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Dynamic Specification Tests for Static Factor Models

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  • Gabriele Fiorentini

    (RCEA and Università di Firenze, Italy)

  • Enrique Sentana

    (CEMFI, Madrid, Spain)

Abstract

We derive computationally simple score tests of serial correlation in the levels and squares of common and idiosyncratic factors in static factor models. The implicit orthogonality conditions resemble the orthogonality conditions of models with observed factors but the weighting matrices reflect their unobservability. We derive more powerful tests for elliptically symmetric distributions, which can be either parametrically or semiparametrically specified, and robustify the Gaussian tests against general non-normality. Our Monte Carlo exercises assess the finite sample reliability and power of our proposed tests, and compare them to other existing procedures. Finally, we apply our methods to monthly US stock returns.

Suggested Citation

  • Gabriele Fiorentini & Enrique Sentana, 2010. "Dynamic Specification Tests for Static Factor Models," Working Paper series 04_10, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:04_10
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    2. Bouakez, Hafedh & Chihi, Foued & Normandin, Michel, 2014. "Fiscal policy and external adjustment: New evidence," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 1-20.
    3. Hafedh Bouakez & Denis Larocque & Michel Normandin, 2018. "Separating the wheat from the chaff: A disaggregate analysis of the effects of public spending in the US," Canadian Journal of Economics, Canadian Economics Association, vol. 51(2), pages 361-390, May.
    4. Bouakez, Hafedh & Essid, Badye & Normandin, Michel, 2013. "Stock returns and monetary policy: Are there any ties?," Journal of Macroeconomics, Elsevier, vol. 36(C), pages 33-50.
    5. Gabriele Fiorentini & Enrique Sentana, 2012. "Tests for Serial Dependence in Static, Non-Gaussian Factor Models," Working Papers wp2012_1211, CEMFI.

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    More about this item

    Keywords

    ARCH; Financial returns; Kalman filter; LM tests; Predictability;
    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
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions

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