Econometrics of Complex Survey Data: Theory and Applications

This volume of Advances in Econometrics contains a selection of papers presented at the Econometrics of Complex Survey Data: Theory and Applications conference, covering methodological and practical topics such as survey collection comparisons, imputation mechanisms, the bootstrap, nonparametric techniques, specification tests, and empirical likelihood estimation. It is essential for academics and students interested in econometrics and complex survey data.

Format: Hardback
Length: 344 pages
Publication date: 10 April 2019
Publisher: Emerald Publishing Limited

This volume of Advances in Econometrics presents a collection of papers that were presented at the Econometrics of Complex Survey Data: Theory and Applications conference, organized by the Bank of Canada in Ottawa, Canada, from October 19 to 20, 2017. The papers included in this volume cover a wide range of methodological and practical topics, such as survey collection comparisons, imputation mechanisms, the bootstrap, nonparametric techniques, specification tests, and empirical likelihood estimation using complex survey data. For academics and students interested in econometrics and the ways in which complex survey data can be used and evaluated, this volume is essential.

Survey collection comparisons: This section explores different methods for comparing survey collections, including the use of sample weights, survey-to-survey comparisons, and the analysis of survey nonresponse. The papers discuss the advantages and disadvantages of each method and provide examples of their application in practice.

Imputation mechanisms: Imputation is a technique used to fill in missing values in survey data. The papers in this section discuss various imputation mechanisms, such as mean imputation, multiple imputation, and Bayesian imputation. The authors discuss the strengths and weaknesses of each method and provide examples of their application in different settings.

The bootstrap: The bootstrap is a statistical method used to estimate confidence intervals and test statistical hypotheses. The papers in this section discuss the bootstrap method and its application in econometrics, including the estimation of model parameters, the testing of statistical significance, and the analysis of complex survey data.

Nonparametric techniques: Nonparametric techniques are used when the data does not meet the assumptions of parametric models. The papers in this section discuss various nonparametric techniques, such as kernel density estimation, histogram smoothing, and nonparametric regression. The authors discuss the advantages and disadvantages of each method and provide examples of their application in practice.

Specification tests: Specification tests are used to test the validity of economic models. The papers in this section discuss various specification tests, such as the ordinary least squares (OLS) regression model, the generalized method of moments (GMM) model, and the maximum likelihood (ML) model. The authors discuss the assumptions and limitations of each method and provide examples of their application in practice.

Empirical likelihood estimation using complex survey data: This section discusses the estimation of empirical likelihood functions using complex survey data. The papers in this section discuss the use of survey-weighted estimators, the bootstrap-based approach, and the EM algorithm. The authors discuss the advantages and disadvantages of each method and provide examples of their application in practice.

Conclusion: In conclusion, this volume of Advances in Econometrics provides a comprehensive overview of the latest developments in econometrics and the use of complex survey data. The papers included in this volume cover a wide range of methodological and practical topics and are written by leading experts in the field. For academics and students interested in econometrics and the ways in which complex survey data can be used and evaluated, this volume is essential.

Weight: 606g
Dimension: 160 x 237 x 25 (mm)
ISBN-13: 9781787567269