Pre-Test Strategies for Time-Series Forecasting in the Linear Regression Model

Maxwell L. King and Mei Leng Rankin

No 267423, Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In linear time-series regression analysis, there is typically uncertainty about which variables to include as regressors and the exact form of the disturbance process. This paper uses the Monte Carlo method to investigate the predictive performance of nine different pretesting strategies for misspecification. Data generating processes used in the study include first-order autoregressive (AR(1)) disturbances, first-order moving average disturbances, an extra exogenous regressor and the lagged dependent variable as an extra regressor. We find that remarkably robust predictions for a range of misspecified models result from applying the Durbin-Watson test for autocorrelation and correcting for AR(1) disturbances when the test is significant.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 27
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/267423/files/monash-165.pdf (application/pdf)
https://ageconsearch.umn.edu/record/267423/files/monash-165.pdf?subformat=pdfa (application/pdf)

Related works:
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:ags:monebs:267423

DOI: 10.22004/ag.econ.267423

Access Statistics for this paper

More papers in Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().