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Dynamics of Unemployment Insurance Claims: An Application of ARIMA-GARCH Models

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  • Hassan Mohammadi
  • Daniel Rich

Abstract

Time-series analysis of weekly initial claims over 1967–2012 reveal the following: (1) Initial claims are highly seasonal and cyclical, but do not follow a specific trend. Seasonality follows a “W” pattern over the 52 week period. (2) Initial claims are subject to conditional volatility and volatility clustering. The EGARCH and CGARCH specifications provide reasonable representations of the conditional volatility. The former suggests the existence of asymmetries in conditional variance. The latter implies that both permanent and transitory shocks affect volatility, but the effect of permanent shocks is more pronounced. (3) Both models perform well in terms of forecasting as well as within- and out-of-sample model selection criteria. Copyright International Atlantic Economic Society 2013

Suggested Citation

  • Hassan Mohammadi & Daniel Rich, 2013. "Dynamics of Unemployment Insurance Claims: An Application of ARIMA-GARCH Models," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 41(4), pages 413-425, December.
    • Handle: RePEc:kap:atlecj:v:41:y:2013:i:4:p:413-425
    DOI: 10.1007/s11293-013-9393-z
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    References listed on IDEAS

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    1. repec:mpr:mprres:5070 is not listed on IDEAS
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    4. James H. Stock & Mark W. Watson, 1990. "Business Cycle Properties of Selected U.S. Economic Time Series, 1959-1988," NBER Working Papers 3376, National Bureau of Economic Research, Inc.
    5. Kevin L. Kliesen & David C. Wheelock, 2012. "How well do initial claims forecast employment growth over the business cycle and over time?," Economic Synopses, Federal Reserve Bank of St. Louis.
    6. William T. Gavin & Kevin L. Kliesen, 2002. "Unemployment insurance claims and economic activity," Review, Federal Reserve Bank of St. Louis, vol. 84(May), pages 15-28.
    7. Stock, James H. & Watson, Mark W., 1999. "Business cycle fluctuations in us macroeconomic time series," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 1, pages 3-64, Elsevier.
    8. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    9. Gianluca Cubadda, 2007. "A Reduced Rank Regression Approach to Coincident and Leading Indexes Building," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(2), pages 271-292, April.
    10. Walter Nicholson & Karen Needels, "undated". "Unemployment Insurance: Strengthening the Relationship Between Theory and Policy," Mathematica Policy Research Reports 062881e2c70d4513829b70fee, Mathematica Policy Research.
    11. Walter Nicholson & Karen Needels, 2006. "Unemployment Insurance: Strengthening the Relationship between Theory and Policy," Journal of Economic Perspectives, American Economic Association, vol. 20(3), pages 47-70, Summer.
    12. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
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    Cited by:

    1. Viktor Stojkoski & Petar Jolakoski & Igor Ivanovski, 2021. "The short‐run impact of COVID‐19 on the activity in the insurance industry in the Republic of North Macedonia," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 24(3), pages 221-242, September.

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    More about this item

    Keywords

    Weekly initial claims; Conditional mean; Conditional variance; GARCH; C10; J21;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

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