Bruno, Giancarlo (2009): Non-linear relation between industrial production and business surveys data.
Download (263kB) | Preview
n this paper I compare different models, a linear and a non-linear one, for forecasting industrial production by means of some related indicators. I claim that the difficulties associated with the correct identification of a non-linear model could be a possible cause of the often observed worse performance of non-linear models with respect to linear ones observed in the empirical literature. To cope with this issue I use a non-linear non-parametric model. The results are promising, as the forecasting performance shows a clear improvement over the linear parametric model.
|Item Type:||MPRA Paper|
|Original Title:||Non-linear relation between industrial production and business surveys data|
|Keywords:||Forecasting; Business Surveys; Non-linear time-series models; Non-parametric models|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
|Depositing User:||Giancarlo Bruno|
|Date Deposited:||01. Nov 2012 07:42|
|Last Modified:||19. Feb 2013 11:40|
Bradley, M. D. and Jansen, D. W. (2004). Forecasting with a nonlinear dynamic model of stock returns and industrial production. International Journal of Forecasting, 20(2):321–342.
Bruno, G. and Lupi, C. (2004). Forecasting industrial production and the early detection of turning points. Empirical Economics, 29(3):647–671.
Cai, Z., Fan, J., and Li, R. (2000a). Efficient estimation and inferences for varying-coefficient models. Journal of the American Statistical Association, 95(451):888–902.
Cai, Z., Fan, J., and Yao, Q. (2000b). Functional-coefficient regression models for nonlinear time series. Journal of the American Statistical Association, 5(451):941– 956.
Chen, R. and Tsay, R. S. (1993). Functional-coefficient autoregressive models. Journal of the American Statistical Association, 88(421):298–308.
Diebold, F. and Mariano, R. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13:253–263.
Fan, J. and Zhang, W. (2000). Simultaneous confidence bands and hypothesis testing in varying-coefficient models. Scandinavian Journal of Statistics, 27(4):715–731.
Franses, P. H. and van Dijk, D. (2005). The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production. International Journal of Forecasting, 21(1):87–102.
Garcia-Ferrer, A. and Bujosa-Brun, M. (2000). Forecasting oecd industrial turning points using unobserved components models with business survey data. International Journal of Forecasting, 16(2):207–227.
Harvey, D. I., Leybourne, S. J., and Newbold, P. (1997). Tests the equality of prediction mean squared error. Internation Journal of Forecasting, 13:281–291.
Harvey, D. I., Leybourne, S. J., and Newbold, P. (1998). Tests for forecast encompassing. Journal of Business and Economic Statistics, 16:254–259.
Harvill, J. L. and Ray, B. K. (2005). A note on multi-step forecasting with functional coefficient autoregressive models. International Journal of Forecasting, 21:717–727.
Huh, C. (1998). Forecasting industrial production using models with business cycle asymmetry. Economic Review, (1):29–41.
Jagric, T. (2003). A nonlinear approach to forecasting with leading economic indicators. Studies in Nonlinear Dynamics & Econometrics, 7(2):1135–1135.
Marchetti, D. J. and Parigi, G. (2000). Energy consumption, survey data and the prediction of industrial production in Italy: a comparison and combination of different models. Journal of Forecasting, 19(5):419–440.
Marques, C. R. (2004). Inflation persistence - facts or artefacts? Working Paper Series 371, European Central Bank.
Öcal, N. (2000). Nonlinear models for U.K. macroeconomic time series. Studies in Nonlinear Dynamics & Econometrics, 4(3):123–135.
Osborn, D. R. and Matas-Mir, A. (2003). The extent of seasonal/business cycle interactions in European industrial production. Discussion Paper 38, The University of Manchester.
Pappalardo, C. (1998). La stagionalità nelle serie ISCO. Rassegna di lavori dell’ISCO 3, ISCO.
Priestley, M. B. (1980). State-dependent models: A general approach to nonlinear time series analysis. Journal of Time Series Analysis, 1:47–71.
Proietti, T. (1998). Seasonal heteroscedasticity and trends. Journal of Forecasting, 17:1–17.
Proietti, T. and Frale, C. (2007). New proposals for the quantification of qualitative survey data. CEIS Research Paper 98, Tor Vergata University, CEIS.
Siliverstovs, B. and Dijk, D. V. (2003). Forecasting industrial production with linear, nonlinear and structural change models. Econometric Institute Report 321, Erasmus University Rotterdam, Econometric Institute.
Simpson, P. W., Osborn, D. R., and Sensier, M. (2001). Forecasting UK industrial production over the business cycle. Journal of Forecasting, 20(6):405–24.
Teräsvirta, T. (2006). Forecasting economic variables with nonlinear models. In Elliot, G., Granger, C. W., and Timmermann, A., editors, Handbook of Economic Forecasting, volume I, chapter 8, pages 413–57. Elsevier.
Teräsvirta, T., Lin, C., and Granger, C. (1993). Power of the neural network linearity test. Journal of Time Series Analysis, 14:209–220.
Tschernig, R. (2004). Nonparametric Time Series Modeling. Cambridge University Press, Cambridge.
Venetis, I. A., Peel, D. A., and Paya, I. (2004). Asymmetry in the link between the yield spread and industrial production: threshold effects and forecasting. Journal of Forecasting, 23(5):373–384.
Xia, Y. and Li, W. K. (1999). On the estimation and testing of functional-coefficient linear models. Statistica Sinica, 9:735–757.