EconPapers    
Economics at your fingertips  
 

On Robust Inference in Time Series Regression

Richard T. Baillie (), Francis Diebold (), George Kapetanios () and Kun Ho Kim ()
Additional contact information
Richard T. Baillie: Michigan State University King’s College, University of London
George Kapetanios: King’s College, University of London
Kun Ho Kim: Yeshiva University, New York

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: Least squares regression with heteroskedasticity and autocorrelation consistent (HAC) standard errors has proved very useful in cross section environments. However, several major di?culties, which are generally overlooked, must be confronted when transferring the HAC estimation technology to time series environments. First, most economic time series have strong autocorrelation, which renders HAC regression parameter estimates highly inef?cient. Second, strong autocorrelation similarly renders HAC conditional predictions highly ine?cient. Finally, the structure of most popular HAC estimators is ill-suited to capture the autoregressive autocorrelation typically present in economic time series, which produces large size distortions and reduced power in hypothesis testing, in all but the largest sample sizes. We show that all three problems are largely avoided by the use of a simple dynamic regression (DynReg), which is easily implemented and also avoids possible problems concerning strong exogeneity. We demonstrate the advantages of DynReg with detailed simulations covering a range of practical issues.

Keywords: Serial correlation; heteroskedasticity and autocorrelation consistent (HAC) regression; dynamic regression (search for similar items in EconPapers)
JEL-codes: C13 C22 C31 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2022-03-24
References: Add references at CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
https://economics.sas.upenn.edu/sites/default/files/filevault/22-012.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
Working Paper: On Robust Inference in Time Series Regression (2022) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:22-012

Access Statistics for this paper

More papers in PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania 133 South 36th Street, Philadelphia, PA 19104. Contact information at EDIRC.
Bibliographic data for series maintained by Administrator ().

 
Page updated 2023-02-04
Handle: RePEc:pen:papers:22-012