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Introduction to time series and forecasting.
Authors: ---
ISBN: 9780387953519 0387953515 9786610187829 1280187824 038721657X Year: 2010 Publisher: New York Springer

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Abstract

Some of the key mathematical results are stated without proof in order to make the underlying theory accessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and nonstationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to nonlinear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

Keywords

304.0 --- AA / International- internationaal --- Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen. --- Analyse : reeksen --- Mathematics. --- Computer software. --- Probabilities. --- Statistics. --- Econometrics. --- Mathematical Software. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Time-series analysis --- 519.55 --- tijdreeksanalyse --- 517 --- 519.2 --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Mathematical statistics --- Probabilities --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- 519.24 --- 519.24 Special statistical applications and models --- Special statistical applications and models --- Time-series analysis. --- Mathematical statistics. --- Économétrie --- Séries chronologiques --- Série chronologique --- EPUB-LIV-FT SPRINGER-B --- Distribution (Probability theory. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Économétrie. --- Séries chronologiques. --- Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen --- Statistics . --- Software, Computer --- Computer systems --- Economics, Mathematical --- Statistics --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk

Forecasting, structural time series models and the Kalman filter
Author:
ISBN: 0521405734 0521321964 1139881744 1107713013 1107714559 1107049997 1107715903 1107712661 1107720036 9780521405737 9781107720039 9781107049994 9781107715905 9781107714557 9780521321969 Year: 1990 Publisher: Cambridge Cambridge University press

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In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Unit roots, cointegration, and structural change
Authors: ---
ISBN: 0521587824 0521582571 0511751974 9780521582575 9780521587822 9780511751974 Year: 1998 Publisher: Cambridge Cambridge University Press

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Time series analysis has undergone many changes in recent years with the advent of unit roots and cointegration. Maddala and Kim present a comprehensive review of these important developments and examine structural change. The volume provides an analysis of unit root tests, problems with unit root testing, estimation of cointegration systems, cointegration tests, and econometric estimation with integrated regressors. The authors also present the Bayesian approach to these problems and bootstrap methods for small-sample inference. The chapters on structural change discuss the problems of unit root tests and cointegration under structural change, outliers and robust methods, the Markov-switching model and Harvey's structural time series model. Unit Roots, Cointegration and Structural Change is a major contribution to Themes in Modern Econometrics, of interest both to specialists and graduate and upper-undergraduate students.

The econometric modelling of financial time series.
Authors: ---
ISBN: 9780521883818 9780521710091 0521883814 9780511817380 9780511381034 0511381034 9780511386824 0511386826 9780511649684 0511649681 051181738X 0511574312 9780511574313 052171009X 1107714125 0511384998 9781107714120 9780511384998 Year: 2008 Publisher: Cambridge Cambridge University press

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Terence Mills' best-selling graduate textbook provides detailed coverage of research techniques and findings relating to the empirical analysis of financial markets. In its previous editions it has become required reading for many graduate courses on the econometrics of financial modelling. This third edition, co-authored with Raphael Markellos, contains a wealth of material reflecting the developments of the last decade. Particular attention is paid to the wide range of nonlinear models that are used to analyse financial data observed at high frequencies and to the long memory characteristics found in financial time series. The central material on unit root processes and the modelling of trends and structural breaks has been substantially expanded into a chapter of its own. There is also an extended discussion of the treatment of volatility, accompanied by a new chapter on nonlinearity and its testing.

Keywords

Mathematical statistics --- Quantitative methods (economics) --- Finance --- Time-series analysis --- Stochastic processes --- Econometric models --- Processus stochastiques --- Marché financier --- Séries chronologiques --- Modèles économétriques --- AA / International- internationaal --- 305.970 --- 305.91 --- 330.3 --- 305.971 --- 304.0 --- 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. --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles. --- Methode in staathuishoudkunde. Statische, dynamische economie. Modellen. Experimental economics. --- Speciale gevallen in econometrische modelbouw. --- Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen. --- -Time-series analysis --- 332.015195 --- Random processes --- Probabilities --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Funding --- Funds --- Economics --- Currency question --- 519.2 --- 336.7 --- econometrie --- regressie-analyse --- financiewezen --- stochastische modellen --- tijdreeksanalyse --- Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles --- 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 --- Speciale gevallen in econometrische modelbouw --- Methode in staathuishoudkunde. Statische, dynamische economie. Modellen. Experimental economics --- Processus stochastiques. --- Séries chronologiques. --- Modèles économétriques. --- Time-series analysis. --- Stochastic processes. --- Econometric models. --- Business, Economy and Management --- Finance - Econometric models


Book
Wavelet methods in statistics with R
Author:
ISBN: 9780387759609 9780387759616 0387759603 0387759611 Year: 2008 Publisher: New York, N.Y. Springer

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Wavelet methods have recently undergone a rapid period of development with important implications for a number of disciplines including statistics. This book has three main objectives: (i) providing an introduction to wavelets and their uses in statistics; (ii) acting as a quick and broad reference to many developments in the area; (iii) interspersing R code that enables the reader to learn the methods, to carry out their own analyses, and further develop their own ideas. The book code is designed to work with the freeware R package WaveThresh4, but the book can be read independently of R. The book introduces the wavelet transform by starting with the simple Haar wavelet transform, and then builds to consider more general wavelets, complex-valued wavelets, non-decimated transforms, multidimensional wavelets, multiple wavelets, wavelet packets, boundary handling, and initialization. Later chapters consider a variety of wavelet-based nonparametric regression methods for different noise models and designs including density estimation, hazard rate estimation, and inverse problems; the use of wavelets for stationary and non-stationary time series analysis; and how wavelets might be used for variance estimation and intensity estimation for non-Gaussian sequences. The book is aimed both at Masters/Ph.D. students in a numerate discipline (such as statistics, mathematics, economics, engineering, computer science, and physics) and postdoctoral researchers/users interested in statistical wavelet methods. Guy Nason is Professor of Statistics at the University of Bristol. He has been actively involved in the development of various wavelet methods in statistics since 1993. He was awarded the Royal Statistical Society’s 2001 Guy Medal in Bronze for work on wavelets in statistics. He was the author of the first, free, generally available wavelet package for statistical purposes in S and R (WaveThresh2).

Keywords

Programming --- Mathematical statistics --- Statistics. --- Statistical Theory and Methods. --- Econometrics. --- Bioinformatics. --- Psychometrics. --- Data Mining and Knowledge Discovery. --- Data mining. --- Mathematical statistics. --- Statistique --- Exploration de données (Informatique) --- Bio-informatique --- Statistique mathématique --- Econométrie --- Psychométrie --- Wavelets (Mathematics) --- R (Computer program language) --- Statistics --- Data mining --- Bioinformatics --- Econometrics --- Psychometrics --- R (Computer program language). --- Wavelets (Mathematics). --- Civil & Environmental Engineering --- Mathematics --- Physical Sciences & Mathematics --- Engineering & Applied Sciences --- Operations Research --- Mathematical Statistics --- Logiciel --- Computer software --- Méthode statistique --- Statistical methods --- Modèle mathématique --- Mathematical models --- Traitement des données --- Data processing --- Analyse de séries chronologiques --- Time series analysis --- GNU-S (Computer program language) --- Statistical inference --- Statistics, Mathematical --- Wavelet analysis --- Distribution (Probability theory. --- Probability Theory and Stochastic Processes. --- Probabilities --- Sampling (Statistics) --- Domain-specific programming languages --- Harmonic analysis --- Economics, Mathematical --- Distribution functions --- Frequency distribution --- Characteristic functions --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychology --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Measurement --- Scaling --- Methodology --- Probabilities. --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Probability --- Combinations --- Chance --- Least squares --- Risk

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