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

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Author Info
Amos Golan (American University)
Jeffrey Perloff (University of California, Berkeley, and Giannini Foundation)

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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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Publisher Info
Paper provided by Department of Agricultural & Resource Economics, UC Berkeley in its series Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series with number 956.

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Date of creation: 01 Jan 2002
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Handle: RePEc:cdl:agrebk:956

Note: oai:cdlib1:are_ucb-1034
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Related research
Keywords: embedding dimension; nonlinearity; nonparametric; unemployment rate;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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. [Downloadable!] (restricted)
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  2. 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. [Downloadable!] (restricted)
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  3. 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. [Downloadable!] (restricted)
  4. 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. [Downloadable!] (restricted)
  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. [Downloadable!] (restricted)
  6. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70. [Downloadable!] (restricted)
  7. Christopher A. Sims, 1992. "A Nine Variable Probabilistic Macroeconomic Forecasting Model," Cowles Foundation Discussion Papers 1034, Cowles Foundation, Yale University. [Downloadable!]
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  8. 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. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Österholm, Pär, 2009. "Improving Unemployment Rate Forecasts Using Survey Data," Working Paper 112, National Institute of Economic Research. [Downloadable!]
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