Nikolay Gospodinov – författare

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. 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 inferenceEstimation 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 modelsNon-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.

The authors introduce a novel bootstrap approach to resampling asset price data that can be used for both finite-maturity assets and equities. The key insight is that they bootstrap primitive objects with more appealing statistical properties to avoid resampling series with strong time-series and cross-sectional dependence. They then recover the original dependence structure in an internally consistent manner via definitional identities. Their bootstrap is nonparametric in nature and so avoids the common practice of committing to a tightly parameterized pricing model with explicit assumptions on the form of cross-sectional and time-series dependence. They demonstrate the appealing finite-sample properties of their bootstrap approach in a series of simulation experiments and empirical applications.

The authors introduce a novel bootstrap approach to resampling asset price data that can be used for both finite-maturity assets and equities. The key insight is that they bootstrap primitive objects with more appealing statistical properties to avoid resampling series with strong time-series and cross-sectional dependence. They then recover the original dependence structure in an internally consistent manner via definitional identities. Their bootstrap is nonparametric in nature and so avoids the common practice of committing to a tightly parameterized pricing model with explicit assumptions on the form of cross-sectional and time-series dependence. They demonstrate the appealing finite-sample properties of their bootstrap approach in a series of simulation experiments and empirical applications.