This paper introduces a new autoregressive model, with the specific feature that the lag structure can vary over time. More precise, and to keep matters simple, the autoregressive model sometimes has lag 1, and sometimes lag 2. Representation, autocorrelation, specification, inference, and the creation of forecasts are presented. A detailed illustration for annual inflation rates for eight countries in Africa shows the empirical relevance of the new model. Various potential extensions are discussed.

Additional Metadata
Keywords Autoregression, Time-varying lags, Forecasting
JEL Time-Series Models; Dynamic Quantile Regressions (jel C22), Forecasting and Other Model Applications (jel C53)
Persistent URL
Franses, Ph.H.B.F. (2020). An Introduction to time-varying lag autoregression. Retrieved from