**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 X_{t}
on the endogenous variable Y_{t}
with an exogenous control variable Z_{t}. The
t subscripts refer to the time period. In general functional form the function
of interest is

Y_{t} =
f(X_{t}, Z_{t}),

where X_{t} and Z_{t}
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 X_{t} on Y_{t}
holding Z_{t} constant.