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Hayashi's 'Econometrics' promises to be the next great synthesis of modern econometrics. It introduces first year Ph.D. students to standard graduate econometrics material from a modern perspective. It covers all the standard material necessary for understanding the principal techniques of econometrics from ordinary least squares through cointegration. The book is also distinctive in developing both time-series and cross-section analysis fully, giving the reader a unified framework for understanding and integrating results. 'Econometrics' has many useful features and covers all the important topics in econometrics in a succinct manner. All the estimation techniques that could possibly be taught in a first-year graduate course, except maximum likelihood, are treated as special cases of GMM (generalized methods of moments). Maximum likelihood estimators for a variety of models (such as probit and tobit) are collected in a separate chapter. This arrangement enables students to learn various estimation techniques in an efficient manner. Eight of the ten chapters include a serious empirical application drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises at the end of each chapter provide students a hands-on experience applying the techniques covered in the chapter. The exposition is rigorous yet accessible to students who have a working knowledge of very basic linear algebra and probability theory. All the results are stated as propositions, so that students can see the points of the discussion and also the conditions under which those results hold. Most propositions are proved in the text. For those who intend to write a thesis on applied topics, the empirical applications of the book are a good way to learn how to conduct empirical research. For the theoretically inclined, the no-compromise treatment of the basic techniques is a good preparation for more advanced theory courses.

Quantitative methods (economics) --- Econometrics. --- Econométrie --- Econometrics --- 330.115 --- AA / International- internationaal --- 303.0 --- 305.970 --- 303.6 --- 303.2 --- 303.5 --- 305.974 --- 330.015195 --- #SBIB:303H66 --- Economics, Mathematical --- Statistics --- Econometrie --- Kwantitatieve methoden (economie) --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken). --- Algemeenheden: Autoregression and moving average representation. ARIMA. ARMAX. Lagrange multiplier. Wald. Function (mis) specification. Autocorrelation. Homoscedasticity. Heteroscedasticity. ARCH. GARCH. Integration and co-integration. Unit roots. --- Raming : theorie (wiskundige statistiek). Bayesian analysis and inference. --- Spreiding en deviatie (wiskundige statistiek). Curtosis. Moments. GMM. --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek). --- Time varying coefficients. Kalman Filter. --- 330.115 Econometrie --- Econométrie --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken) --- Spreiding en deviatie (wiskundige statistiek). Curtosis. Moments. GMM --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek) --- Raming : theorie (wiskundige statistiek). Bayesian analysis and inference --- Algemeenheden: Autoregression and moving average representation. ARIMA. ARMAX. Lagrange multiplier. Wald. Function (mis) specification. Autocorrelation. Homoscedasticity. Heteroscedasticity. ARCH. GARCH. Integration and co-integration. Unit roots --- Time varying coefficients. Kalman Filter --- Économétrie --- Économétrie

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Graphic modelling is a form of multivariate analysis that uses graphs to represent models. These graphs display the structure of dependencies, both associational and causal, between the variables in the model. This textbook provides an introduction to graphical modelling with emphasis on applications and practicalities rather than on a formal development. It is based on the popular software package for graphical modelling, MIM, a freeware version of which can be downloaded from the Internet. Following an introductory chapter which sets the scene and describes some of the basic ideas of graphical modelling, subsequent chapters describe particular families of models, including log-linear models, Gaussian models, and models for mixed discrete and continuous variables. Further chapters cover hypothesis testing and model selection. Chapters 7 and 8 are new to the second edition. Chapter 7 describes the use of directed graphs, chain graphs, and other graphs. Chapter 8 summarizes some recent work on causal inference, relevant when graphical models are given a causal interpretation. This book will provide a useful introduction to this topic for students and researchers.

Graphical modeling (Statistics) --- AA / International- internationaal --- 302 --- 303.0 --- Opmaak en presentatie van statistische reeksen en tabellen. Grafieken. --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken). --- Graphical modeling (Statistics). --- Multivariate analysis --- Opmaak en presentatie van statistische reeksen en tabellen. Grafieken --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken) --- Graphic methods --- Mathematical models. --- Statistics . --- Mathematical Modeling and Industrial Mathematics. --- Statistical Theory and Methods. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Models, Mathematical --- Simulation methods

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Most of the available texts on the subject are either quite advanced and theoretical or too applied, descriptive, and lacking statistical results. Sampling Methodologies with Applications offers a balanced, practical treatment of the techniques and applications of the commonly used procedures for sampling from finite populations. It keeps mathematics to a minimum, but does not avoid them entirely. In an easily followed, step-by-step presentation, the author motivates each topic with illustrations followed by examples and exercises. All of these are constructed from everyday, practical situations covering a wide variety of topics.

Mathematical statistics --- Social sciences --- Sampling (Statistics) --- Statistical methods --- 519.243 --- #SBIB:013.GIFTSOC --- #SBIB:303H510 --- maatschappijwetenschappen, methoden --- wiskundige statistiek --- Sampling. Sampling theory --- Methoden sociale wetenschappen: statistische technieken, algemeen --- Statistical methods. --- Sampling (Statistics). --- 519.243 Sampling. Sampling theory --- Random sampling --- Statistics of sampling --- Statistics --- Social sciences - Statistical methods

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This volume contains lectures given at the Saint-Flour Summer School of Probability Theory during 17th Aug. - 3rd Sept. 1998. The contents of the three courses are the following: - Continuous martingales on differential manifolds. - Topics in non-parametric statistics. - Free probability theory. The reader is expected to have a graduate level in probability theory and statistics. This book is of interest to PhD students in probability and statistics or operators theory as well as for researchers in all these fields. The series of lecture notes from the Saint-Flour Probability Summer School can be considered as an encyclopedia of probability theory and related fields.

Mathematical statistics --- Probabiliteit--Theorie --- Probabiliteitstheorie --- Probabilities --- Probabilité [Théorie de la ] --- Probabilités --- Statistique mathématique --- Waarschijnlijkheid--Theorie --- Waarschijnlijkheidstheorie --- Wiskundige statistiek --- Probabilities. --- Mathematical analysis. --- Analysis (Mathematics). --- Differential geometry. --- Statistics . --- Probability Theory and Stochastic Processes. --- Analysis. --- Differential Geometry. --- Statistical Theory and Methods. --- 517.1 Mathematical analysis --- Mathematical analysis --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Differential geometry --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk

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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.

Quantitative methods (economics) --- Simulation, Méthodes de --- -330.015195 --- Simulation, Méthodes de --- Econometric models --- Economics --- Simulation methods --- Econometric models. --- Simulation methods. --- Modèles économétriques --- Economie --- 330.015195 --- 303.6 --- AA / International- internationaal --- Economic theory --- Political economy --- Social sciences --- Economic man --- Econometrics --- Mathematical models --- Raming : theorie (wiskundige statistiek). Bayesian analysis and inference --- Business, Economy and Management --- Economics - Simulation methods