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Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method

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  • Amos Golan

    (American University, Berkeley)

  • Jeffrey M. Perloff

    (University of California, Berkeley)

Abstract

We use a nonlinear, nonparametric method to forecast unemployment rates. This method is an extension of the nearest-neighbor method but uses a higher-dimensional simplex approach. We compare these forecasts with several linear and nonlinear parametric methods based on the work of Montgomery et al. (1998) and Carruth et al. (1998). Our main result is that, due to the nonlinearity in the data-generating process, the nonparametric method outperforms many other well-known models, even when these models use more information. This result holds for forecasts based on quarterly and on monthly data. © 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Bibliographic Info

Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 86 (2004)
Issue (Month): 1 (February)
Pages: 433-438

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Handle: RePEc:tpr:restat:v:86:y:2004:i:1:p:433-438

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  1. Carruth,a. & Hooker, N. & Oswald,A., 1997. "Unemployment Equilibria and Input Prices: Theory and Evidence from the United States," Papers, Centre for Economic Performance & Institute of Economics 22, Centre for Economic Performance & Institute of Economics.
  2. Fernandez-Rodriguez, Fernando & Sosvilla-Rivero, Simon, 1998. "Testing nonlinear forecastability in time series: Theory and evidence from the EMS," Economics Letters, Elsevier, Elsevier, vol. 59(1), pages 49-63, April.
  3. Fernandez-Rodriguez, Fernando & Sosvilla-Rivero, Simon & Andrada-Felix, Julian, 1999. "Exchange-rate forecasts with simultaneous nearest-neighbour methods: evidence from the EMS," International Journal of Forecasting, Elsevier, Elsevier, vol. 15(4), pages 383-392, October.
  4. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, Elsevier, vol. 45(1-2), pages 39-70.
  5. Mulhern, Francis J. & Caprara, Robert J., 1994. "A nearest neighbor model for forecasting market response," International Journal of Forecasting, Elsevier, Elsevier, vol. 10(2), pages 191-207, September.
  6. Agnon, Yehuda & Golan, Amos & Shearer, Matthew, 1999. "Nonparametric, nonlinear, short-term forecasting: theory and evidence for nonlinearities in the commodity markets," Economics Letters, Elsevier, Elsevier, vol. 65(3), pages 293-299, December.
  7. Christopher A. Sims, 1989. "A nine variable probabilistic macroeconomic forecasting model," Discussion Paper / Institute for Empirical Macroeconomics, Federal Reserve Bank of Minneapolis 14, Federal Reserve Bank of Minneapolis.
  8. Ramsey James B., 1996. "If Nonlinear Models Cannot Forecast, What Use Are They?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, De Gruyter, vol. 1(2), pages 1-24, July.
  9. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, Econometric Society, vol. 57(2), pages 357-84, March.
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Cited by:
  1. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers, The George Washington University, Department of Economics, Research Program on Forecasting 2010-002, The George Washington University, Department of Economics, Research Program on Forecasting.
  2. Francesco D’Amuri & Juri Marcucci, 2010. "“Google it!”Forecasting the US Unemployment Rate with a Google Job Search index," Working Papers, Fondazione Eni Enrico Mattei 2010.31, Fondazione Eni Enrico Mattei.
  3. Theodore Panagiotidis, 2010. "An Out-of-Sample Test for Nonlinearity in Financial Time Series: An Empirical Application," Working Paper Series, The Rimini Centre for Economic Analysis 20_10, The Rimini Centre for Economic Analysis.
  4. Österholm, Pär, 2009. "Improving Unemployment Rate Forecasts Using Survey Data," Working Paper, National Institute of Economic Research 112, National Institute of Economic Research.
  5. Regis Barnichon & Christopher J. Nekarda, 2013. "The ins and outs of forecasting unemployment: Using labor force flows to forecast the labor market," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2013-19, Board of Governors of the Federal Reserve System (U.S.).
  6. Magnus Gustavsson & Pär Österholm, 2010. "The presence of unemployment hysteresis in the OECD: what can we learn from out-of-sample forecasts?," Empirical Economics, Springer, Springer, vol. 38(3), pages 779-792, June.
  7. Francesco D'Amuri & Juri Marcucci, 2012. "The predictive power of Google searches in forecasting unemployment," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area 891, Bank of Italy, Economic Research and International Relations Area.
  8. Elena Olmedo, 2014. "Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 43(2), pages 183-197, February.
  9. Hutter, Christian & Weber, Enzo, 2013. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," IAB Discussion Paper, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany] 201317, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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