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DO YOU WANT TO RECOGNIZE THE MOST SUITABLE MODELS FOR ANALYSIS OF STATISTICAL DATA SETS? THIS BOOK PROVIDES A HANDS–ON PRACTICAL GUIDE TO USING THE MOST SUITABLE MODELS FOR ANALYSIS OF STATISTICAL DATA SETS USING EVIEWS – AN INTERACTIVE WINDOWS–BASED COMPUTER SOFTWARE PROGRAM FOR SOPHISTICATED DATA ANALYSIS, REGRESSION, AND FORECASTING – TO DEFINE AND TEST STATISTICAL HYPOTHESES. RICH IN EXAMPLES AND WITH AN EMPHASIS ON HOW TO DEVELOP ACCEPTABLE STATISTICAL MODELS, TIME SERIES DATA ANALYSIS USING EVIEWS IS A PERFECT COMPLEMENT TO THEORETICAL BOOKS PRESENTING STATISTICAL OR ECONOMETRIC MODELS FOR TIME SERIES DATA. THE PROCEDURES INTRODUCED ARE EASILY EXTENDIBLE TO CROSS–SECTION DATA SETS. THE AUTHOR: PROVIDES STEP–BY–STEP DIRECTIONS ON HOW TO APPLY EVIEWS SOFTWARE TO TIME SERIES DATA ANALYSIS OFFERS GUIDANCE ON HOW TO DEVELOP AND EVALUATE ALTERNATIVE EMPIRICAL MODELS, PERMITTING THE MOST APPROPRIATE TO BE SELECTED WITHOUT THE NEED FOR COMPUTATIONAL FORMULAE EXAMINES A VARIETY OF TIMES SERIES MODELS, INCLUDING CONTINUOUS GROWTH, DISCONTINUOUS GROWTH, SEEMINGLY CAUSAL, REGRESSION, ARCH, AND GARCH AS WELL AS A GENERAL FORM OF NONLINEAR TIME SERIES AND NONPARAMETRIC MODELS GIVES OVER 250 ILLUSTRATIVE EXAMPLES AND NOTES BASED ON THE AUTHOR'S OWN EMPIRICAL FINDINGS, ALLOWING THE ADVANTAGES AND LIMITATIONS OF EACH MODEL TO BE UNDERSTOOD DESCRIBES THE THEORY BEHIND THE MODELS IN COMPREHENSIVE APPENDICES PROVIDES SUPPLEMENTARY INFORMATION AND DATA SETS AN ESSENTIAL TOOL FOR ADVANCED UNDERGRADUATE AND GRADUATE STUDENTS TAKING FINANCE OR ECONOMETRICS COURSES. STATISTICS, LIFE SCIENCES, AND SOCIAL SCIENCE STUDENTS, AS WELL AS APPLIED RESEARCHERS, WILL ALSO FIND THIS BOOK AN INVALUABLE RESOURCE.
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Do you want to recognize the most suitable models for analysis of statistical data sets? This book provides a hands-on practical guide to using the most suitable models for analysis of statistical data sets using EViews - an interactive Windows-based computer software program for sophisticated data analysis, regression, and forecasting - to define and test statistical hypotheses. Rich in examples and with an emphasis on how to develop acceptable statistical models, Time Series Data Analysis Using EViews is a perfect complement to theoretical books presenting statistical or econometric models for time series data. The procedures introduced are easily extendible to cross-section data sets. The author: Provides step-by-step directions on how to apply EViews software to time series data analysisOffers guidance on how to develop and evaluate alternative empirical models, permitting the most appropriate to be selected without the need for computational formulaeExamines a variety of times series models, including continuous growth, discontinuous growth, seemingly causal, regression, ARCH, and GARCH as well as a general form of nonlinear time series and nonparametric modelsGives over 250 illustrative examples and notes based on the author's own empirical findings, allowing the advantages and limitations of each model to be understoodDescribes the theory behind the models in comprehensive appendicesProvides supplementary information and data sets An essential tool for advanced undergraduate and graduate students taking finance or econometrics courses. Statistics, life sciences, and social science students, as well as applied researchers, will also find this book an invaluable resource.Biografía del autor:
I Gusti Ngurah Agung is a Lecturer and Academic Advisor at the Graduate School of Management, Faculty of Economics at the University of Indonesia. He has been teaching mathematical statistics and applied statistics since 1960 at the Makassar Public University as well as Hassanudin University, Makassar, and since 2006 at the Graduate School of Planning, Strategy and Public Policy, University of Indonesia. Agung has authored more than 10 pocket books in applied statistics (in Indonesian). He holds a BSc in Mathematical Education from Hassanudin University, a Masters in Mathematics from the New Mexico State University and a second Masters in mathematical statistics as well as a PhD in biostatistics from the University of North Carolina at Chapel Hill.
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