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Identification by Laplace transforms in nonlinear time series and panel models with unobserved stochastic dynamic effects

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  • Gagliardini, Patrick
  • Gouriéroux, Christian

Abstract

We consider nonlinear parametric and semi-parametric models for time series and panel data including unobserved dynamic effects. These regression models have an affine specification with respect to lagged endogenous variables and unobserved dynamic effects. We derive conditional moment restrictions based on suitable Laplace transforms. We show how to deploy these nonlinear moment restrictions to identify the parameters of the affine regression model, and the parametric or nonparametric distribution of the unobserved effects. This approach is appropriate for studying identification in (nonlinear) latent factor models encountered in macroeconomic and financial applications as well as in panel models with stochastic time effects.

Suggested Citation

  • Gagliardini, Patrick & Gouriéroux, Christian, 2019. "Identification by Laplace transforms in nonlinear time series and panel models with unobserved stochastic dynamic effects," Journal of Econometrics, Elsevier, vol. 208(2), pages 613-637.
    • Handle: RePEc:eee:econom:v:208:y:2019:i:2:p:613-637
    DOI: 10.1016/j.jeconom.2018.01.012
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    References listed on IDEAS

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    1. Serge Darolles & Christian Gourieroux, 2015. "Contagion phenomena with applications in finance," Post-Print hal-02571861, HAL.
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    Cited by:

    1. Dalderop, Jeroen, 2023. "Semiparametric estimation of latent variable asset pricing models," Journal of Econometrics, Elsevier, vol. 236(1).

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    More about this item

    Keywords

    Semi-parametric identification; Nonlinear factor model; Conditional moment restrictions; Cross-differencing; Count panel data;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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