Simulation-based Inference in Econometrics

Simulation-based Inference in Econometrics PDF Author: Roberto Mariano
Publisher: Cambridge University Press
ISBN: 9780521591126
Category : Business & Economics
Languages : en
Pages : 488

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Book Description
This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.

Simulation-based Inference in Econometrics

Simulation-based Inference in Econometrics PDF Author: Roberto S. Mariano
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 0

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Simulation-based Econometric Methods

Simulation-based Econometric Methods PDF Author: Christian Gouriéroux
Publisher: OUP Oxford
ISBN: 019152509X
Category : Business & Economics
Languages : en
Pages : 190

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Book Description
This book introduces a new generation of statistical econometrics. After linear models leading to analytical expressions for estimators, and non-linear models using numerical optimization algorithms, the availability of high- speed computing has enabled econometricians to consider econometric models without simple analytical expressions. The previous difficulties presented by the presence of integrals of large dimensions in the probability density functions or in the moments can be circumvented by a simulation-based approach. After a brief survey of classical parametric and semi-parametric non-linear estimation methods and a description of problems in which criterion functions contain integrals, the authors present a general form of the model where it is possible to simulate the observations. They then move to calibration problems and the simulated analogue of the method of moments, before considering simulated versions of maximum likelihood, pseudo-maximum likelihood, or non-linear least squares. The general principle of indirect inference is presented and is then applied to limited dependent variable models and to financial series.

Introduction to Statistical Investigations

Introduction to Statistical Investigations PDF Author: Nathan Tintle
Publisher: Wiley Global Education
ISBN: 1119154316
Category : Mathematics
Languages : en
Pages : 696

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Book Description
Introduction to Statistical Investigations leads students to learn about the process of conducting statistical investigations from data collection, to exploring data, to statistical inference, to drawing appropriate conclusions. The text is designed for a one-semester introductory statistics course. It focuses on genuine research studies, active learning, and effective use of technology. Simulations and randomization tests introduce statistical inference, yielding a strong conceptual foundation that bridges students to theory-based inference approaches. Repetition allows students to see the logic and scope of inference. This implementation follows the GAISE recommendations endorsed by the American Statistical Association.

Introductory Econometrics

Introductory Econometrics PDF Author: Humberto Barreto
Publisher: Cambridge University Press
ISBN: 9780521843195
Category : Business & Economics
Languages : en
Pages : 810

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Book Description
This highly accessible and innovative text with supporting web site uses Excel (R) to teach the core concepts of econometrics without advanced mathematics. It enables students to use Monte Carlo simulations in order to understand the data generating process and sampling distribution. Intelligent repetition of concrete examples effectively conveys the properties of the ordinary least squares (OLS) estimator and the nature of heteroskedasticity and autocorrelation. Coverage includes omitted variables, binary response models, basic time series, and simultaneous equations. The authors teach students how to construct their own real-world data sets drawn from the internet, which they can analyze with Excel (R) or with other econometric software. The accompanying web site with text support can be found at www.wabash.edu/econometrics.

Finite-sample Simulation-based Inference in VAR Models with Applications to Order Selection and Causality Testing

Finite-sample Simulation-based Inference in VAR Models with Applications to Order Selection and Causality Testing PDF Author: Dufour, Jean-Marie
Publisher: Montréal : CIRANO
ISBN: 9782893825106
Category : Autoregression (Statistics)
Languages : en
Pages : 32

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Book Description


Applications of Simulation Methods in Environmental and Resource Economics

Applications of Simulation Methods in Environmental and Resource Economics PDF Author: Riccardo Scarpa
Publisher: Springer Science & Business Media
ISBN: 9781402036835
Category : Business & Economics
Languages : en
Pages : 456

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Book Description
Simulation methods are revolutionizing the practice of applied economic analysis. In this book, leading researchers from around the world discuss interpretation issues, similarities and differences across alternative models, and propose practical solutions for the choice of the model and programming. Case studies show the practical use and the results brought forth by the different methods.

Bayesian Inference in Dynamic Econometric Models

Bayesian Inference in Dynamic Econometric Models PDF Author: Luc Bauwens
Publisher: OUP Oxford
ISBN: 0191588466
Category : Business & Economics
Languages : en
Pages : 370

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Book Description
This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.

Discrete Choice Methods with Simulation

Discrete Choice Methods with Simulation PDF Author: Kenneth Train
Publisher: Cambridge University Press
ISBN: 0521766559
Category : Business & Economics
Languages : en
Pages : 399

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Book Description
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

Finite-sample Simulation-based Inference in VAR Models with Applications to Order Selection and Causality Testing

Finite-sample Simulation-based Inference in VAR Models with Applications to Order Selection and Causality Testing PDF Author: Jean-Marie Dufour
Publisher: Centre interuniversitaire de recherche en économie quantitative
ISBN: 9782893825106
Category : Autoregression (Statistics)
Languages : en
Pages : 0

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Book Description