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Periodically integrated subset autoregressions for dutch industrial production and money stock
Author(s) -
Franses Philip Hans
Publication year - 1993
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980120706
Subject(s) - autocorrelation , industrial production , univariate , econometrics , stock (firearms) , lag , economics , vector autoregression , series (stratigraphy) , partial autocorrelation function , autoregressive model , production (economics) , time series , mathematics , computer science , statistics , macroeconomics , autoregressive integrated moving average , multivariate statistics , mechanical engineering , computer network , paleontology , biology , engineering
The univariate quarterly Dutch series of industrial production and money stock are both modelled with a periodically integrated subset autoregression (PISA). This model for a non‐stationary series allows the lag orders, the values of the parameters and the cyclical patterns to vary over the seasons. The PISA models are found by applying a general‐to‐simple specification strategy, which deals with non‐stationarity and periodicity simultaneously. It is found that the two series show a common asymmetric cyclical behaviour. This paper further proposes a test for periodicity in the errors, with which it is argued that a non‐periodic model for the industrial production and money stock is misspecified and that seasonal adjustment does not remove periodicity in the autocorrelation function.