Consistent covariance matrix estimation in probit models with autocorrelated errors

Arturo Estrella and Anthony Rodrigues ()

No 39, Staff Reports from Federal Reserve Bank of New York

Abstract: Some recent time-series applications use probit models to measure the forecasting power of a set of variables. Correct inferences about the significance of the variables requires a consistent estimator of the covariance matrix of the estimated model coefficients. A potential source of inconsistency in maximum likelihood standard errors is serial correlation in the underlying disturbances, which may arise, for example, from overlapping forecasts. We discuss several practical methods for constructing probit autocorrelation-consistent standard errors, drawing on the generalized method of moments techniques of Hansen (1982), Newey-West (1987) and others, and we provide simulation evidence that these methods can work well.

Keywords: Regression analysis; time series analysis (search for similar items in EconPapers)
Date: 1998
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr39.html (text/html)
https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr39.pdf (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:fip:fednsr:39

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Staff Reports from Federal Reserve Bank of New York Contact information at EDIRC.
Bibliographic data for series maintained by Gabriella Bucciarelli ().