Browsing by Author "Pakel, Cavit"

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    Bias reduction in nonlinear and dynamic panels in the presence of cross-section dependence
    (Elsevier, 2019) Pakel, Cavit
    Fixed effects estimation of nonlinear dynamic panel models is subject to the incidental parameter issue, leading to a biased asymptotic distribution. While this problem has been studied extensively in the literature, a general analysis allowing for both serial and cross-sectional dependence is missing. In this paper we investigate the large-N, T theory of the profile and integrated likelihood estimators, allowing for dependence across both dimensions. We show that under stronger dependence types the asymptotic bias disappears, but a Op(1/T ) small-sample bias remains. We provide bias correction and inference methods, and also obtain primitive conditions for asymptotic normality under various dependence settings.
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    Fitting vast dimensional time-varying covariance models
    (Taylor and Francis, 2021) Pakel, Cavit; Shephard, N; Sheppard, K.; Engle, R. F.
    Estimation of time-varying covariances is a key input in risk management and asset allocation. ARCH-type multivariate models are used widely for this purpose. Estimation of such models is computationally costly and parameter estimates are meaningfully biased when applied to a moderately large number of assets. Here, we propose a novel estimation approach that suffers from neither of these issues, even when the number of assets is in the hundreds. The theory of this new method is developed in some detail. The performance of the proposed method is investigated using extensive simulation studies and empirical examples. Supplementary materials for this article are available online.