Drawing statistical inference on the coefficients of a short- orlong-horizon predictive regression with persistent regressors by usingthe IVX method ofMagdalinos andPhillips (2009) andKostakis,Magdalinos and Stamatogiannis (2015).
You can install the development version fromGitHub with:
# Install release version from CRANinstall.packages("ivx")# install.packages("devtools")devtools::install_github("kvasilopoulos/ivx")library(ivx)library(magrittr)#> Warning: package 'magrittr' was built under R version 4.3.3This is a basic example, lets load the data first:
# Monthly data from Kostakis et al (2014)kms%>%names()#> [1] "Date" "DE" "LTY" "DY" "DP" "TBL" "EP" "BM" "INF" "DFY"#> [11] "NTIS" "TMS" "Ret"And then do the univariate estimation:
ivx(Ret~ DP,data = kms)%>%summary()#>#> Call:#> ivx(formula = Ret ~ DP, data = kms, horizon = 1)#>#> Coefficients:#> Estimate Wald Ind Pr(> chi)#> DP 0.006489 2.031 0.154#>#> Joint Wald statistic: 2.031 on 1 DF, p-value 0.1541#> Multiple R-squared: 0.002844, Adjusted R-squared: 0.001877ivx(Ret~ DP,data = kms,horizon =4)%>%summary()#>#> Call:#> ivx(formula = Ret ~ DP, data = kms, horizon = 4)#>#> Coefficients:#> Estimate Wald Ind Pr(> chi)#> DP 0.006931 2.271 0.132#>#> Joint Wald statistic: 2.271 on 1 DF, p-value 0.1318#> Multiple R-squared: 0.01167, Adjusted R-squared: 0.01358And the multivariate estimation, for one or multiple horizons:
ivx(Ret~ DP+ TBL,data = kms)%>%summary()#>#> Call:#> ivx(formula = Ret ~ DP + TBL, data = kms, horizon = 1)#>#> Coefficients:#> Estimate Wald Ind Pr(> chi)#> DP 0.006145 1.819 0.177#> TBL -0.080717 1.957 0.162#>#> Joint Wald statistic: 3.644 on 2 DF, p-value 0.1617#> Multiple R-squared: 0.004968, Adjusted R-squared: 0.003036ivx(Ret~ DP+ TBL,data = kms,horizon =4)%>%summary()#>#> Call:#> ivx(formula = Ret ~ DP + TBL, data = kms, horizon = 4)#>#> Coefficients:#> Estimate Wald Ind Pr(> chi)#> DP 0.006579 2.045 0.153#> TBL -0.073549 1.595 0.207#>#> Joint Wald statistic: 3.527 on 2 DF, p-value 0.1715#> Multiple R-squared: 0.018, Adjusted R-squared: 0.01895ivx_ar(hpi~ cpi,data = ylpc)%>%summary()#>#> Call:#> ivx_ar(formula = hpi ~ cpi, data = ylpc, horizon = 1)#>#> Auto () with AR terms q = 4#>#> Coefficients:#> Estimate Wald Ind Pr(> chi)#> cpi -0.0001775 4.326 0.0375 *#> ---#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#>#> Joint Wald statistic: 4.326 on 1 DF, p-value 0.03753#> Multiple R-squared: 0.02721, Adjusted R-squared: 0.02142#> Wald AR statistic: 132.3 on 4 DF, p-value < 2.2e-16Please note that the ‘ivx’ project is released with aContributorCode of Conduct. By contributing to this project, you agree to abideby its terms.