Long-run covariance and its applications in cointegration regression
Qunyong Wang
Institute of Statistics and Econometrics
Nankai University
Tianjin, China
[email protected]
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Na Wu
Economics School
Tianjin University of Finance and Economics
Tianjin, China
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Abstract. Long-run covariance plays a major role in much of time-series inference, such
as heteroskedasticity- and autocorrelation-consistent standard errors,
generalized method of moments estimation, and cointegration regression. We
propose a Stata command, lrcov, to compute long-run covariance with a
prewhitening strategy and various kernel functions. We illustrate how long-run
covariance matrix estimation can be used to obtain heteroskedasticity- and
autocorrelation-consistent standard errors via the new hacreg command;
we also illustrate cointegration regression with the new cointreg
command. hacreg has several improvements compared with the official
newey command, such as more kernel functions, automatic determination
of the lag order, and prewhitening of the data. cointreg enables the
estimation of cointegration regression using fully modified ordinary least
squares, dynamic ordinary least squares, and canonical cointegration
regression methods. We use several classical examples to demonstrate the use
of these commands.
View all articles by these authors:
Qunyong Wang, Na Wu
View all articles with these keywords:
lrcov, hacreg, cointreg, long-run covariance, fully modified ordinary least squares, dynamic ordinary least squares, canonical cointegration regression
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