Time Series Analysis: Method and Substance Introductory Workshop on Time Series Analysis

Checking Residuals of ARMA(1,1)

• wntestq resid_m2, lags(10)
• `Portmanteau test for white noise
• ---------------------------------------
• Portmanteau (Q) statistic = 7.9763
• Prob > chi2(10) = 0.6312

Forecasting

• The last stage of the ARIMA modeling process would involve forecasting the last few points of the time series using the various models you had estimated.
• You could compare them to see which one has the smallest forecasting error.

Similar approaches

• Transfer function models involve pre-whitening the time series, removing all AR, MA, and integrated processes, and then estimating a standard OLS model.
• Example: MacKuen, Erikson, & Stimson’s work on macro-partisanship (1989)
• You can also estimate the level of fractional integration and then use the transformed data in OLS analysis (e.g. Box-Steffensmeier et al’s (2004) work on the partisan gender gap).
• In OLS, we can add explanatory variables, and various lags of those as well (distributed lag models).