Using gret l for Principles of Econometrics, 4th Edition

Estimation with Serially Correlated Errors

In this section, several methods of estimating models with serially correlated errors will be explored. We will use least squares with robust standard errors to estimate regression models with serial correlation in the errors. We also consider the nonlinear least squares estimator of the model and a more general strategy for estimating models with serially correlation. In the appendix to this chapter, you will find some traditional estimators of this model as well.

9.6.1 Least Squares and HAC Standard Errors

As is the case with heteroskedastic errors, there is a statistically valid way to use least squares when your data are autocorrelated. In this case you can use an estimator of standard errors that is robust to both heteroskedasticity and autocorrelation. This estimator is sometimes called HAC, which stands for heteroskedasticity autocorrelated consistent. This and some issues that surround its use are discussed in the next few sections.

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Using gret l for Principles of Econometrics, 4th Edition

Simulation

In appendix 10F of POE4, the authors conduct a Monte Carlo experiment comparing the performance of OLS and TSLS. The basic simulation is based on the model y = x …

Hausman Test

The Hausman test probes the consistency of the random effects estimator. The null hypothesis is that these estimates are consistent-that is, that the requirement of orthogonality of the model’s errors …

Time-Varying Volatility and ARCH Models: Introduction to Financial Econometrics

In this chapter we’ll estimate several models in which the variance of the dependent variable changes over time. These are broadly referred to as ARCH (autoregressive conditional heteroskedas - ticity) …

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