Consistent Covariance Matrix Estimation in Probit Models with Autocorrelated Errors

25 Pages Posted: 28 Oct 2006

Date Written: April 1998

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: probit, autocorrelation, generalized method of moments

JEL Classification: C22, C25

Suggested Citation

Estrella, Arturo and Rodrigues, Anthony P., Consistent Covariance Matrix Estimation in Probit Models with Autocorrelated Errors (April 1998). FRB of New York Staff Report No. 39. Available at SSRN: https://ssrn.com/abstract=940655 or http://dx.doi.org/10.2139/ssrn.940655

Anthony P. Rodrigues

Federal Reserve Bank of New York ( email )

33 Liberty Street
New York, NY 10045
United States

No contact information is available for Arturo Estrella

Register to save articles to
your library

Register

Paper statistics

Downloads
224
Abstract Views
1,276
rank
137,612
PlumX Metrics