Instrumental Variables Inference In A State Space Model

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Instrumental Variables Inference in a State Space Model

Author : Federico Carlini
Publisher :
ISBN 13 :
Total Pages : 68 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Instrumental Variables Inference in a State Space Model by : Federico Carlini

Download or read book Instrumental Variables Inference in a State Space Model written by Federico Carlini and published by . This book was released on 2019 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study semi-parametric inference in a Vector Autoregressive (VAR) model of order p augmented by unobservable common factors with a dynamic described by a VAR process of order q. This state-space specification is useful to define a network of interconnectedness and to measure separately the impulse responses to either systematic, or idiosyncratic, shocks. We show that the state-space parameters are identifiable from the autocovariance function of the observed process.We estimate the model by means of a multi-step procedure in closed-form, which combines an eigenvalue-eigenvector matrix decomposition and Instrumental Variable (IV) estimation allowing for Hansen-Sargan specification tests. We study the asymptotic and finite-sample properties of the parameter estimators and of rank tests for selecting the number of unobservable factors and VAR orders. In an empirical application we investigate which are the dynamic common factors that drive the co-movements in the daily log absolute return series of four sectorial stock market indices of the Chinese economy.

Identification of State Space Models Using Least Squares Or Instrumental Variables

Author : K. F. Zimmermann
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Identification of State Space Models Using Least Squares Or Instrumental Variables by : K. F. Zimmermann

Download or read book Identification of State Space Models Using Least Squares Or Instrumental Variables written by K. F. Zimmermann and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Introductory Econometrics

Author : Jeffrey M. Wooldridge
Publisher : South Western Educational Publishing
ISBN 13 : 9780324788907
Total Pages : 865 pages
Book Rating : 4.7/5 (889 download)

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Book Synopsis Introductory Econometrics by : Jeffrey M. Wooldridge

Download or read book Introductory Econometrics written by Jeffrey M. Wooldridge and published by South Western Educational Publishing. This book was released on 2009 with total page 865 pages. Available in PDF, EPUB and Kindle. Book excerpt: INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 4e International Edition illustrates how empirical researchers think about and apply econometric methods in real-world practice. The text's unique approach reflects the fact that undergraduate econometrics has moved beyond just a set of abstract tools to being genuinely useful for answering questions in business, policy evaluation, and forecasting environments. The systematic approach, which reduces clutter by introducing assumptions only as they are needed, makes absorbing the material easier and leads to better econometric practices. Its unique organization separates topics by the kinds of data being analyzed , leading to an appreciation for the important issues that arise in drawing conclusions from the different kinds of data economists use. Packed with relevant applications, INTRODUCTORY ECONOMETRICS offers a wealth of interesting data sets that can be used to reproduce the examples in the text or as the starting point for original research projects.

Exact Inference in the Instrumental Variables Form of the Linear Structural Errors-in-variables Models

Author : Jie Sun
Publisher :
ISBN 13 :
Total Pages : 164 pages
Book Rating : 4.:/5 (551 download)

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Book Synopsis Exact Inference in the Instrumental Variables Form of the Linear Structural Errors-in-variables Models by : Jie Sun

Download or read book Exact Inference in the Instrumental Variables Form of the Linear Structural Errors-in-variables Models written by Jie Sun and published by . This book was released on 1994 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Inference in Hidden Markov Models

Author : Olivier Cappé
Publisher : Springer Science & Business Media
ISBN 13 : 0387289828
Total Pages : 656 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Inference in Hidden Markov Models by : Olivier Cappé

Download or read book Inference in Hidden Markov Models written by Olivier Cappé and published by Springer Science & Business Media. This book was released on 2006-04-12 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.

State Space Modeling of Time Series

Author : Masanao Aoki
Publisher : Springer Science & Business Media
ISBN 13 : 3642758835
Total Pages : 339 pages
Book Rating : 4.6/5 (427 download)

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Book Synopsis State Space Modeling of Time Series by : Masanao Aoki

Download or read book State Space Modeling of Time Series written by Masanao Aoki and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, the author adopts a state space approach to time series modeling to provide a new, computer-oriented method for building models for vector-valued time series. This second edition has been completely reorganized and rewritten. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. New, fuller descriptions are given of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are newly introduced in this edition in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further new items in this edition include statistical properties of the two types of estimators, more details on multiplier analysis and identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A whole new chapter is devoted to modeling of integrated, nearly integrated and co-integrated time series.

Econometric Modeling and Inference

Author : Jean-Pierre Florens
Publisher : Cambridge University Press
ISBN 13 : 1139466771
Total Pages : 17 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Econometric Modeling and Inference by : Jean-Pierre Florens

Download or read book Econometric Modeling and Inference written by Jean-Pierre Florens and published by Cambridge University Press. This book was released on 2007-07-02 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the main statistical tools of econometrics, focusing specifically on modern econometric methodology. The authors unify the approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing. The choice of GMM is explained by its relevance in structural econometrics and its preeminent position in econometrics overall. Split into four parts, Part I explains general methods. Part II studies statistical models that are best suited for microeconomic data. Part III deals with dynamic models that are designed for macroeconomic and financial applications. In Part IV the authors synthesize a set of problems that are specific to statistical methods in structural econometrics, namely identification and over-identification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises. Nobel Laureate James A. Heckman offers a foreword to the work.

State-Space Models with Regime Switching

Author : Chang-Jin Kim
Publisher : MIT Press
ISBN 13 : 0262535505
Total Pages : 312 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis State-Space Models with Regime Switching by : Chang-Jin Kim

Download or read book State-Space Models with Regime Switching written by Chang-Jin Kim and published by MIT Press. This book was released on 2017-11-03 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both state-space models and Markov switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents recent advances in econometric methods that make feasible the estimation of models that have both features. One approach, in the classical framework, approximates the likelihood function; the other, in the Bayesian framework, uses Gibbs-sampling to simulate posterior distributions from data. The authors present numerous applications of these approaches in detail: decomposition of time series into trend and cycle, a new index of coincident economic indicators, approaches to modeling monetary policy uncertainty, Friedman's "plucking" model of recessions, the detection of turning points in the business cycle and the question of whether booms and recessions are duration-dependent, state-space models with heteroskedastic disturbances, fads and crashes in financial markets, long-run real exchange rates, and mean reversion in asset returns.

Methods Matter

Author : Richard J. Murnane
Publisher : Oxford University Press
ISBN 13 : 0199890153
Total Pages : 414 pages
Book Rating : 4.1/5 (998 download)

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Book Synopsis Methods Matter by : Richard J. Murnane

Download or read book Methods Matter written by Richard J. Murnane and published by Oxford University Press. This book was released on 2010-09-17 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate. Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.

Simplicity, Inference and Modelling

Author : Arnold Zellner
Publisher : Cambridge University Press
ISBN 13 : 1139432389
Total Pages : 314 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Simplicity, Inference and Modelling by : Arnold Zellner

Download or read book Simplicity, Inference and Modelling written by Arnold Zellner and published by Cambridge University Press. This book was released on 2002-02-07 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: The idea that simplicity matters in science is as old as science itself, with the much cited example of Ockham's Razor, 'entia non sunt multiplicanda praeter necessitatem': entities are not to be multiplied beyond necessity. A problem with Ockham's razor is that nearly everybody seems to accept it, but few are able to define its exact meaning and to make it operational in a non-arbitrary way. Using a multidisciplinary perspective including philosophers, mathematicians, econometricians and economists, this 2002 monograph examines simplicity by asking six questions: what is meant by simplicity? How is simplicity measured? Is there an optimum trade-off between simplicity and goodness-of-fit? What is the relation between simplicity and empirical modelling? What is the relation between simplicity and prediction? What is the connection between simplicity and convenience? The book concludes with reflections on simplicity by Nobel Laureates in Economics.

Data Analysis Using Regression and Multilevel/Hierarchical Models

Author : Andrew Gelman
Publisher : Cambridge University Press
ISBN 13 : 9780521686891
Total Pages : 654 pages
Book Rating : 4.6/5 (868 download)

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Book Synopsis Data Analysis Using Regression and Multilevel/Hierarchical Models by : Andrew Gelman

Download or read book Data Analysis Using Regression and Multilevel/Hierarchical Models written by Andrew Gelman and published by Cambridge University Press. This book was released on 2007 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Mathematical Models in Economics - Volume I

Author : Wei-Bin Zhang
Publisher : EOLSS Publications
ISBN 13 : 1848262280
Total Pages : 542 pages
Book Rating : 4.8/5 (482 download)

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Book Synopsis Mathematical Models in Economics - Volume I by : Wei-Bin Zhang

Download or read book Mathematical Models in Economics - Volume I written by Wei-Bin Zhang and published by EOLSS Publications. This book was released on 2009-06-10 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Models in Economics is a component of Encyclopedia of Mathematical Sciences in which is part of the global Encyclopedia of Life Support Systems (EOLSS), an integrated compendium of twenty one Encyclopedias. This theme is organized into several different topics and introduces the applications of mathematics to economics. Mathematical economics has experienced rapid growth, generating many new academic fields associated with the development of mathematical theory and computer. Mathematics is the backbone of modern economics. It plays a basic role in creating ideas, constructing new theories, and empirically testing ideas and theories. Mathematics is now an integral part of economics. The main advances in modern economics are characterized by applying mathematics to various economic problems. Many of today's profound insights into economic problems could hardly be obtained without the help of mathematics. The concepts of equilibrium versus non-equilibrium, stability versus instability, and steady states versus chaos in the contemporary literature are difficult to explain without mathematics. The theme discusses on modern versions of some classical economic theories, taking account of balancing between significance of economic issues and mathematical techniques. These two volumes are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers and NGOs.

Advanced Methods for Modeling Markets

Author : Peter S. H. Leeflang
Publisher : Springer
ISBN 13 : 3319534696
Total Pages : 725 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Advanced Methods for Modeling Markets by : Peter S. H. Leeflang

Download or read book Advanced Methods for Modeling Markets written by Peter S. H. Leeflang and published by Springer. This book was released on 2017-08-29 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents advanced techniques to modeling markets, with a wide spectrum of topics, including advanced individual demand models, time series analysis, state space models, spatial models, structural models, mediation, models that specify competition and diffusion models. It is intended as a follow-on and companion to Modeling Markets (2015), in which the authors presented the basics of modeling markets along the classical steps of the model building process: specification, data collection, estimation, validation and implementation. This volume builds on the concepts presented in Modeling Markets with an emphasis on advanced methods that are used to specify, estimate and validate marketing models, including structural equation models, partial least squares, mixture models, and hidden Markov models, as well as generalized methods of moments, Bayesian analysis, non/semi-parametric estimation and endogeneity issues. Specific attention is given to big data. The market environment is changing rapidly and constantly. Models that provide information about the sensitivity of market behavior to marketing activities such as advertising, pricing, promotions and distribution are now routinely used by managers for the identification of changes in marketing programs that can improve brand performance. In today’s environment of information overload, the challenge is to make sense of the data that is being provided globally, in real time, from thousands of sources. Although marketing models are now widely accepted, the quality of the marketing decisions is critically dependent upon the quality of the models on which those decisions are based. This volume provides an authoritative and comprehensive review, with each chapter including: · an introduction to the method/methodology · a numerical example/application in marketing · references to other marketing applications · suggestions about software. Featuring contributions from top authors in the field, this volume will explore current and future aspects of modeling markets, providing relevant and timely research and techniques to scientists, researchers, students, academics and practitioners in marketing, management and economics.

Bayesian Econometrics

Author : Siddhartha Chib
Publisher : Emerald Group Publishing
ISBN 13 : 1848553099
Total Pages : 656 pages
Book Rating : 4.8/5 (485 download)

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Book Synopsis Bayesian Econometrics by : Siddhartha Chib

Download or read book Bayesian Econometrics written by Siddhartha Chib and published by Emerald Group Publishing. This book was released on 2008-12-18 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: Illustrates the scope and diversity of modern applications, reviews advances, and highlights many desirable aspects of inference and computations. This work presents an historical overview that describes key contributions to development and makes predictions for future directions.

Elements of Causal Inference

Author : Jonas Peters
Publisher : MIT Press
ISBN 13 : 0262037319
Total Pages : 289 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Elements of Causal Inference by : Jonas Peters

Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Dynamic Linear Models with R

Author : Giovanni Petris
Publisher : Springer Science & Business Media
ISBN 13 : 0387772383
Total Pages : 258 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Dynamic Linear Models with R by : Giovanni Petris

Download or read book Dynamic Linear Models with R written by Giovanni Petris and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

A Class of Non-Gaussian State Space Models with Exact Likelihood Inference

Author : Drew Creal
Publisher :
ISBN 13 :
Total Pages : 30 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis A Class of Non-Gaussian State Space Models with Exact Likelihood Inference by : Drew Creal

Download or read book A Class of Non-Gaussian State Space Models with Exact Likelihood Inference written by Drew Creal and published by . This book was released on 2014 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: The likelihood function for general non-linear, non-Gaussian state space models is a high- dimensional integral with no closed-form solution. In this paper, I show how to calculate the likelihood function exactly for a large class of non-Gaussian state space models that includes stochastic intensity, stochastic volatility, and stochastic duration models among others. The state variables in this class follow a non-negative stochastic process that is popular in econometrics for modeling volatility and intensities. In addition to calculating the maximum likelihood estimator, I also show how to perform filtering and smoothing to estimate the latent variables in the model. Finally, it is also possible to take random draws from the joint posterior distribution of the latent states conditional on the data and the model's parameters, which is valuable for inference of more complex models.