Drawing statistical inference on the coefficients of a short- or long-horizon predictive regression with persistent regressors by using the IVX method of Magdalinos and Phillips (2009) and Kostakis, Magdalinos and Stamatogiannis (2015).

## Installation

You can install the development version from GitHub with:

# Install release version from CRAN
install.packages("ivx")

# install.packages("devtools")
devtools::install_github("kvasilopoulos/ivx")

## Usage

library(ivx)
library(magrittr)

This is a basic example, lets load the data first:

# Monthly data from Kostakis et al (2014)
kms %>%
names()
#>   "Date" "DE"   "LTY"  "DY"   "DP"   "TBL"  "EP"   "BM"   "INF"  "DFY"
#>  "NTIS" "TMS"  "Ret"

## Univariate

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:  0.001877

ivx(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:  0.01358

## Multivariate

And 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:  0.003036

ivx(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:  0.01895

## Yang et al. (2020) IVX-AR methodology

ivx_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-16

Please note that the ‘ivx’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.