Forecasting U.S. output growth with non-linear models in the presence of data uncertainty
Clements, Michael P.. (2012) Forecasting U.S. output growth with non-linear models in the presence of data uncertainty. Studies in Nonlinear Dynamics & Econometrics, Vol.16 (No.1). p. 2. ISSN 1081-1826
WRAP_Clements_1558-3708.1865.pdf - Published Version
Restricted to Repository staff only until 1 January 2013.
Official URL: http://dx.doi.org/10.1515/1558-3708.1865
We consider the impact of data revisions on the forecast performance of a SETAR regime-switching model of U.S. output growth. The impact of data uncertainty in real-time forecasting will affect a model's forecast performance via the effect on the model parameter estimates as well as via the forecast being conditioned on data measured with error. We find that benchmark revisions do affect the performance of the non-linear model of the growth rate, and that the performance relative to a linear comparator deteriorates in real-time compared to a pseudo out-of-sample forecasting exercise.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HB Economic Theory|
|Divisions:||Faculty of Social Sciences > Economics|
|Library of Congress Subject Headings (LCSH):||Gross national product -- United States -- Econometric models, Economic development -- United States -- Econometric models, Economic forecasting|
|Journal or Publication Title:||Studies in Nonlinear Dynamics & Econometrics|
|Publisher:||Walter de Gruyter GmbH & Co. KG|
|Page Range:||p. 2|
|Access rights to Published version:||Restricted or Subscription Access|
|References:||Aruoba, S. B. (2008): “Data revisions are not well-behaved,” Journal of Money, Credit and Banking, 40, 319–340. Box, G. E. P. and G. M. Jenkins (1970): Time Series Analysis, Forecasting and Control, San Francisco: Holden-Day. Clements, M. P. and A. B. Galvão (2008): “Macroeconomic forecasting with mixed-frequency data: Forecasting output growth in the United States,” Journal of Business and Economic Statistics, 26, 546–554, no. 4. Clements, M. P. and A. B. Galvão (2009): “Forecasting US output growth using leading indicators: An appraisal using MIDAS models,” Journal of Applied Econometrics, 24, 1187–1206. Clements, M. P. and A. B. Galvão (2010): “Real-time forecasting of inflation and output growth in the presence of data revisions,” Warwick economics research paper, no. 953, Department of Economics, University of Warwick. Clements, M. P. and D. F. Hendry (1999): Forecasting Non-Stationary Economic Time Series, Cambridge, Mass.: MIT Press, the Zeuthen Lectures on Economic Forecasting. Clements, M. P. and D. F. Hendry (2006): “Forecasting with breaks,” in G. Elliott, C. Granger, and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 1. Handbook of Economics 24, Elsevier, Horth-Holland, 605–657. Clements, M. P. and H.-M. Krolzig (1998): “A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP,” Econometrics Journal, 1, C47–75. Clements, M. P. and H.-M. Krolzig (2003): “Business cycle asymmetries: Characterisation and testing based on Markov-switching autoregressions,” Journal of Business and Economic Statistics, 21, 196–211. Clements, M. P. and J. Smith (1997): “The performance of alternative forecasting methods for SETAR models,” International Journal of Forecasting, 13, 463–475. Clements, M. P. and J. Smith (1999): “A Monte Carlo study of the forecasting performance of empirical SETAR models,” Journal of Applied Econometrics, 14, 124–141. Croushore, D. (2006): “Forecasting with real-time macroeconomic data,” in G. Elliott, C. Granger, and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 1. Handbook of Economics 24, Elsevier, Horth-Holland, 961–982. Croushore, D. and T. Stark (2001): “A real-time data set for macroeconomists,” Journal of Econometrics, 105, 111–130. Croushore, D. and T. Stark (2003): “A real-time data set for macroeconomists: Does the data vintage matter?” The Review of Economics and Statistics, 85, 605–617. Cunningham, A., J. Eklund, C. Jeffery, G. Kapetanios, and V. Labhard (2009): “A state space approach to extracting the signal from uncertain data,” Journal of Business & Economic Statistics. Diebold, F. X. and J. A. Nason (1990): “Nonparametric exchange rate prediction,” Journal of International Economics, 28, 315–332. Diebold, F. X. and G. D. Rudebusch (1991): “Forecasting output with the composite leading index: A real-time analysis,” Journal of the American Statistical Association, 86, 603–610. Faust, J., J. H. Rogers, and J. H. Wright (2003): “Exchange rate forecasting: The errors we’ve really made,” Journal of International Economic Review, 60, 35–39. Fixler, D. J. and B. T. Grimm (2005): “Reliability of the NIPA estimates of U.S. economic activity,” Survey of Current Business, 85, 9–19. Fixler, D. J. and B. T. Grimm (2008): “The reliability of the GDP and GDI estimates,” Survey of Current Business, 88, 16–32. Franses, P. H. and D. van Dijk (2000): Non-linear time series models in empirical finance, Cambridge: Cambridge University Press. Granger, C. W. J. and T. Teräsvirta (1993): Modelling Nonlinear Economic Relationships, Oxford: Oxford University Press. Hall, R. E. (1978): “Stochastic implications of the life cycle-permanent income hypothesis: Evidence,” Journal of Political Economy, 86, 971–987. Hamilton, J. D. (1989): “A new approach to the economic analysis of nonstationary time series and the business cycle,” Econometrica, 57, 357–384. Harrison, R., G. Kapetanios, and T. Yates (2005): “Forecasting with measurement errors in dynamic models,” International Journal of Forecasting, 21, 595–607. Howrey, E. P. (1978): “The use of preliminary data in economic forecasting,” The Review of Economics and Statistics, 60, 193–201. Howrey, E. P. (1984): “Data revisions, reconstruction and prediction: an application to inventory investment,” The Review of Economics and Statistics, 66, 386–393. Jacobs, J. P. A. M. and S. van Norden (2007): “Modeling data revisions: Measurement error and dynamics of ‘true’ values,” Technical report cref 07-09, HEC, Montreal. Kishor, N. K. and E. F. Koenig (2010): “VAR estimation and forecasting when data are subject to revision,” Journal of Business and Economic Statistics, forthcoming. Koenig, E. F., S. Dolmas, and J. Piger (2003): “The use and abuse of real-time data in economic forecasting,” The Review of Economics and Statistics, 85(3), 618–628. Landefeld, J. S., E. P. Seskin, and B. M. Fraumeni (2008): “Taking the pulse of the economy,” Journal of Economic Perspectives, 22, 193–216. Mankiw, N. G. and M. D. Shapiro (1986): “News or noise: An analysis of GNP revisions,” Survey of Current Business (May 1986), US Department of Commerce, Bureau of Economic Analysis, 20–25. Orphanides, A. (2001): “Monetary policy rules based on real-time data,” American Economic Review, 91(4), 964–985. Orphanides, A. and S. van Norden (2005): “The reliability of inflation forecasts based on output gaps in real time,” Journal of Money, Credit and Banking, 37, 583–601. Potter, S. (1995): “A nonlinear approach to US GNP,” Journal of Applied Econometrics, 10, 109–125. Runkle, D. E. (1998): “Revisionist history: How data revisions distort economic policy research,” Federal Reserve Bank of Minneapolis Quarterly Review, 22, 3–12, no. 4. Sargent, T. J. (1989): “Two models of measurements and the investment accelerator,” Journal of Political Economy, 97, 251–287. Siklos, P. L. (2008): “What can we learn from comprehensive data revisions for forecasting inflation: Some US evidence,” in D. E. Rapach and M. E. Wohar, eds., Forecasting in the Presence of Structural Breaks and Model Uncertainty. Frontiers of Economics and Globalization. Volume 3, Emerald, 271–299. Stock, J. H. and M.W.Watson (1996): “Evidence on structural instability in macroeconomic time series relations,” Journal of Business and Economic Statistics, 14, 11–30. Teräsvirta, T. and H. M. Anderson (1992): “Characterizing nonlinearities in business cycles using smooth transition autoregressive models,” Journal of Applied Econometrics, 7, 119–139. Tiao, G. C. and R. S. Tsay (1994): “Some advances in non-linear and adaptive modelling in time-series,” Journal of Forecasting, 13, 109–131. Tong, H. (1978): “On a threshold model,” in C. H. Chen, ed., Pattern Recognition and Signal Processing, Amsterdam: Sijhoff and Noordoff, 101–141. Tong, H. (1983): Threshold Models in Non-Linear Time Series Analysis, Springer- Verlag, New York. Tong, H. (1995): Non-linear Time Series. A Dynamical System Approach, Oxford: Clarendon Press, first published 1990. Tong, H. and K. S. Lim (1980): “Threshold autoregression, limit cycles and cyclical data,” Journal of The Royal Statistical Society, B 42, 245–292. West, K. D. (2006): “Forecasting evaluation,” in G. Elliott, C. Granger, and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 1. Handbook of Economics 24, Elsevier, Horth-Holland, 99–134.|
Actions (login required)