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Common factors in conditional distributions

Author

Listed:
  • 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.

Suggested Citation

  • Granger, Clive W.J. & Teräsvirta, Timo & Patton, Andrew J., 2002. "Common factors in conditional distributions," SSE/EFI Working Paper Series in Economics and Finance 515, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0515
    as

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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
    2. Olmo, José, 2005. "Contagion versus flight to quality in financial markets," UC3M Working papers. Economics we051810, Universidad Carlos III de Madrid. Departamento de Economía.

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    More about this item

    Keywords

    bivariate time series; business cycles; conditional distribution; consumption-income relationship; copula; multivariate time-series model;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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