Modelling Economic Series
Download Modelling Economic Series full books in PDF, epub, and Kindle. Read online free Modelling Economic Series ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Clive William John Granger |
| Publisher | : Oxford University Press |
| Total Pages | : 428 |
| Release | : 1990 |
| Genre | : Business & Economics |
| ISBN | : 9780198287360 |
This is a volume of readings for graduate students, especially those taking courses in applied econometrics, who need to learn how to evaluate the validity of present theories and techniques. The aim of the text is to aid readers in the difficult task of actually constructing models. The essays vary in the degree of technical sophistication used, but each paper intends to provide students with a sound knowledge of the practical difficulties of model specification, evaluation and interpretation, as well as advice on tackling these difficulties.
| Author | : Bent Jesper Christensen |
| Publisher | : Princeton University Press |
| Total Pages | : 508 |
| Release | : 2009 |
| Genre | : Business & Economics |
| ISBN | : 9780691120591 |
Economic Modeling and Inference takes econometrics to a new level by demonstrating how to combine modern economic theory with the latest statistical inference methods to get the most out of economic data. This graduate-level textbook draws applications from both microeconomics and macroeconomics, paying special attention to financial and labor economics, with an emphasis throughout on what observations can tell us about stochastic dynamic models of rational optimizing behavior and equilibrium. Bent Jesper Christensen and Nicholas Kiefer show how parameters often thought estimable in applications are not identified even in simple dynamic programming models, and they investigate the roles of extensions, including measurement error, imperfect control, and random utility shocks for inference. When all implications of optimization and equilibrium are imposed in the empirical procedures, the resulting estimation problems are often nonstandard, with the estimators exhibiting nonregular asymptotic behavior such as short-ranked covariance, superconsistency, and non-Gaussianity. Christensen and Kiefer explore these properties in detail, covering areas including job search models of the labor market, asset pricing, option pricing, marketing, and retirement planning. Ideal for researchers and practitioners as well as students, Economic Modeling and Inference uses real-world data to illustrate how to derive the best results using a combination of theory and cutting-edge econometric techniques. Covers identification and estimation of dynamic programming models Treats sources of error--measurement error, random utility, and imperfect control Features financial applications including asset pricing, option pricing, and optimal hedging Describes labor applications including job search, equilibrium search, and retirement Illustrates the wide applicability of the approach using micro, macro, and marketing examples
| Author | : William R. Bell |
| Publisher | : CRC Press |
| Total Pages | : 544 |
| Release | : 2018-11-14 |
| Genre | : Mathematics |
| ISBN | : 1439846588 |
Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time s
| Author | : Timo Teräsvirta |
| Publisher | : OUP Oxford |
| Total Pages | : 592 |
| Release | : 2010-12-16 |
| Genre | : Business & Economics |
| ISBN | : 9780199587148 |
This book contains an extensive up-to-date overview of nonlinear time series models and their application to modelling economic relationships. It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear time series models. Importantly, it shows the reader how to apply these models in practice. For thispurpose, the building of various nonlinear models with its three stages of model building: specification, estimation and evaluation, is discussed in detail and is illustrated by several examples involving both economic and non-economic data. Since estimation of nonlinear time series models is carried outusing numerical algorithms, the book contains a chapter on estimating parametric nonlinear models and another on estimating nonparametric ones.Forecasting is a major reason for building time series models, linear or nonlinear. The book contains a discussion on forecasting with nonlinear models, both parametric and nonparametric, and considers numerical techniques necessary for computing multi-period forecasts from them. The main focus of the book is on models of the conditional mean, but models of the conditional variance, mainly those of autoregressive conditional heteroskedasticity, receive attention as well. A separate chapter isdevoted to state space models. As a whole, the book is an indispensable tool for researchers interested in nonlinear time series and is also suitable for teaching courses in econometrics and time series analysis.
| Author | : Lars Peter Hansen |
| Publisher | : World Scientific Publishing Company Incorporated |
| Total Pages | : 454 |
| Release | : 2014 |
| Genre | : Business & Economics |
| ISBN | : 9789814578110 |
"Studying this work in real time taught me a lot, but seeing it laid out in conceptual, rather than chronological, order provides even clearer insights into the evolution of this provocative line of research. Hansen and Sargent are two of the best economists of our time, they are also among the most dedicated teachers in our profession. They have once again moved the research frontier, and with this book provide a roadmap for the rest of us to follow. This is a must-have for anyone interested in modeling uncertainty, ambiguity and robustness."Stanley E ZinWilliam R Berkley Professor of Economics and BusinessLeonard N Stern School of BusinessNew York UniversityWritten by Lars Peter Hansen (Nobel Laureate in Economics, 2013) and Thomas Sargent (Nobel Laureate in Economics, 2011), Uncertainty within Economic Models includes articles adapting and applying robust control theory to problems in economics and finance. This book extends rational expectations models by including agents who doubt their models and adopt precautionary decisions designed to protect themselves from adverse consequences of model misspecification. This behavior has consequences for what are ordinarily interpreted as market prices of risk, but big parts of which should actually be interpreted as market prices of model uncertainty. The chapters discuss ways of calibrating agents' fears of model misspecification in quantitative contexts.
| Author | : Philip Hans Franses |
| Publisher | : Cambridge University Press |
| Total Pages | : 421 |
| Release | : 2014-04-24 |
| Genre | : Business & Economics |
| ISBN | : 1139952129 |
With a new author team contributing decades of practical experience, this fully updated and thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time series analysis. Taking a practical and example-driven approach, this textbook summarises the most critical decisions, techniques and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirely new set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends, seasonality, aberrant observations, conditional heteroskedasticity and ARCH models, non-linearity and multivariate time series, making this a complete practical guide. Downloadable datasets are available online.
| Author | : S. Burke |
| Publisher | : Springer |
| Total Pages | : 253 |
| Release | : 2005-06-14 |
| Genre | : Business & Economics |
| ISBN | : 0230005780 |
Co-integration, equilibrium and equilibrium correction are key concepts in modern applications of econometrics to real world problems. This book provides direction and guidance to the now vast literature facing students and graduate economists. Econometric theory is linked to practical issues such as how to identify equilibrium relationships, how to deal with structural breaks associated with regime changes and what to do when variables are of different orders of integration.
| Author | : Andrew Briggs |
| Publisher | : OUP Oxford |
| Total Pages | : 269 |
| Release | : 2006-08-17 |
| Genre | : Medical |
| ISBN | : 0191004952 |
In financially constrained health systems across the world, increasing emphasis is being placed on the ability to demonstrate that health care interventions are not only effective, but also cost-effective. This book deals with decision modelling techniques that can be used to estimate the value for money of various interventions including medical devices, surgical procedures, diagnostic technologies, and pharmaceuticals. Particular emphasis is placed on the importance of the appropriate representation of uncertainty in the evaluative process and the implication this uncertainty has for decision making and the need for future research. This highly practical guide takes the reader through the key principles and approaches of modelling techniques. It begins with the basics of constructing different forms of the model, the population of the model with input parameter estimates, analysis of the results, and progression to the holistic view of models as a valuable tool for informing future research exercises. Case studies and exercises are supported with online templates and solutions. This book will help analysts understand the contribution of decision-analytic modelling to the evaluation of health care programmes. ABOUT THE SERIES: Economic evaluation of health interventions is a growing specialist field, and this series of practical handbooks will tackle, in-depth, topics superficially addressed in more general health economics books. Each volume will include illustrative material, case histories and worked examples to encourage the reader to apply the methods discussed, with supporting material provided online. This series is aimed at health economists in academia, the pharmaceutical industry and the health sector, those on advanced health economics courses, and health researchers in associated fields.
| Author | : Tshilidzi Marwala |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 271 |
| Release | : 2013-04-02 |
| Genre | : Computers |
| ISBN | : 1447150104 |
Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic modeling. However, the inherent limitation of game theory when dealing with many player games encourages the use of multi-agent systems for modeling economic phenomena. The artificial intelligence techniques used to model economic data include: multi-layer perceptron neural networks radial basis functions support vector machines rough sets genetic algorithm particle swarm optimization simulated annealing multi-agent system incremental learning fuzzy networks Signal processing techniques are explored to analyze economic data, and these techniques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and controling inflation, option pricing, modeling economic growth as well as portfolio optimization are examined. The relationship between economic dependency and interstate conflict is explored, and knowledge on how economics is useful to foster peace – and vice versa – is investigated. Economic Modeling Using Artificial Intelligence Methods deals with the issue of causality in the non-linear domain and applies the automatic relevance determination, the evidence framework, Bayesian approach and Granger causality to understand causality and correlation. Economic Modeling Using Artificial Intelligence Methods makes an important contribution to the area of econometrics, and is a valuable source of reference for graduate students, researchers and financial practitioners.
| Author | : Roger B. Myerson |
| Publisher | : MIT Press |
| Total Pages | : 569 |
| Release | : 2019-12-17 |
| Genre | : Business & Economics |
| ISBN | : 0262355604 |
An introduction to the use of probability models for analyzing risk and economic decisions, using spreadsheets to represent and simulate uncertainty. This textbook offers an introduction to the use of probability models for analyzing risks and economic decisions. It takes a learn-by-doing approach, teaching the student to use spreadsheets to represent and simulate uncertainty and to analyze the effect of such uncertainty on an economic decision. Students in applied business and economics can more easily grasp difficult analytical methods with Excel spreadsheets. The book covers the basic ideas of probability, how to simulate random variables, and how to compute conditional probabilities via Monte Carlo simulation. The first four chapters use a large collection of probability distributions to simulate a range of problems involving worker efficiency, market entry, oil exploration, repeated investment, and subjective belief elicitation. The book then covers correlation and multivariate normal random variables; conditional expectation; optimization of decision variables, with discussions of the strategic value of information, decision trees, game theory, and adverse selection; risk sharing and finance; dynamic models of growth; dynamic models of arrivals; and model risk. New material in this second edition includes two new chapters on additional dynamic models and model risk; new sections in every chapter; many new end-of-chapter exercises; and coverage of such topics as simulation model workflow, models of probabilistic electoral forecasting, and real options. The book comes equipped with Simtools, an open-source, free software used througout the book, which allows students to conduct Monte Carlo simulations seamlessly in Excel.
