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In particular, students need to have solid background in probability theory, mathematical statistics, econometric methods and time series analysis, comparable to the knowledge obtained during the econometric courses of the bachelor programme Econometrics and Operations Research.

Econometric Modeling and Inference Themes in Modern Econometrics

Familiarity with asymptotic analysis is necessary. In addition, a solid knowledge about time series econometrics is recommended, in particular about VAR models. One way to achieve more than sufficient knowledge of time series econometrics is by following the course Time Series Analysis and Dynamic Econometrics potentially in parallel. This website uses cookies. Skip to main content.

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Full course description In this course we cover several advanced techniques that have recently been developed in econometrics and statistics for the analysis of high-dimensional problems, which often arise in the context of Big Data. Course objectives The objective of this course is to provide students with an understanding of modern and advanced econometric techniques for the analysis of high-dimensional data.


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Anatolyev Stanislav. Sanbook 2.

Methods for Estimation and Inference in Modern Econometrics provides a comprehensive introduction to a wide range of emerging topics, such as generalized empirical likelihood estimation and alternative asymptotics under drifting parameterizations, which have not been discussed in detail outside of highly technical research papers. The book also addresses several problems often arising in the analysis of economic data, including weak identification, model misspecification, and possible nonstationarity.

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Anatolyev / Gospodinov | Methods for Estimation and Inference in Modern Econometrics |

The book's appendix provides a review of some basic concepts and results from linear algebra, probability theory, and statistics that are used throughout the book. Topics covered include: Well-established nonparametric and parametric approaches to estimation and conventional asymptotic and bootstrap frameworks for statistical inference Estimation of models based on moment restrictions implied by economic theory, including various method-of-moments estimators for unconditional and conditional moment restriction models, and asymptotic theory for correctly specified and misspecified models Non-conventional asymptotic tools that lead to improved finite sample inference, such as higher-order asymptotic analysis that allows for more accurate approximations via various asymptotic expansions, and asymptotic approximations based on drifting parameter sequences Offering a unified approach to studying econometric problems, Methods for Estimation and Inference in Modern Econometrics links most of the existing estimation and inference methods in a general framework to help readers synthesize all aspects of modern econometric theory.

Various theoretical exercises and suggested solutions are included to facilitate understanding. Have doubts regarding this product?