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Abstract
Industrial production series are volatile and often cyclical. Time series models can be used to establish certain stylized facts, such as trends and cycles, which may be present in these series. In certain situations, it is also possible that common factors, which may have an interesting interpretation, can be detected in production series. Series from two neighboring countries with close economic relationships, such as Germany and Austria, are especially likely to exhibit such joint stylized facts.
Keywords
Industrial production
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multiple structural time series modeling
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common factors
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Gerhard Thury.
Industrial production in Germany and Austria: A case study in structural time series modelling.
Journal of Systems Science and Systems Engineering, 2003, 12(2): 159-170 DOI:10.1007/s11518-006-0127-5
| [1] |
Bowman K.O., Shenton L.R.. Omnibus test contours for departures from normality based √b i and b i. Biometrika, 1975, 62: 243-250.
|
| [2] |
Box G.E.P., Tiao G.C.. A canonical analysis of multiple time series. Biometrika, 1977, 64: 355-365.
|
| [3] |
Brillinger D.R.. Time Series: Data Analysis and Theory, 1975, New York: Holt
|
| [4] |
Engle R.F., Kozicki S.. Testing for common features (with discussion). Journal of Business and Economic Statistics, 1993, 11: 369-395.
|
| [5] |
Garcia-Ferrer A., del Hoyo J.. On trend extraction models: interpretation, empirical evidence and forecasting performance. Journal of Forecasting, 1992, 11: 645-665.
|
| [6] |
Geweke J.. Aigner D.J., Goldberger A.S.. The dynamic factor analysis of economic time series models. Latent Variables in Socio-Economic Models, 1977, New York: North Holland
|
| [7] |
Geweke J., Singleton K.. Maximum likelihood confirmatory factor analysis of economic time series. International Economic Review, 1981, 22(1): 37-54.
|
| [8] |
Hannan E.J.. The identification of vector mixed autoregressive moving average systems. Biometrika, 1969, 56: 223-225.
|
| [9] |
Harvey A.C.. Forecasting, Structural Time Series Models and the Kalman Filter, 1989, Cambridge: Cambridge University Press
|
| [10] |
Harvey A.C., Todd P.H.J.. Forecasting economic time series with structural and Box-Jenkins models: a case study. Journal of Business and Economic Statistics, 1983, 1(4): 299-307.
|
| [11] |
Koopman S.J., Harvey A.C., Doornik J.A., Shephard N.. Stamp 5.0. Structural Time Series Analyser, Modeller and Predictor, 1995, London: Chapman & Hall
|
| [12] |
Sims C.A.. Kmenta J., Ramsey J.B.. An autoregressive index model for the US, 1948–1975. Large Scale Macro-Econometric Models, 1981, Amsterdam: North Holland
|
| [13] |
Thury G., Witt S.F.. Forecasting industrial production using structural time series models”, Omega. The International Journal of Management Science, 1998, 26(6): 751-767.
|
| [14] |
Velu R.P., Reinsel G.C., Wichern D.W.. Reduced rank models for multiple time series. Biometrika, 1986, 73: 105-118.
|
| [15] |
Zellner A.. Estimation of seemingly unrelated regression equations: some exact finite sample results. Journal of the American Statistical Association, 1963, 58: 977-992.
|