Estimation of Stationary Time Series Processes
M. Hashem Pesaran · Oxford University Press eBooks · 2015
This chapter begins with the problem of estimating the mean and autocovariances of a stationary process. It then considers the estimation of autoregressive and moving average processes as well as the estimation of spectral density functions. The analysis is also related to the standard ordinary least squares (OLS) regression models. It shows that when the errors are serially correlated, the OLS estimators of models with lagged dependent variables are inconsistent, and derives an asymptotic expression for the bias. Exercises are provided at the end of the chapter.