Improved Augmented Regression Method (IARM) for Predictive Regression

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Description

Function for Improved ARM (IARM) estimation and testing

Usage

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PR.IARM(x, y, p, Rmat = diag(k * p), rvec = matrix(0, nrow = k * p))

Arguments

x

predictor or a matrix of predictors in column

y

variable to be predicted, usually data1 return

p

AR order

Rmat

Restriction matrix, refer to function Rmatrix

rvec

Restriction matrix, refer to function Rmatrix

Details

Kim J.H., 2014, Predictive Regression: Improved Augmented Regression Method, Journal of Empirical Finance, 26, 13-25.

Value

LS

Ordinary Least Squares Estimators

IARM

IARM Estimators

AR

AR parameter estimators

ARc

Bias-corrected AR parameter estimators

Fstats

Fstats and their p-values

Covbc

Covariance matrix of the IARM estimators (for the predictive coefficients only)

Note

Kim J.H., 2014, Predictive Regression: Improved Augmented Regression Method, Journal of Empirical Finance, 26, 13-25.

Author(s)

Jae H. Kim

References

Kim J.H., 2014, Predictive Regression: Improved Augmented Regression Method, Journal of Empirical Finance, 26, 13-25.

Examples

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data(data1)
# Replicating Table 5 (excess return) of Kim (2014)
y=data1$ret.nyse.vw*100 -data1$tbill*100
x=cbind(log(data1$dy.nyse), data1$tbill*100); 

Rmat1=Rmatrix(p=1,k=2,type=1,index=0); Rmat=Rmat1$Rmat; rvec=Rmat1$rvec
M=PR.IARM(x,y,p=1,Rmat,rvec)