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The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling.

Mathematical statistics --- AA / International- internationaal --- 305.970 --- 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. --- Econometrics --- Finance --- Time-series analysis --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Probabilities --- Economics, Mathematical --- Statistics --- Mathematical models --- 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 --- Finance - Mathematical models --- Business, Economy and Management --- Economics --- Econometrics. --- Time-series analysis. --- Mathematical models.

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Econometric models are widely used in the creation and evaluation of economic policy in the public and private sectors. But these models are useful only if they adequately account for the phenomena in question, and they can be quite misleading if they do not. In response, econometricians have developed tests and other checks for model adequacy. All of these methods, however, take as given the specification of the model to be tested. In this book, John Geweke addresses the critical earlier stage of model development, the point at which potential models are inherently incomplete. Summarizing and extending recent advances in Bayesian econometrics, Geweke shows how simple modern simulation methods can complement the creative process of model formulation. These methods, which are accessible to economics PhD students as well as to practicing applied econometricians, streamline the processes of model development and specification checking. Complete with illustrations from a wide variety of applications, this is an important contribution to econometrics that will interest economists and PhD students alike.

Econometric models --- AA / International- internationaal --- 330.3 --- 303.8 --- 305.970 --- Methode in staathuishoudkunde. Statische, dynamische economie. Modellen. Experimental economics. --- Econometrische behandeling van een onderwerp. --- 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. --- -330.015195 --- Econometrics --- Mathematical models --- Electronic information resources --- E-books --- Econometric models. --- Economic Theory --- Business & Economics --- Econometrics. --- Economics, Mathematical --- Statistics --- Econometrische behandeling van een onderwerp --- 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 --- Methode in staathuishoudkunde. Statische, dynamische economie. Modellen. Experimental economics

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This volume is dedicated to two recent intensive areas of research in the econometrics of panel data, namely nonstationary panels and dynamic panels. It includes a comprehensive survey of the nonstationary panel literature including panel unit root tests, spurious panel regressions and panel cointegration tests. In addition, it provides recent developments in the estimation of dynamic panel data models using generalized method of moments. The volume includes eleven chapters written by twenty authors. These chapters: investigate better methods of estimating dynamic panels; develop methods for estimating and testing hypotheses for cointegrating vectors in dynamic panels; extend the concept of serial correlation common features analysis to nonstationary panel data models; study the local power of panel unit root test statistics; derive the asymptotic distributions of various estimators for the panel cointegrated regression model; propose a unit root test in the presence of structural change; develop a new limit theory for panel data that may be cross-sectionally heterogeneous; propose stationarity tests for a heterogeneous panel data model; derive instrumental variable estimators for a semiparametric partially linear dynamic panel data model; and conduct Monte Carlo experiments to study the small sample properties of a growth convergence equation. This collection of papers should prove useful for practitioners and researchers working with panel data.

330.115 --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- 330.115 Econometrie --- Econometrie --- Modeles econometriques. --- Panel (Psychologie sociale). --- 304.8 --- Panel analysis --- Panel studies --- Steekproeftheorie. --- Operational research. Game theory --- Quantitative methods (economics) --- Recherche. --- AA / International- internationaal --- 305.970 --- 303.6 --- Econometrics --- 330.015195 --- Social sciences --- Statistics --- Economics, Mathematical --- 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. --- Methodology --- Econometric models. --- Panel analysis. --- Mathematical models --- Research. --- 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 --- Steekproeftheorie --- Modèles économétriques --- Business & Economics --- Econometrics. --- Econometrie - Recherche.

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"Hayashi's Econometrics 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."

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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Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.

Investments --- Stock price forecasting --- Data mining --- Data processing --- AA / International- internationaal --- 305.970 --- 303.0 --- 301 --- 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. --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken). --- Techniek van statistische inlichtingen. Organisatie van de statistische enquêtes. Statistische kritiek. --- Investments -- Data processing. --- Stock price forecasting -- Data processing. --- Data mining. --- Data processing. --- Data structures (Computer scienc. --- Artificial intelligence. --- Finance. --- Data Structures and Information Theory. --- Artificial Intelligence. --- Finance, general. --- Data structures (Computer science) --- Forecasting, Stock price --- Security price forecasting --- Stocks --- Business forecasting --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Techniek van statistische inlichtingen. Organisatie van de statistische enquêtes. Statistische kritiek --- 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 --- Prices --- Forecasting --- Data structures (Computer science). --- Funding --- Funds --- Economics --- Currency question --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- File organization (Computer science) --- Abstract data types (Computer science) --- Investments - Data processing --- Stock price forecasting - Data processing