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On the predictive content of nonlinear transformations of lagged autoregression residuals and time series observations

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  • Rossen, Anja

Abstract

This study focuses on the question whether nonlinear transformation of lagged time series values and residuals are able to systematically improve the average forecasting performance of simple Autoregressive models. Furthermore it investigates the potential superior forecasting results of a nonlinear Threshold model. For this reason, a large-scale comparison over almost 400 time series which span from 1996:3 up to 2008:12 (production indices, price indices, unemployment rates, exchange rates, money supply) from 10 European countries is made. The average forecasting performance is appraised by means of Mean Group statistics and simple t-tests. Autoregressive models are extended by transformed first lags of residuals and time series values. Whereas additional transformation of lagged time series values are able to reduce the ex-ante forecast uncertainty and provide a better directional accuracy, transformations of lagged residuals also lead to smaller forecast errors. Furthermore, the nonlinear Threshold model is able to capture certain type of economic behavior in the data and provides superior forecasting results than a simple Autoregressive model. These findings are widely independent of considered economic variables. --

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Paper provided by Hamburg Institute of International Economics (HWWI) in its series HWWI Research Papers with number 113.

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Date of creation: 2011
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Handle: RePEc:zbw:hwwirp:113

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Keywords: Time series modeling; forecasting comparison; nonlinear transformations; Threshold Autoregressive modeling; average forecasting performance;

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  1. Hansen, Bruce E, 1999. " Testing for Linearity," Journal of Economic Surveys, Wiley Blackwell, vol. 13(5), pages 551-76, December.
  2. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
  3. Clements, Michael P & Smith, Jeremy, 1996. "A Monte Carlo Study of the Forecasting Performance of Empirical Setar Models," The Warwick Economics Research Paper Series (TWERPS) 464, University of Warwick, Department of Economics.
  4. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
  5. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July.
  6. Allan Timmermann & M. Hashem Pesaran, 2003. "How Costly is it to Ignore Breaks when Forecasting the Direction of a Time Series?," CESifo Working Paper Series 875, CESifo Group Munich.
  7. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
  8. Helmut Luetkepohl & Fang Xu, 2009. "The Role of the Log Transformation in Forecasting Economic Variables," CESifo Working Paper Series 2591, CESifo Group Munich.
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Cited by:
  1. Hillmann, Katja & Hohenleitner, Ingrid, 2012. "Impact of benefit sanctions on unemployment outflow: Evidence from German survey data," HWWI Research Papers 129 [rev.], Hamburg Institute of International Economics (HWWI).

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