We propose a new periodic autoregressive model for seasonally observed time series, where the number of seasons can potentially be very large. The main novelty is that we collect the periodic coefficients in a second-level stochastic model. This leads to a random-coefficient periodic autoregression with a substantial reduction in the number of parameters to be estimated. We discuss representation, parameter estimation, and inference. An illustration for monthly growth rates of US industrial production shows the merits of the new model specification.

Additional Metadata
Keywords periodic autoregression, random coefficient model
JEL Time-Series Models; Dynamic Quantile Regressions (jel C22), Model Construction and Estimation (jel C51)
Persistent URL dx.doi.org/10.1111/j.1467-9574.2010.00477.x, hdl.handle.net/1765/22658
Series Econometric Institute Reprint Series
Journal Statistica Neerlandica
Franses, Ph.H.B.F, & Paap, R. (2011). Random-coefficient periodic autoregressions. Statistica Neerlandica, 65(1), 101–115. doi:10.1111/j.1467-9574.2010.00477.x