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Common factors in conditional distributions

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Author Info

  • 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)

Abstract

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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Bibliographic Info

Paper provided by Stockholm School of Economics in its series 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

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Keywords: bivariate time series; business cycles; conditional distribution; consumption-income relationship; copula; multivariate time-series model;

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References

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  1. Hansen, B.E., 1992. "Autoregressive Conditional Density Estimation," RCER Working Papers 322, University of Rochester - Center for Economic Research (RCER).
  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. 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.
  4. 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.
  5. 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.
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Cited by:
  1. Xiaohong Chen & Yanqin Fan & Victor Tsyrennifov, 2004. "Efficient Estimation of Semiparametric Multivariate Copula Models," Vanderbilt University Department of Economics Working Papers 0420, Vanderbilt University Department of Economics.
  2. Yanqin Fan & Xiaohong Chen, 2004. "Estimation of Copula-Based Semiparametric Time Series Models," Econometric Society 2004 Far Eastern Meetings 559, Econometric Society.
  3. Xiaohong Chen & Yanqin Fan, 2004. "Estimation and Model Selection of Semiparametric Copula-Based Multivariate Dynamic Models under Copula Misspecification," Vanderbilt University Department of Economics Working Papers 0419, Vanderbilt University Department of Economics, revised Sep 2004.
  4. Jesus Gonzalo & Jose Olmo, 2005. "Contagion Versus Flight To Quality In Financial Markets," Economics Working Papers we051810, Universidad Carlos III, Departamento de Economía.
  5. Xiaohong Chen & Yanqin Fan, 2002. "Estimation of Copula-Based Semiparametric Time Series Models," Vanderbilt University Department of Economics Working Papers 0226, Vanderbilt University Department of Economics, revised Oct 2004.

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