Does seasonality influence the dating of business cycle turning points?*
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On data transformations and evidence of nonlinearity
2002, Computational Statistics and Data AnalysisCitation Excerpt :Using seasonally adjusted data also ensures that the auxiliary test regressions for nonlinearity do not contain too many parameters. We are however familiar with the results in Ghysels et al. (1996) and Franses and Paap (1999), where it is documented that Census X-12 type seasonal adjustment may introduce nonlinearity in otherwise linear time series, and that it changes the key parameters in nonlinear models, respectively. A description of the data is given in Tables 1–4 in De Bruin and Franses (1998).
Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting
2019, Applied Time Series Analysis: A Practical Guide to Modeling and ForecastingLarge Shocks and the Business Cycle: The Effect of Outlier Adjustments
2018, Journal of Business Cycle ResearchImproving the Timeliness of Turning Signals for Business Cycles with Monthly Data
2016, Journal of ForecastingOn the Predictive Content of Nonlinear Transformations of Lagged Autoregression Residuals and Time Series Observations
2016, Jahrbucher fur Nationalokonomie und Statistik
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The first author thanks the Royal Netherlands Academy of Arts and Sciences and the second author thanks the Netherlands Foundation for Scientific Research (N.W.O.) for their financial support. We thank James Hamilton for his comments and for providing Gauss programs. Thanks are also due to two anonymous referees for their helpful comments. An earlier version of this paper titled “Does Seasonal Adjustment Change Inference from Markov Switching Models?” was presented at the Econometric Society European Meeting 1996 in Istanbul, Turkey. Comments from Robert Engle and Svend Hylleberg proved very helpful.