Introduction to Time Series Regression
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The present introduction to time series regression focuses on the basic design and estimate of an economic time series model. The methods are transparent regression analysis that can be done with any statistical software. This step by step approach leads to understanding the principles of time series regression. Canned programs miss the finer points that become apparent with the present approach to the data.
The book is designed for working with a data set. The way to learn the concepts is to begin estimating an time series model. Identify a model and put together a data set. While more observations are always better, quarterly or monthly data may have seasonality that complicate the analysis. Good advice is to find yearly data for at least 40 years.
Suppose a model reduces to the effect of exogenous variable Xt on the endogenous variable Yt with an exogenous control variable Zt. The t subscripts refer to the time period. In general functional form the function of interest is
Yt = f(Xt, Zt),
where Xt and Zt may be vectors. The present introduction focuses on how to estimate a regression to reliably capture this relationship. The issue for economic theory is the significance, sign, and size of the partial derivative effect of Xt on Yt holding Zt constant.