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Common factors in conditional distributions

  • Granger, Clive W.J.


    (Department of Economics, University of California, San Diego)

  • Teräsvirta, Timo


    (Dept. of Economic Statistics, Stockholm School of Economics)

  • Patton, Andrew J.


    (Department of Economics, London School of Economics)

The concept of common factors has in the econometrics literature been applied to conditional means or in some cases to conditional variances. In this paper we generalize this concept to bivariate distributions. This is done using the conditional bivariate copula as the statistical tool. The definition of common factors in distributions is illustrated by an empirical application to the income-consumption relationship, using monthly US time series. Evidence is found to support the claim that the true relationship between these variables is independent of the phase of the business cycle. The indicator representing the business cycle is thus a common factor in distributions of the type defined and discussed in the paper.

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Paper provided by Stockholm School of Economics in its series SSE/EFI Working Paper Series in Economics and Finance with number 515.

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Length: 12 pages
Date of creation: 20 Nov 2002
Date of revision:
Publication status: Published in Journal of Econometrics, 2006, pages 43-57.
Handle: RePEc:hhs:hastef:0515
Contact details of provider: Postal: The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden
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  1. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  2. Issler, Joao Victor & Vahid, Farshid, 2001. "Common cycles and the importance of transitory shocks to macroeconomic aggregates," Journal of Monetary Economics, Elsevier, vol. 47(3), pages 449-475, June.
  3. Hansen, B.E., 1992. "Autoregressive Conditional Density Estimation," RCER Working Papers 322, University of Rochester - Center for Economic Research (RCER).
  4. Patton, Andrew J, 2001. "Modelling Time-Varying Exchange Rate Dependence Using the Conditional Copula," University of California at San Diego, Economics Working Paper Series qt01q7j1s2, Department of Economics, UC San Diego.
  5. Patton, Andrew J, 2001. "Estimation of Copula Models for Time Series of Possibly Different Length," University of California at San Diego, Economics Working Paper Series qt3fc1c8hw, Department of Economics, UC San Diego.
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