Download or read book Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model written by Huigang Chen and published by International Monetary Fund. This book was released on 2011-10-01 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model averaging and selection. In particular, LIBMA recovers the data generating process well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to their true values. These findings suggest that our methodology is well suited for inference in short dynamic panel data models with endogenous regressors in the context of model uncertainty. We illustrate the use of LIBMA in an application to the estimation of a dynamic gravity model for bilateral trade.
Download or read book Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods written by Alin Mirestean and published by International Monetary Fund. This book was released on 2009-04 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model selection and averaging. In particular, LIBMA recovers the data generating process very well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to the true values. These findings suggest that our methodology is well suited for inference in dynamic panel data models with short time periods in the presence of endogenous regressors under model uncertainty.
Download or read book A Bayesian Approach to Model Uncertainty written by Charalambos G. Tsangarides and published by . This book was released on 2006 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops the theoretical background for the Limited Information Bayesian Model Averaging (LIBMA). The proposed approach accounts for model uncertainty by averaging over all possible combinations of predictors when making inferences about the variables of interest, and it simultaneously addresses the biases associated with endogenous and omitted variables by incorporating a panel data systems Generalized Method of Moments estimator. Practical applications of the developed methodology are discussed, including testing for the robustness of explanatory variables in the analyses of the determinants of economic growth and poverty.
Download or read book Bayesian Model Averaging of Space Time CAR Models with Application to U S House Price Foresasting written by Caoxin Sun and published by . This book was released on 2018 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Prediction Under Model Uncertainty written by Javed Iqbal Ahmed and published by . This book was released on 2010 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Averaging Over Many Dynamic Model Structures with Evidence on the Great Ratios and Liquidity Trap Risk written by Rodney W. Strachan and published by . This book was released on 2008 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Panels with Predetermined Regressors written by Enrique Moral-Benito and published by . This book was released on 2011 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mixtures of G priors for Bayesian Model Averaging with Economic Application written by Eduardo Ley and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression written by Eduardo Ley and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors and on predictive performance. We illustrate these issues in the context of cross-country growth regressions using three datasets with 41-67 potential drivers of growth and 72-93 observations. Finally, we recommend priors for use in this and related contexts.
Download or read book Model Averaging written by David Fletcher and published by Springer. This book was released on 2019-01-17 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means of allowing for model uncertainty when analysing data, and has been used in a wide range of application areas, such as ecology, econometrics, meteorology and pharmacology. The book presents an overview of the methods developed in this area, illustrating many of them with examples from the life sciences involving real-world data. It also includes an extensive list of references and suggestions for further research. Further, it clearly demonstrates the links between the methods developed in statistics, econometrics and machine learning, as well as the connection between the Bayesian and frequentist approaches to model averaging. The book appeals to statisticians and scientists interested in what methods are available, how they differ and what is known about their properties. It is assumed that readers are familiar with the basic concepts of statistical theory and modelling, including probability, likelihood and generalized linear models.
Download or read book Statistical Rethinking written by Richard McElreath and published by CRC Press. This book was released on 2018-01-03 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
Download or read book Recent Econometric Techniques for Macroeconomic and Financial Data written by Gilles Dufrénot and published by Springer Nature. This book was released on 2020-11-21 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides a comprehensive overview of the latest econometric methods for studying the dynamics of macroeconomic and financial time series. It examines alternative methodological approaches and concepts, including quantile spectra and co-spectra, and explores topics such as non-linear and non-stationary behavior, stochastic volatility models, and the econometrics of commodity markets and globalization. Furthermore, it demonstrates the application of recent techniques in various fields: in the frequency domain, in the analysis of persistent dynamics, in the estimation of state space models and new classes of volatility models. The book is divided into two parts: The first part applies econometrics to the field of macroeconomics, discussing trend/cycle decomposition, growth analysis, monetary policy and international trade. The second part applies econometrics to a wide range of topics in financial economics, including price dynamics in equity, commodity and foreign exchange markets and portfolio analysis. The book is essential reading for scholars, students, and practitioners in government and financial institutions interested in applying recent econometric time series methods to financial and economic data.
Download or read book The Trade Impact of European Union Preferential Policies written by Luca De Benedictis and published by Springer Science & Business Media. This book was released on 2011-06-21 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book investigates the EU preferential trade policy and, in particular, the impact it had on trade flows from developing countries. It shows that the capability of the "trade as aid" model to deliver its expected benefits to these countries crucially differs between preferential schemes and sectors. The book takes an eclectic but rigorous approach to the econometric analysis by combining different specifications of the gravity model. An in-depth presentation of the gravity model is also included, providing significant insights into the distinctive features of this technique and its state-of-art implementation. The evidence produced in the book is extensively applied to the analysis of the EU preferential policies with substantial suggestions for future improvement. Additional electronic material to replicate the book's analysis (datasets and Gams and Stata 9.0 routines) can be found in the Extra Materials menu on the website of the book.
Download or read book The Oxford Handbook of Panel Data written by Badi Hani Baltagi and published by . This book was released on 2015 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.
Download or read book Panel Data Econometrics with R written by Yves Croissant and published by John Wiley & Sons. This book was released on 2018-08-10 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.
Download or read book Data Analysis written by Devinderjit Sivia and published by OUP Oxford. This book was released on 2006-06-02 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. - Katie St. Clair MAA Reviews.
Download or read book Econometric Analysis of Panel Data written by Badi Baltagi and published by John Wiley & Sons. This book was released on 2008-06-30 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by one of the world's leading researchers and writers in the field, Econometric Analysis of Panel Data has become established as the leading textbook for postgraduate courses in panel data. This new edition reflects the rapid developments in the field covering the vast research that has been conducted on panel data since its initial publication. Featuring the most recent empirical examples from panel data literature, data sets are also provided as well as the programs to implement the estimation and testing procedures described in the book. These programs will be made available via an accompanying website which will also contain solutions to end of chapter exercises that will appear in the book. The text has been fully updated with new material on dynamic panel data models and recent results on non-linear panel models and in particular work on limited dependent variables panel data models.
