How to Use Lagged Time-Series Variables in a Python Pandas Regression Model?
<p>I'm creating time-series econometric regression models. The data is stored in a Pandas data frame.</p>

<p><strong>How can I do lagged time-series econometric analysis using Python</strong>? I have used Eviews in the past (which is a standalone econometric program i.e. not a Python package). To estimate an OLS equation using Eviews you can write something like:</p>

<pre><code>equation eq1.ls log(usales) c log(usales(-1)) log(price(-1)) tv_spend radio_spend
</code></pre>

<p>Note the lagged dependent and lagged price terms. It's these lagged variables which seem to be difficult to handle using Python e.g. using scikit or statmodels (unless I've missed something).</p>

<p>Once I've created a model I'd like to perform tests and use the model to forecast.</p>

<p>I'm not interested in doing ARIMA, Exponential Smoothing, or Holt Winters time-series projections - I'm mainly interested in time-series OLS.</p>