**Introduction to Time Series Regression**

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The
present introduction to time series regression focuses on the basics of
designing and estimating an economic model.
The methods are transparent regression analysis that can be done with
any statistical software. While this
step by step approach is slow, it does lead to understanding the principles of
time series regression analysis. Canned
programs may be quicker but often miss the finer points that become apparent
with the present approach handling of the data.

The
book is designed for working with a data set.
The way to learn the concepts is to work with estimating an economic
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 and at least 40 years of history.

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 to time
series regression focuses on how to estimate regressions based on 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. Lags of the two independent variables X_{t-i} and Z_{t-i}
can be considered.