Download or read book Essays in Modeling Fat Time Series Data Using Bayesian Econometrics written by Jan Prüser and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays on Bayesian Time Series and Variable Selection written by Debkumar De and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimating model parameters in dynamic model continues to be challenge. In my dissertation, we have introduced a Stochastic Approximation based parameter estimation approach under Ensemble Kalman Filter set-up. Asymptotic properties of the resultant estimates are discussed here. We have compared our proposed method to current methods via simulation studies. We have demonstrated predictive performance of our proposed method on a large spatio-temporal data. In my other topic, we presented a method for simultaneous estimation of regression parameters and the covariance matrix, developed for a nonparametric Seemingly Unrelated Regression problem. This is a very flexible modeling technique that essentially performs a sparse high-dimensional multiple predictor(p), multiple responses(q) regression where the responses may be correlated. Such data appear abundantly in the fields of genomics, finance and econometrics. We illustrate and compare performances of our proposed techniques with previous analyses using both simulated and real multivariate data arising in econometrics and government. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/152793
Download or read book Time Series Analysis and Macroeconometric Modelling written by Kenneth Frank Wallis and published by Edward Elgar Publishing. This book was released on 1995-01-01 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'An excellent reference volume of this author's work, bringing together articles published over a 25 year span on the statistical analysis of economic time series, large scale macroeconomic modelling and the interface between them.' - Aslib Book Guide This major volume of essays by Kenneth F. Wallis features 28 articles published over a quarter of a century on the statistical analysis of economic time series, large-scale macroeconometric modelling, and the interface between them. The first part deals with time-series econometrics and includes significant early contributions to the development of the LSE tradition in time-series econometrics, which is the dominant British tradition and has considerable influence worldwide. Later sections discuss theoretical and practical issues in modelling seasonality and forecasting with applications in both large-scale and small-scale models. The final section summarizes the research programme of the ESRC Macroeconomic Modelling Bureau, a unique comparison project among economy-wide macroeconometric models.
Download or read book Essays in Honor of Cheng Hsiao written by Dek Terrell and published by Emerald Group Publishing. This book was released on 2020-04-15 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.
Download or read book Bayesian Multivariate Time Series Methods for Empirical Macroeconomics written by Gary Koop and published by Now Publishers Inc. This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used.
Download or read book Applied Bayesian Forecasting and Time Series Analysis written by Andy Pole and published by CRC Press. This book was released on 2018-10-08 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Explore diverse aspects of time series, including how to identify, structure, explain observed behavior, model structures and behaviors, and interpret analyses to make informed forecasts Illustrate concepts such as component decomposition, fundamental model forms including trends and cycles, and practical modeling requirements for routine change and unusual events Conduct all analyses in the BATS computer programs, furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.
Download or read book Essays on Bayesian Inference of Time series and Ordered Panel Data Models written by Jeehyun Park and published by . This book was released on 2012 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the heart of my dissertation is the study of Markov chain Monte Carlo algorithms and their applications. My dissertation consists of three essays as follow. The first chapter is on MCMC algorithms for the dynamic ordered probit model with random effects. I have tried to estimate the model with four representative MCMC algorithms: two algorithms by Albert and Chib (1993) and Albert and Chib (2001), Liu and Sabatti (2000), and Chen and Dey (2000). I have found that the autocorrelations still remain high in the cutoffs compared to other parameters even though the levels of autocorrelation are reduced in the algorithms by Liu and Sabatti (2000), and Chen and Dey (2000). In the second chapter, I have developed the dynamic ordered probit model studied in the first chapter. It is natural for panel data to have missing data problem because there is no guarantee that subjects will stay over the study periods. This chapter provides Bayesian statistical methods that permit non-ignorable missing data in panel datasets. In order to incorporate non-random missing data in the model, I jointly model observed and non-ignorable missing ordinal data with selection model approach. In the empirical section, I have used the model to examine determinants of self-rated health of old people in the Health and Retirement Study. I have concluded that in this elderly American population, the longest occupation that respondents have held over their careers is strongly associated with self-rated health. In the third chapter of my dissertation, I analyze financial time-series data before and after the Wall Street meltdown in 2008. In this chapter, I develop MCMC algorithms for the CKLS model and examine (1) time-series characteristics of the credit default swap index, stock index and federal funds rate from January 2007 to September 2009, the highly volatile period. (2) The lead-lag relationship between the credit default swap and stock markets are examined using the CKLS model employing multivariate analysis.
Download or read book Modelling Spatial and Spatial Temporal Data A Bayesian Approach written by Robert P. Haining and published by CRC Press. This book was released on 2020-01-27 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.
Download or read book Regression Modelling wih Spatial and Spatial Temporal Data written by Robert P. Haining and published by CRC Press. This book was released on 2020-01-27 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.
Download or read book Time Series written by Raquel Prado and published by CRC Press. This book was released on 2010-05-21 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian t
Download or read book Essays in Honour of Fabio Canova written by Juan J. Dolado and published by Emerald Group Publishing. This book was released on 2022-09-16 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both parts of Volume 44 of Advances in Econometrics pay tribute to Fabio Canova for his major contributions to economics over the last four decades.
Download or read book Essays on Bayesian Econometrics written by Jaeho Kim and published by . This book was released on 2014 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation explores important macroeconomics issues based on Bayesian Econometrics tools developed. One goal of the first chapter of the dissertation is to develop an efficient Markov-Chain Monte Carlo (MCMC) algorithm for estimating an ARMA model with a regime-switching mean, based on a multi-move sampler. Unlike the existing algorithm of Billio et al. (1999) based on a single-move sampler, our algorithm can achieve reasonably fast convergence to the posterior distribution even when the latent regime indicator variable is highly persistent or when there exist absorbing states. Another goal of the first chapter is to appropriately investigate the dynamics of the latent ex-ante real interest rate (EARR) in the presence of structural breaks, by employing the econometric tool developed. We argue Garcia and Perron's (1996) conclusion that the EARR rate is a constant subject to occasional jumps may be sample-specific. For an extended sample that includes recent data, Garcia and Perron's (1996) AR(2) model of EPRR may be misspecified, and we show that excluding the theory-implied moving-average terms may understate the persistence of the observed ex-post real interest rate (EPRR) dynamics. Our empirical results suggest that, even though we rule out the possibility of a unit root in the EARR, it may be more persistent and volatile than has been documented in some of the literature including Garcia and Perron (1996). The second chapter of the dissertation investigates the conventional wisdom that in the case of a flat prior Bayesian inference will not be very different from classical inference, as the likelihood dominates the posterior density. This chapter shows that there are cases in which this conventional wisdom does not apply. An ARMA model of real GDP growth estimated by Perron and Wada (2009) is an example. While their maximum likelihood estimation of the model implies that real GDP may be a trend stationary process, Bayesian estimation of the same model implies that most of the variations in real GDP can be explained by the stochastic trend component, as in Nelson and Plosser (1982) and Morley et al. (2003). We show such dramatically different results stem from the differences in how the nuisance parameters are handled between the two approaches, especially when the parameter estimate of interest is dependent upon the estimates of the nuisance parameters for small samples. For the maximum likelihood approach, as the number of the nuisance parameters increases, we have higher probability that the moving-average root may be estimated to be one even when its true value is less than one, spuriously indicating that the data is `over-differenced.' However, the Bayesian approach is relatively free from this pile-up problem, as the posterior distribution is not dependent upon the nuisance parameters. The last chapter of the dissertation is about Bayesian model comparison. Bayesian model comparison is often achieved by the Bayes Factor which is sensitive to prior assumptions. Various alternative Bayes Factors such as the Intrinsic}and Fractional Bayes Factors have been proposed to overcome this problem. However, practical problems arise since they include many marginal likelihoods which are not analytically tractable in most cases. An encompassing prior approach (EP) is a recently proposed method to approximate the Bayes Factor numerically in comparing nested models. We extend EP approach to the alternative Bayes Factors in this chapter. Our method provides a simple and elegant way to conduct robust model comparisons to the prior sensitivity for nested models.
Download or read book Essays in Macroeconomic Forecasting with Linear and Non linear Time Series Models Using Bayesian Techniques written by Georgios Kontogeorgos and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays in Honor of Joon Y Park written by Yoosoon Chang and published by Emerald Group Publishing. This book was released on 2023-04-24 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volumes 45a and 45b of Advances in Econometrics honor Professor Joon Y. Park, who has made numerous and substantive contributions to the field of econometrics over a career spanning four decades since the 1980s and counting.
Download or read book Essays on Bayesian Model Averaging Using Economic Time Series written by Richard Hugo Kleijn and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Three Essays Involving Time Series Analysis written by Jeffrey Harris Dorfman and published by . This book was released on 1989 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Data Analysis Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
