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Superior forecasts of the U.S. unemployment rate using a nonparametric method

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  • Golan, Amos
  • Perloff, Jeffrey M

    ()
    (University of California, Berkeley. Dept of agricultural and resource economics and policy)

Abstract

We use a nonlinear, nonparametric method to forecast the unemployment rates. We compare these forecasts to 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 nonlin-earity 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.

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Bibliographic Info

Paper provided by University of California at Berkeley, Department of Agricultural and Resource Economics and Policy in its series CUDARE Working Paper Series with number 956.

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Length: 24 pages
Date of creation: 2002
Date of revision:
Handle: RePEc:are:cudare:956

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Postal: University of California, Giannini Foundation of Agricultural Economics Library, 248 Giannini Hall #3310, Berkeley CA 94720-3310
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Related research

Keywords: forecasting; mathematical models; nonlinear models; unemployment;

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References

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  1. 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.
  2. Christopher A. Sims, 1992. "A Nine Variable Probabilistic Macroeconomic Forecasting Model," Cowles Foundation Discussion Papers 1034, Cowles Foundation for Research in Economics, Yale University.
  3. Carruth,a. & Hooker, N. & Oswald,A., 1997. "Unemployment Equilibria and Input Prices: Theory and Evidence from the United States," Papers 22, Centre for Economic Performance & Institute of Economics.
  4. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
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Citations

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Cited by:
  1. Theodore Panagiotidis, 2010. "An out-of-sample test for nonlinearity in financial time series: An empirical application," Discussion Paper Series 2010_08, Department of Economics, University of Macedonia, revised Jun 2010.
  2. 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.
  3. repec:ese:iserwp:2009-32 is not listed on IDEAS
  4. 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.
  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 2013-19, Board of Governors of the Federal Reserve System (U.S.).
  6. 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].
  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. D'Amuri, Francesco/FD & Marcucci, Juri/JM, 2009. ""Google it!" Forecasting the US unemployment rate with a Google job search index," MPRA Paper 18248, University Library of Munich, Germany.
  9. 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.
  10. 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.

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