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Econometric Inference Using Simulation Techniques

Author : Herman K. van Dijk
Publisher :
Page : 290 pages
File Size : 24,29 MB
Release : 1995-07-11
Category : Business & Economics
ISBN :

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This book provides a comprehensive assessment of the latest simulation techniques, and examines the three main areas of econometric inference where the use of simulation methods has been successful; Bayesian inference, classical inference, and the solution and stochastic simulation of dynamic econometric models, in particular general equilibrium models.

Simulation-based Inference in Econometrics

Author : Roberto Mariano
Publisher : Cambridge University Press
Page : 488 pages
File Size : 33,64 MB
Release : 2000-07-20
Category : Business & Economics
ISBN : 9780521591126

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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.

Palgrave Handbook of Econometrics

Author : Terence C. Mills
Publisher : Springer
Page : 1406 pages
File Size : 36,16 MB
Release : 2009-06-25
Category : Business & Economics
ISBN : 0230244408

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Following theseminal Palgrave Handbook of Econometrics: Volume I , this second volume brings together the finestacademicsworking in econometrics today andexploresapplied econometrics, containing contributions onsubjects includinggrowth/development econometrics and applied econometrics and computing.

The Econometrics of Panel Data

Author : László Mátyás
Publisher : Springer Science & Business Media
Page : 944 pages
File Size : 33,86 MB
Release : 2013-12-01
Category : Business & Economics
ISBN : 9400901372

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The aim of this volume is to provide a general overview of the econometrics of panel data, both from a theoretical and from an applied viewpoint. Since the pioneering papers by Edwin Kuh (1959), Yair Mundlak (1961), Irving Hoch (1962), and Pietro Balestra and Marc Nerlove (1966), the pooling of cross sections and time series data has become an increasingly popular way of quantifying economic relationships. Each series provides information lacking in the other, so a combination of both leads to more accurate and reliable results than would be achievable by one type of series alone. Over the last 30 years much work has been done: investigation of the properties of the applied estimators and test statistics, analysis of dynamic models and the effects of eventual measurement errors, etc. These are just some of the problems addressed by this work. In addition, some specific diffi culties associated with the use of panel data, such as attrition, heterogeneity, selectivity bias, pseudo panels etc., have also been explored. The first objective of this book, which takes up Parts I and II, is to give as complete and up-to-date a presentation of these theoretical developments as possible. Part I is concerned with classical linear models and their extensions; Part II deals with nonlinear models and related issues: logit and pro bit models, latent variable models, duration and count data models, incomplete panels and selectivity bias, point processes, and simulation techniques.

Financial Risk Management with Bayesian Estimation of GARCH Models

Author : David Ardia
Publisher : Springer Science & Business Media
Page : 206 pages
File Size : 34,50 MB
Release : 2008-05-08
Category : Business & Economics
ISBN : 3540786570

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This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.

The Econometrics of Macroeconomic Modelling

Author : Gunnar Bårdsen
Publisher : OUP Oxford
Page : 362 pages
File Size : 38,41 MB
Release : 2005-04-14
Category : Business & Economics
ISBN : 0191529877

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Macroeconometric models, in many ways the flagships of the economist's profession in the 1960s, came under increasing attack from both theoretical economist and practitioners in the late 1970s. Critics referred to their lack of microeconomic theoretical foundations, ad hoc models of expectations, lack of identification, neglect of dynamics and non-stationarity, and poor forecasting properties. By the start of the 1990s, the status of macroeconometric models had declined markedly, and had fallen completely out of, and with, academic economics. Nevertheless, unlike the dinosaurs to which they often have been likened, macroeconometric models have never completely disappeared from the scene. This book describes how and why the discipline of macroeconometric modelling continues to play a role for economic policymaking by adapting to changing demands, in response, for instance, to new policy regimes like inflation targeting. Model builders have adopted new insights from economic theory and taken advantage of the methodological and conceptual advances within time series econometrics over the last twenty years. The modelling of wages and prices takes a central part in the book as the authors interpret and evaluate the last forty years of international research experience in the light of the Norwegian 'main course' model of inflation in a small open economy. The preferred model is a dynamic model of incomplete competition, which is evaluated against alternatives as diverse as the Phillips curve, Nickell-Layard wage curves, the New Keynesian Phillips curve, and monetary inflation models on data from the Euro area, the UK, and Norway. The wage price core model is built into a small econometric model for Norway to analyse the transmission mechanism and to evaluate monetary policy rules. The final chapter explores the main sources of forecast failure likely to occur in a practical modelling situation, using the large-scale nodel RIMINI and the inflation models of earlier chapters as case studies.

New Directions in Spatial Econometrics

Author : Luc Anselin
Publisher : Springer Science & Business Media
Page : 432 pages
File Size : 22,36 MB
Release : 2012-12-06
Category : Business & Economics
ISBN : 3642798772

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The promising new directions for research and applications described here include alternative model specifications, estimators and tests for regression models and new perspectives on dealing with spatial effects in models with limited dependent variables and space-time data.