Seasonal unit roots and forecasts of two-digit European industrial production

Abstract Monthly industrial production in important sectors of the German, French and UK economies are shown to exhibit very strong seasonality, such that typically 80% or more of the variation in monthly growth can be attributed to seasonal effects. Seasonal unit root test results imply that most of these series should be modelled using conventional first differences with the inclusion of monthly dummy variables, rather than as specifications involving other levels of differencing. However, when the post-sample forecast accuracy is compared for various models, annual difference specifications often produce the most accurate forecasts at horizons of up to a year. First difference models appear to be most accurate at short forecast horizons for series where seasonality is particularly marked. The study also examines the impact of updating coefficient estimates and model respecification within the forecast period.

[1]  Kenneth F. Wallis,et al.  Unobserved-Components Models For Seasonal Adjustment Filters , 1984 .

[2]  Byung Sam Yoo,et al.  Seasonal integration and cointegration , 1990 .

[3]  Wayne A. Fuller,et al.  Testing for Nonstationary Parameter Specifications in Seasonal Time Series Models , 1982 .

[4]  Alastair R. Hall,et al.  Testing for a Unit Root in Time Series With Pretest Data-Based Model Selection , 1994 .

[5]  Michael P. Clements,et al.  An empirical study of seasonal unit roots in forecasting , 1997 .

[6]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[7]  Michael McAleer,et al.  Review of Microfit 3.0: An Interactive Econometric Software Package , 1993 .

[8]  P. Franses.  Seasonality, Nonstationarity And The Forecasting Of Monthly Time Series , 1991 .

[9]  Jeffrey A. Miron,et al.  Seasonal Unit Roots in Aggregate U.S. Data , 1992 .

[10]  P. Perron,et al.  Trends and random walks in macroeconomic time series : Further evidence from a new approach , 1988 .

[11]  Michael P. Clements,et al.  On the limitations of comparing mean square forecast errors , 1993 .

[12]  Spurious deterministic seasonality , 1995 .

[13]  T. Abeysinghe.  Deterministic seasonal models and spurious regressions , 1994 .

[14]  D. Dickey.  Seasonal unit roots in aggregate U.S. data , 1993 .

[15]  A. Taylor.  Testing for Unit Roots in Monthly Time Series , 1998 .

[16]  B. G. Quinn,et al.  The determination of the order of an autoregression , 1979 .

[17]  Philip Hans Franses,et al.  Periodicity and Stochastic Trends in Economic Time Series , 1996 .

[18]  Helmut Lütkepohl,et al.  Introduction to multiple time series analysis , 1991 .

[19]  Michael P. Clements,et al.  Multi-Step Estimation For Forecasting , 2009 .

[20]  Seasonal Unit-Root Tests on Canadian Macroeconomic Time Series , 1990 .

[21]  J. Miron,et al.  The Economics of Seasonal Cycles , 1990 .

[22]  S. Pantula,et al.  Determining the Order of Differencing in Autoregressive Processes , 1987 .

[23]  C. Nelson,et al.  Trends and random walks in macroeconmic time series: Some evidence and implications , 1982 .

[24]  Lon-Mu Liu,et al.  Joint Estimation of Model Parameters and Outlier Effects in Time Series , 1993 .

[25]  Svend Hylleberg,et al.  Seasonality in macroeconomic time series , 1993 .

[26]  Denise R. Osborn,et al.  A survey of seasonality in UK macroeconomic variables , 1990 .