This book presents recent research on robustness in econometrics. Robust data processing techniques i.e., techniques that yield results minimally affected by outliers and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems.
Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.
Part I Keynote Addresses: Robust Estimation of Heckman Model.- Part II Fundamental Theory: Sequential Monte Carlo Sampling for State Space Models.- Robustness as a Criterion for Selecting a Probability Distribution Under Uncertainty.- Why Cannot We Have a Strongly Consistent Family of Skew Normal (and Higher Order) Distributions.- Econometric Models of Probabilistic Choice: Beyond McFaddens Formulas.- How to Explain Ubiquity of Constant Elasticity of Substitution (CES) Production and Utility Functions Without Explicitly Postulating CES.- How to Make Plausibility-Based Forecasting More Accurate.- Structural Breaks of CAPM-type Market Model with Heteroskedasticity and Quantile Regression.- Weighted Least Squares and Adaptive Least Squares: Further Empirical Evidence.- Prior-free probabilistic inference for econometricians.- Robustness in Forecasting Future Liabilities in Insurance.- On Conditioning in Multidimensional Probabilistic Models.- New Estimation Method for Mixture of Normal Distributions.- EM Eslœ
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