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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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Web page: http://mitpress.mit.edu/journals/

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  1. Mulhern, Francis J. & Caprara, Robert J., 1994. "A nearest neighbor model for forecasting market response," International Journal of Forecasting, Elsevier, vol. 10(2), pages 191-207, September.
  2. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  3. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
  4. Ramsey James B., 1996. "If Nonlinear Models Cannot Forecast, What Use Are They?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(2), pages 1-24, July.
  5. Agnon, Yehuda & Golan, Amos & Shearer, Matthew, 1999. "Nonparametric, nonlinear, short-term forecasting: theory and evidence for nonlinearities in the commodity markets," Economics Letters, Elsevier, vol. 65(3), pages 293-299, December.
  6. 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, vol. 15(4), pages 383-392, October.
  7. Christopher A. Sims, 1992. "A Nine Variable Probabilistic Macroeconomic Forecasting Model," Cowles Foundation Discussion Papers 1034, Cowles Foundation for Research in Economics, Yale University.
  8. Alan A. Carruth & Mark A. Hooker & Andrew J. Oswald, 1998. "Unemployment Equilibria And Input Prices: Theory And Evidence From The United States," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 621-628, November.
  9. Fernandez-Rodriguez, Fernando & Sosvilla-Rivero, Simon, 1998. "Testing nonlinear forecastability in time series: Theory and evidence from the EMS," Economics Letters, Elsevier, vol. 59(1), pages 49-63, April.
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Cited by:
  1. 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, vol. 38(3), pages 779-792, June.
  2. repec:ese:iserwp:2009-32 is not listed on IDEAS
  3. Francesco D’Amuri & Juri Marcucci, 2010. "“Google it!”Forecasting the US Unemployment Rate with a Google Job Search index," Working Papers 2010.31, Fondazione Eni Enrico Mattei.
  4. Hutter, Christian & Weber, Enzo, 2013. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," IAB Discussion Paper 201317, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  5. Theodore Panagiotidis, 2010. "An Out-of-Sample Test for Nonlinearity in Financial Time Series: An Empirical Application," Computational Economics, Society for Computational Economics, vol. 36(2), pages 121-132, August.
  6. Elena Olmedo, 2014. "Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques," Computational Economics, Society for Computational Economics, vol. 43(2), pages 183-197, February.
  7. Pär Österholm, 2010. "Improving Unemployment Rate Forecasts Using Survey Data," Finnish Economic Papers, Finnish Economic Association, vol. 23(1), pages 16-26, Spring.
  8. 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 2013-19, Board of Governors of the Federal Reserve System (U.S.).
  9. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers 2010-002, The George Washington University, Department of Economics, Research Program on Forecasting.
  10. Francesco D'Amuri & Juri Marcucci, 2012. "The predictive power of Google searches in forecasting unemployment," Temi di discussione (Economic working papers) 891, Bank of Italy, Economic Research and International Relations Area.

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