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Estimation, Inference and Specification Analysis
923
TL;DR: The underlying motivation for maximum-likelihood estimation is explored, the interpretation of the MLE for misspecified probability models is treated, and the conditions under which parameters of interest can be consistently estimated despite misspecification are given.
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Abstract: This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models, and gives the conditions under which parameters of interest can be consistently estimated despite misspecification, and the consequences of misspecification, for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. Although the theory presented in the book is motivated by econometric problems, its applicability is by no means restricted to economics. Subject to defined limitations, the theory applies to any scientific context in which statistical analysis is conducted using approximate models.
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•Book
Econometric Analysis of Cross Section and Panel Data
Jeffrey M. Wooldridge
- 01 Jan 2001
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Simultaneous inference in general parametric models.
TL;DR: This paper describes simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters, and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalizedlinear models, linear mixed effects models, the Cox model, robust linear models, etc.
On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms
Francis X. Diebold,Kamil Yilmaz +1 more
TL;DR: In this paper, the authors propose several connectedness measures built from pieces of variance decomposition positions, and argue that they provide natural and insightful measures of connectedness among nancial asset returns and volatilities.
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CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles *
TL;DR: In this article, the authors propose a new approach to quantile estimation which does not require any of the extreme assumptions invoked by existing methodologies (such as normality or i.i.d. returns).
The Model Confidence Set
TL;DR: The paper revisits the inflation forecasting problem posed by Stock and Watson (1999), and compute the model confidence set (MCS) for their set of inflation forecasts, and compares a number of Taylor rule regressions to determine the MCS of the best in terms of in-sample likelihood criteria.