* 0.024
+ (0.014) (0.034) (0.013) (0.036)
-------------------------------------------------------------------------------------------
Observations 236 256
-R2 0.438 0.400
-Adjusted R2 0.431 0.393
-Residual Std. Error 0.070 (df = 232) 0.070 (df = 252)
-F Statistic 60.270*** (df = 3; 232) 56.022*** (df = 3; 252)
+R2 0.463 0.423
+Adjusted R2 0.456 0.416
+Residual Std. Error 0.069 (df = 232) 0.070 (df = 252)
+F Statistic 66.603*** (df = 3; 232) 61.652*** (df = 3; 252)
===========================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01
>
@@ -159,21 +159,21 @@ Note: *p<0.1; **p<0.05
===========================================================
Future Excess Returns
-----------------------------------------------------------
-D/P ratio 2.788***
- (0.411)
+D/P ratio 2.833***
+ (0.419)
-cay 2.076***
- (0.234)
+cay 2.270***
+ (0.233)
-T-bill (three-month) -1.093***
- (0.151)
+T-bill (three-month) -1.152***
+ (0.156)
-Constant 0.022*
+Constant 0.024*
(0.013)
Observations 256
-R2 0.400
-F Statistic 56.022*** (df = 3; 252)
+R2 0.423
+F Statistic 61.652*** (df = 3; 252)
-----------------------------------------------------------
Notes: ***Significant at the 1 percent level.
**Significant at the 5 percent level.
@@ -196,21 +196,21 @@ Notes: ***Significant at the 1 percent level.
> ##################################################################################
> # PLOT
> dt_plot <- dt_exp_rmrf[, .(
-+ date=as.Date(ISOdate(str_sub(dateym,1, 4), 3*as.integer(str_sub(dateym, 5, 6)), 1)),
++ 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 0.05812871 0.01646544 0.0157 0.17701996 0.201294617
- 2: 1952-06-01 0.05899675 0.02551783 0.0154 0.19964326 0.222836429
- 3: 1952-09-01 0.05817138 0.01633620 0.0159 0.18092953 0.200926735
- 4: 1952-12-01 0.05847809 0.02542006 0.0157 0.21473395 0.220859636
- 5: 1953-03-01 0.05472504 0.02543387 0.0196 0.21110181 0.206163415
+ 1: 1952-03-01 0.05817138 0.01646544 0.0159 0.18092953 0.207727996
+ 2: 1952-06-01 0.05739649 0.02551783 0.0170 0.21642173 0.224814274
+ 3: 1952-09-01 0.05709103 0.01633620 0.0171 0.23193277 0.202990900
+ 4: 1952-12-01 0.05522191 0.02542006 0.0209 0.22202729 0.213936934
+ 5: 1953-03-01 0.05455042 0.02543387 0.0201 0.26058722 0.212987813
---
-252: 2014-12-01 0.02159773 -0.02747550 0.0003 0.08441447 0.025044998
-253: 2015-03-01 0.02090927 -0.03462231 0.0003 0.12632351 0.008287874
-254: 2015-06-01 0.02098770 -0.03462943 0.0002 0.09072934 0.008601014
-255: 2015-09-01 0.02111608 -0.02656083 0.0003 0.08734601 0.025601134
-256: 2015-12-01 0.02109137 -0.03519129 0.0002 0.08565438 0.007723565
+252: 2014-12-01 0.02094913 -0.02747550 0.0003 0.09787760 0.020501440
+253: 2015-03-01 0.02111608 -0.03462231 0.0003 0.08734601 0.004750878
+254: 2015-06-01 0.02111542 -0.03462943 0.0002 0.09928718 0.004848069
+255: 2015-09-01 0.02153820 -0.02656083 0.0002 0.15268617 0.024361936
+256: 2015-12-01 0.02191271 -0.03519129 0.0023 0.07465740 0.003411385
>
>
> p0 <- dt_plot[, .(date, dp, cay, rf, rmrf_y3) ] %>%
@@ -240,4 +240,4 @@ Notes: ***Significant at the 1 percent level.
>
> proc.time()
user system elapsed
- 2.672 0.240 2.992
+ 2.563 0.196 2.694
diff --git a/output/predict.csv b/output/predict.csv
@@ -1,257 +1,257 @@
dateym,dp,rf,rmrf_y3,cay,exp_rmrf
-195201,0.0581287131894728,0.0157,0.177019961562261,0.016465442,0.201294617486037
-195202,0.0589967453225037,0.0154,0.19964325804685,0.025517831,0.222836429411759
-195203,0.0581713806310763,0.0159,0.180929532251361,0.016336205,0.200926734684572
-195204,0.0584780946526435,0.0157,0.214733953196853,0.025420057,0.220859636404277
-195301,0.0547250371055443,0.0196,0.211101809171043,0.025433872,0.206163415216804
-195302,0.0546498098979295,0.0197,0.227929429874815,0.020293056,0.195171419127699
-195303,0.0545504244369111,0.0201,0.260587223971332,0.019145117,0.192074005009544
-195304,0.054206387694773,0.0219,0.2748816063734,0.015786553,0.182175259682799
-195401,0.0570604043316226,0.0118,0.21766172756048,0.020888805,0.211760914645625
-195402,0.0568472586516797,0.0097,0.202697379083833,0.026069547,0.224217091252964
-195403,0.056836429875606,0.0103,0.196594741462133,0.026467811,0.224358164083958
-195404,0.0566512145759913,0.0097,0.196775291888223,0.026721624,0.22502431191414
-195501,0.0487919016478713,0.0122,0.05890251816168,0.036556615,0.220799481829112
-195502,0.0470588193792381,0.0117,0.0424017681122801,0.029382724,0.201620006439142
-195503,0.0464351569511314,0.0128,0.0538608706994908,0.021232813,0.181759051820372
-195504,0.0461107465486547,0.0159,0.053349266978119,0.020523432,0.175994639323023
-195601,0.0422207458629858,0.0241,0.110128089148917,0.007713701,0.129594932312297
-195602,0.0417131044839564,0.0232,0.0997714119762674,0.001744495,0.116770150872083
-195603,0.0410553744034771,0.0225,0.0761698431974343,0.001832813,0.115884591875336
-195604,0.0410189463354631,0.026,0.0883760916392848,-0.001335726,0.105380495628137
-195701,0.0403717456935299,0.0311,0.0939832648286218,0.00339647,0.107828273231766
-195702,0.0410273505043959,0.031,0.106745572597796,-0.003867536,0.0946843648738425
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diff --git a/output/predict.png b/output/predict.png
Binary files differ.
diff --git a/readme.md b/readme.md
@@ -30,21 +30,21 @@ We use the dividend-price ratio, cay and the three-month T-bill to predict futur
===========================================================
Future Excess Returns
-----------------------------------------------------------
-D/P ratio 2.788***
- (0.411)
+D/P ratio 2.833***
+ (0.419)
-cay 2.076***
- (0.234)
+cay 2.270***
+ (0.233)
-T-bill (three-month) -1.093***
- (0.151)
+T-bill (three-month) -1.152***
+ (0.156)
-Constant 0.022*
+Constant 0.024*
(0.013)
Observations 256
-R2 0.400
-F Statistic 56.022*** (df = 3; 252)
+R2 0.423
+F Statistic 61.652*** (df = 3; 252)
-----------------------------------------------------------
Notes: ***Significant at the 1 percent level.
**Significant at the 5 percent level.
diff --git a/src/import_predictors.R b/src/import_predictors.R
@@ -63,7 +63,9 @@ dt_tbill <- dt_tbill[, .(dateym=year(date)*100+month(date), rf=rf/100)]
##################################################################################
# 2. CAY
dt_cay <- fread("./input/cay_current.csv", skip=1, header=T)
-dt_cay <- dt_cay[, .(dateym=as.integer(str_sub(date, 1, 6)), cay=cay) ]
+dt_cay <- dt_cay[, .(date_y=as.integer(str_sub(date, 1, 4)),
+ quarter=as.integer(str_sub(date, 5, 6)), cay=cay) ]
+dt_cay <- dt_cay[, .(dateym=date_y*100+quarter*3, cay) ]
dt_cay
##################################################################################
@@ -113,7 +115,8 @@ dt_rmrf[ !is.na(rmrf_y3) ]
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) & !is.na(cay) ]
+dt_predict <- dt_predict[ !is.na(rmrf_y3) ]
+dt_predict <- dt_predict[ !is.na(cay) ]
dt_predict[]
fwrite(dt_predict, "./tmp/predict.csv")
diff --git a/src/rp_measure.R b/src/rp_measure.R
@@ -77,7 +77,7 @@ fwrite(dt_exp_rmrf, "./output/predict.csv")
##################################################################################
# PLOT
dt_plot <- dt_exp_rmrf[, .(
- date=as.Date(ISOdate(str_sub(dateym,1, 4), 3*as.integer(str_sub(dateym, 5, 6)), 1)),
+ 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[]
