Consistent Covariance Matrix Estimation in Probit Models with Autocorrelated Errors

Maximum Simulated Likelihood Estimation of Random Effects Dynamic Probit Models with Autocorrelated Errors

This paper investigates the use of Maximum Simulated Likelihood estimation for random effects dynamic probit models with autocorrelated errors. It presents a new Stata command, redpace, for this estimator and illustrates its usage. The paper also compares the use of pseudo-random numbers and Halton sequences of quasi-random numbers for the MSL estimation of these models.

Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors

Spatial Heteroskedasticity and Autocorrelation Consistent Estimation of Covariance Matrix

This paper considers spatial heteroskedasticity and autocorrelation consistent (spatial HAC) estimation of covariance matrices of parameter estimators. We generalize the spatial HAC estimators introduced by Kelejian and Prucha (2007) to apply to linear and nonlinear spatial models with moment conditions. We establish its consistency, rate of convergence and asymptotic truncated mean squared err...

More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors

We propose a modification of kernel time series regression estimators that improves efficiency when the innovation process is autocorrelated. The procedure is based on a pre-whitening transformation of the dependent variable that has to be estimated from the data. We establish the asymptotic distribution of our estimator under weak dependence conditions. It is shown that the proposed estimation...

Improved HAC Covariance Matrix Estimation Based on Forecast Errors

We propose computing HAC covariance matrix estimators based on one-stepahead forecasting errors. It is shown that this estimator is consistent and has smaller bias than other HAC estimators. Moreover, the tests that rely on this estimator have more accurate sizes without sacrificing its power.