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  • 1.
    Forchini, Giovanni
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Jiang, Bin
    Fragility of identification in panel binary response models2019In: Econometrics Journal, ISSN 1368-4221, E-ISSN 1368-423X, Vol. 22, no 3, p. 282-291Article in journal (Refereed)
    Abstract [en]

    The present paper considers a linear binary response model for panel data with random effects that differ across individuals but are constant over time, and it investigates the roles of the various assumptions that are used to establish conditions for identification. The paper also shows that even for this simple model, it is always possible-including in the logistic case-to find a distribution of the random effects given the exogenous variables, such that the slopes' parameters are arbitrarily different, but the joint distributions of the binary response variables are arbitrarily close.

  • 2.
    Forchini, Giovanni
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Jiang, Bin
    The unconditional distributions of the OLS, TSLS and LIML estimators in a simple structural equations model2019In: Econometric Reviews, ISSN 0747-4938, E-ISSN 1532-4168, Vol. 38, no 2, p. 208-247Article in journal (Refereed)
    Abstract [en]

    The exact distributions of the standard estimators of the structural coefficients in a linear structural equations model conditional on the exogenous variables have been shown to have some unexpected and quirky features. Since the argument for conditioning on exogenous (ancillary) variables has been weakened over the past 20 years by the discovery of an “ancillarity paradox,” it is natural to wonder whether such finite sample properties are in fact due to conditioning on the exogenous variables. This article studies the exact distributions of the ordinary least squares (OLS), two-stage least squares (TSLS), and limited information maximum likelihood (LIML) estimators of the structural coefficients in a linear structural equation without conditioning on the exogenous variables.

  • 3.
    Forchini, Giovanni
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Jiang, Bin
    Peng, Bin
    TSLS and LIML Estimators in Panels with Unobserved Shocks2018In: Econometrics, ISSN 2225-1146, Vol. 6, no 2, article id 19Article in journal (Refereed)
    Abstract [en]

    The properties of the two stage least squares (TSLS) and limited information maximum likelihood (LIML) estimators in panel data models where the observables are affected by common shocks, modelled through unobservable factors, are studied for the case where the time series dimension is fixed. We show that the key assumption in determining the consistency of the panel TSLS and LIML estimators, as the cross section dimension tends to infinity, is the lack of correlation between the factor loadings in the errors and in the exogenous variables-including the instruments-conditional on the common shocks. If this condition fails, both estimators have degenerate distributions. When the panel TSLS and LIML estimators are consistent, they have covariance-matrix mixed-normal distributions asymptotically. Tests on the coefficients can be constructed in the usual way and have standard distributions under the null hypothesis.

  • 4. Forchini, Giovanni
    et al.
    Peng, Bin
    A conditional approach to panel data models with common shocks2016In: Econometrics, ISSN 2225-1146, Vol. 4, no 1, article id 4Article in journal (Refereed)
    Abstract [en]

    This paper studies the effects of common shocks on the OLS estimators of the slopes' parameters in linear panel data models. The shocks are assumed to affect both the errors and some of the explanatory variables. In contrast to existing approaches, which rely on using results on martingale difference sequences, our method relies on conditional strong laws of large numbers and conditional central limit theorems for conditionally-heterogeneous random variables.

  • 5.
    Forchini, Giovanni
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Peng, Bin
    A modified first difference estimator for panel data models with a multifactor error structure when the time dimension is small2017In: Communications in Statistics, Vol. forthcomingArticle in journal (Refereed)
  • 6.
    Forchini, Giovanni
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Peng, Bin
    Modified first-difference estimator in a panel data model with unobservable factors both in the errors and the regressors when the time dimension is small2017In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 46, no 24, p. 12226-12239Article in journal (Refereed)
    Abstract [en]

    Panel data models with factor structures in both the errors and the regressors have received considerable attention recently. In these models, the errors and the regressors are correlated and the standard estimators are inconsistent. This paper shows that, for such models, a modified first-difference estimator (in which the time and the cross-sectional dimensions are interchanged) is consistent as the cross-sectional dimension grows but the time dimension is small. Although the estimator has a non standard asymptotic distribution, t and F tests have standard asymptotic distribution under the null hypothesis.

  • 7.
    Forchini, Giovanni
    et al.
    School of Economics, University of Surrey Guildford, Surrey, United Kingdom.
    Ranasinghe, Kulan
    Modelling Multivariate Durations2016In: International Journal of Statistics & Economic, ISSN 0975-556X, Vol. 17, no 1, p. 82-93Article in journal (Refereed)
    Abstract [en]

    We propose a simple procedure to model multivariate durations. This is done by specifying two conditional models: anautoregressive conditional durationmodel for on a pooled series of durationsand a logit model for the type marks. We estimate the model by maximising a pseudo-likelihood which is equivalent to estimating the autoregressive conditional duration model and the logit model separately. We illustrate this methodology by modelling the joint dynamics of the trade shares of Tabcorp Holdings Limited and Tatts group Limited in the Australian financial market between January 15 and January 31 2009.

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