A Comparison of Alternative Instrumental Variables Estimators of a Dynamic Linear Model

A Comparison of Alternative Instrumental Variables Estimators of a Dynamic Linear Model PDF Author: Kenneth David West
Publisher:
ISBN:
Category : Instrumental variables (Statistics)
Languages : en
Pages : 76

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A Comparison of Alternative Instrumental Variables Estimators of a Dynamic Linear Model

A Comparison of Alternative Instrumental Variables Estimators of a Dynamic Linear Model PDF Author: Kenneth David West
Publisher:
ISBN:
Category : Instrumental variables (Statistics)
Languages : en
Pages : 76

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Book Description


A Comparison of Alternative Instruments Variables Estimators of a Dynamic Linear Model

A Comparison of Alternative Instruments Variables Estimators of a Dynamic Linear Model PDF Author: Kenneth D. West
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
Using a dynamic linear equation that has a conditionally homoskedastic moving average disturbance, we compare two parameterizations of a commonly used instrumental variables estimator (Hansen (1982)) to one that is asymptotically optimal in a class of estimators that includes the conventional one (Hansen (1985)). We find that for some plausible data generating processes, the optimal one is distinctly more efficient asymptotically. Simulations indicate that in samples of size typically available, asymptotic theory describes the distribution of the parameter estimates reasonably well, but that test statistics sometimes are poorly sized.

A Comparison of Alternative Instrumental Variables Estimators of a Dynamic Li

A Comparison of Alternative Instrumental Variables Estimators of a Dynamic Li PDF Author: Kenneth D. West
Publisher:
ISBN:
Category :
Languages : en
Pages :

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On Optimal Instrumental Variables Estimation of Stationary Time Series Models

On Optimal Instrumental Variables Estimation of Stationary Time Series Models PDF Author: Kenneth D. West
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 30

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Book Description
In many time series models, an infinite number of moments can be used for estimation in a large sample. I supply a technically undemanding proof of a condition for optimal instrumental variables use of such moments in a parametric model. I also illustrate application of the condition in estimation of a linear model with a conditionally heteroskedastic disturbance.

Nominations of David L. Aaron, Mary Ann Cohen, Margaret Ann Hamburg, M.D., Stanford G. Ross, Ph.D., and David W. Wilcox, Ph.D.

Nominations of David L. Aaron, Mary Ann Cohen, Margaret Ann Hamburg, M.D., Stanford G. Ross, Ph.D., and David W. Wilcox, Ph.D. PDF Author: United States. Congress. Senate. Committee on Finance
Publisher:
ISBN:
Category : Law
Languages : en
Pages : 112

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Methods for Estimation and Inference in Modern Econometrics

Methods for Estimation and Inference in Modern Econometrics PDF Author: Stanislav Anatolyev
Publisher: CRC Press
ISBN: 1439838267
Category : Business & Economics
Languages : en
Pages : 230

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Book Description
This book covers important topics in econometrics. It discusses methods for efficient estimation in models defined by unconditional and conditional moment restrictions, inference in misspecified models, generalized empirical likelihood estimators, and alternative asymptotic approximations. The first chapter provides a general overview of established nonparametric and parametric approaches to estimation and conventional frameworks for statistical inference. The next several chapters focus on the estimation of models based on moment restrictions implied by economic theory. The final chapters cover nonconventional asymptotic tools that lead to improved finite-sample inference.

Generalized Method of Moments

Generalized Method of Moments PDF Author: Alastair R. Hall
Publisher: Oxford University Press
ISBN: 0198775210
Category : Business & Economics
Languages : en
Pages : 413

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Book Description
Generalized Method of Moments (GMM) has become one of the main statistical tools for the analysis of economic and financial data. This book is the first to provide an intuitive introduction to the method combined with a unified treatment of GMM statistical theory and a survey of recentimportant developments in the field. Providing a comprehensive treatment of GMM estimation and inference, it is designed as a resource for both the theory and practice of GMM: it discusses and proves formally all the main statistical results, and illustrates all inference techniques using empiricalexamples in macroeconomics and finance.Building from the instrumental variables estimator in static linear models, it presents the asymptotic statistical theory of GMM in nonlinear dynamic models. Within this framework it covers classical results on estimation and inference techniques, such as the overidentifying restrictions test andtests of structural stability, and reviews the finite sample performance of these inference methods. And it discusses in detail recent developments on covariance matrix estimation, the impact of model misspecification, moment selection, the use of the bootstrap, and weak instrumentasymptotics.

Handbook of Macroeconomics

Handbook of Macroeconomics PDF Author: John B. Taylor
Publisher: Elsevier
ISBN: 9780444501578
Category : Business & Economics
Languages : en
Pages : 576

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Book Description
Annotation Part 6: Financial Markets and the Macroeconomy. 19. Asset prices, consumption, and the business cycle (J.Y. Campbell). 20. Human behavior and the efficiency of the financial system (R.J. Shiller). 21. The financial accelerator in a quantitative business cycle framework (B. Bernanke, M. Gertler and S. Gilchrist). Part 7: Monetary and Fiscal Policy. 22. Political economics and macroeconomic policy (T. Persson, G. Tabellini). 23. Issues in the design of monetary policy rules (B.T. McCallum). 24. Inflation stabilization and BOP crises in developing countries (G.A. Calvo, C.A. Vegh). 25. Government debt (D.W. Elmendorf, N.G. Mankiw). 26. Optimal fiscal and monetary policy (V.V. Chari, P.J. Kehoe).

Methods for Applied Macroeconomic Research

Methods for Applied Macroeconomic Research PDF Author: Fabio Canova
Publisher: Princeton University Press
ISBN: 140084102X
Category : Business & Economics
Languages : en
Pages : 509

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Book Description
The last twenty years have witnessed tremendous advances in the mathematical, statistical, and computational tools available to applied macroeconomists. This rapidly evolving field has redefined how researchers test models and validate theories. Yet until now there has been no textbook that unites the latest methods and bridges the divide between theoretical and applied work. Fabio Canova brings together dynamic equilibrium theory, data analysis, and advanced econometric and computational methods to provide the first comprehensive set of techniques for use by academic economists as well as professional macroeconomists in banking and finance, industry, and government. This graduate-level textbook is for readers knowledgeable in modern macroeconomic theory, econometrics, and computational programming using RATS, MATLAB, or Gauss. Inevitably a modern treatment of such a complex topic requires a quantitative perspective, a solid dynamic theory background, and the development of empirical and numerical methods--which is where Canova's book differs from typical graduate textbooks in macroeconomics and econometrics. Rather than list a series of estimators and their properties, Canova starts from a class of DSGE models, finds an approximate linear representation for the decision rules, and describes methods needed to estimate their parameters, examining their fit to the data. The book is complete with numerous examples and exercises. Today's economic analysts need a strong foundation in both theory and application. Methods for Applied Macroeconomic Research offers the essential tools for the next generation of macroeconomists.

Dynamic Linear Models with R

Dynamic Linear Models with R PDF Author: Giovanni Petris
Publisher: Springer Science & Business Media
ISBN: 0387772383
Category : Mathematics
Languages : en
Pages : 258

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Book Description
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.