AR Model Forecast in Stata: Out-of-Sample Time Series Forecasting

Autoregressive models are among the most widely used tools in time series econometrics and forecasting. Their central idea is simple: the past behavior of a variable may contain useful information about its future values.

In this tutorial, we study how to produce an AR model forecast in Stata, with particular emphasis on out-of-sample forecasting. As an empirical application, we use Brent crude oil prices and construct an autoregressive model for the annual growth rate of oil prices.

The objective is not only to estimate an autoregressive model, but to move from estimation to an actual forecasting exercise. This includes checking the time-series properties of the data, transforming the variable when appropriate, specifying an AR model, generating multi-period forecasts, incorporating parameter uncertainty, and constructing confidence bands around the forecast.

The complete Stata implementation used in the tutorial is available separately as part of the premium material. This article focuses on the econometric intuition and methodology behind the exercise.