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Can Sectoral Shifts Generate Persistent Unemployment in Real Business Cycle Models?

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Author Info
Ossama Mikhail
Curtis J. Eberwein
Jagdish Handa

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Abstract

This paper extends the standard Real Business Cycle model to incorporate sectoral shifts in unemployment. Using relative sectoral technology and sectoral tastes shocks, combined with labor adjustment costs across sectors, we assess the possibility of generating persistent aggregate unemployment. Calibrated to Canadian data, the models suggest that the introduction of sectoral labor mobility with adjustment costs improves the ability of the standard real business cycle model to match the observed persistence in unemployment. Empirically, we estimated a Vector Auto-Regressive model and successfully matched the models' overshooting of labor. The results suggest that government policies aimed to alleviate the unemployment burden should pay closer attention to sectoral phenomena, specifically to sectoral labor mobility.

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Paper provided by EconWPA in its series Macroeconomics with number 0311004.

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Date of creation: 11 Nov 2003
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Handle: RePEc:wpa:wuwpma:0311004

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Related research
Keywords: Real Business Cycle (RBC) Sectoral Shocks Unemployment Persistence Vector Auto-Regressive (VAR) Blanchard-Quah (B-Q) Identification

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Find related papers by JEL classification:
E24 - Macroeconomics and Monetary Economics - - Macroeconomics: Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution
E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
J61 - Labor and Demographic Economics - - Mobility, Unemployment, and Vacancies - - - Geographic Labor Mobility; Immigrant Workers
C61 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Optimization Techniques; Programming Models; Dynamic Analysis
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

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  1. Runkle, David E, 1987. "Vector Autoregressions and Reality," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 437-42, October.
  2. Runkle, David E, 1987. "Vector Autoregressions and Reality: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 454, October.
  3. Jonathan H. Wright, 2000. "Exact confidence intervals for impulse responses in a Gaussian vector autoregression," International Finance Discussion Papers 682, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  4. David E. Runkle, 1987. "Vector autoregressions and reality," Staff Report 107, Federal Reserve Bank of Minneapolis. [Downloadable!]
  5. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-44, January. [Downloadable!] (restricted)
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