prais
Overview
prais implements the Prais-Winsten estimator for models with strictly exogenous regressors and AR(1) serial correlation of the errors.
Installation
CRAN
install.packages("prais")Development version
# install.packages("devtools")
devtools::install_github("franzmohr/prais")Usage
# Load the package
library(prais)
# Load the data
# install.packages("wooldridge")
library(wooldridge)
data("barium")
pw <- prais_winsten(lchnimp ~ lchempi + lgas + lrtwex + befile6 + affile6 + afdec6, data = barium)## Iteration 0: rho = 0
## Iteration 1: rho = 0.2708
## Iteration 2: rho = 0.291
## Iteration 3: rho = 0.293
## Iteration 4: rho = 0.2932
## Iteration 5: rho = 0.2932
## Iteration 6: rho = 0.2932
## Iteration 7: rho = 0.2932
summary(pw)##
## Call:
## prais_winsten(formula = lchnimp ~ lchempi + lgas + lrtwex + befile6 +
## affile6 + afdec6, data = barium)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.99386 -0.32219 0.03747 0.40226 1.50281
##
## AR(1) coefficient rho after 7 Iterations: 0.2932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -37.07770 22.77830 -1.628 0.1061
## lchempi 2.94095 0.63284 4.647 8.46e-06 ***
## lgas 1.04638 0.97734 1.071 0.2864
## lrtwex 1.13279 0.50666 2.236 0.0272 *
## befile6 -0.01648 0.31938 -0.052 0.9589
## affile6 -0.03316 0.32181 -0.103 0.9181
## afdec6 -0.57681 0.34199 -1.687 0.0942 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5733 on 124 degrees of freedom
## Multiple R-squared: 0.2021, Adjusted R-squared: 0.1635
## F-statistic: 5.235 on 6 and 124 DF, p-value: 7.764e-05
##
## Durbin-Watson statistic (original): 1.458
## Durbin-Watson statistic (transformed): 2.087
References
Prais, S. J. and Winsten, C. B. (1954): Trend Estimators and Serial Correlation. Cowles Commission Discussion Paper, 383 (Chicago).
Wooldridge, J. M. (2016). Introductory Econometrics. A Modern Approach. 6th ed. Mason, OH: South-Western Cengage Learning.