Econometrics and economic statistics Books

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Book SynopsisIn the memorable words of Ragnar Frisch, econometrics is âa unification of the theoreticalâquantitative and the empiricalâquantitative approach to economic problemsâ. Beginning to take shape in the 1930s and 1940s, econometrics is now recognized as a vital subdiscipline supported by a vastâand still rapidly growingâbody of literature.Edited by a leading researcher in the history of econometrics, this new collection from Routledgeâs Critical Concepts in Economics series brings together in one âmini libraryâ the best and most influential scholarship on the rise of econometrics. The set provides an authoritative one-stop resource to enable users to understand what has shaped econometrics into its current form. With a full index and comprehensive introductions to each volume, newly written by the editor, the collection also provides a synoptic view of many current key debates and issues.

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Book SynopsisThought-provoking and clearly explained, the new edition provides students of international economics and international business with a rigorous explanation of global economic theory and policy, both current trends and historic developments. It explores key models through case studies and review questions, enabling students to challenge the reporting of economic events by press and government alike. Split into 2 parts â International Trade and International Finance â the text explains conceptual building blocks before applying them to current events and controversies. Key issues discussed include: the influence of transportation costs economies of scale and the new economic geography the evaluation of preferential trade agreements european Economic and Monetary Union the integration of international financial markets international financial crises, China and other emerging economies. Fully illustrated with tables and figures to allow students to visualise the issues discussed, the lively prose gives this book a refreshing approach. An accompanying website also provides context and coverage of the international financial crisis of October 2008, including the so-called âcredit crunchâ and the collapse of some banking institutions.Table of Contents1. An Introduction: Life in an International Economy Part 1: International Trade and Trade Policy 2. Why Do Nations Trade? Some Early Answers 3. Why Do Nations Trade? Some Later Answers 4. Trade and the Role of Factor Endowments 5. Scale, Competition and Trade 6. The Theory of Protection Tariffs and Other Barriers to Trade 7. Arguments for Protection and the Political Economy of Trade Policy 8. International Mobility of Labor and Capital 9. Regional Blocs: Preferential Trade Liberalization 10. Commercial Policy and the WTO 11. Trade and Growth Part 2: International Finance and Open Economy Macroeconomics 12. The Balance of Payments and the Exchange Rate 13. Fundamentals of Exchange Rate Systems 14. An Introduction to Modeling the Open Economy 15. Extensions of the Basic Open Economy Model: Policy Effectiveness and the Large Open Economy 16. International Capital Markets 17. Modelling International Capital Markets 18. Policy Under Fixed Exchange Rates 19. Policy under Flexible Exchange Rates and Extensions of the Mundell-Fleming Model 20. Perfectly Flexible Prices and Exchange Rate Dynamics 21. The International Monetary System: A Brief History 22. European Monetary Integration 23. International Financial Crises

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Book SynopsisThe English language edition of the successful French publication.The Ichimoku Kinko Hyo trading indicator is an information-rich and extremely reliable tool that can be employed across all time frames. Once you have learned the subtleties of the method and understand its unique system of validating price movements, it will improve your trading.Trading with Ichimoku is a practical handbook explaining the different elements of the Ichimoku system of chart reading, from the description of each of its five lines to their interpretation within a wider process of trading analysis.You will rapidly conclude that even though there are only five lines to look at on Ichimoku charts, the information given is more than enough to achieve a detailed and broad view of market and what the price action reveals.Part 1 is devoted to the theoretical description of the various components making up Ichimoku.Part 2 explains how to trade with Ichimoku Kinko Hyo through several examples in various time frames.Part 3 introduces trading methods that combine classical trading tools with Ichimoku Kinko Hyo.Explanations and examples are illustrated throughout with detailed colour charts.Whether you are a beginner or an accomplished trader, you should add a knowledge of Ichimoku to your armoury to improve your analysis and your results.Reviews from French readers:?I highly recommend this book for anyone who wants to learn to use Ichimoku.??Clear explanations and especially great tips on how to trade.??Very informative book with clear and precise examples.??Good balance between the theory, analysis and trading.?Table of ContentsAbout the authorIntroductionPart 1: Ichimoku Theory1. Theory2. Reading Ichimoku charts3. AnalysisPart 2: Ichimoku in Practice4. Trading5. Advanced techniquesPart 3: Ichimoku and Other Indicators6. FibonacciPart 4: The Art of Disciplined Trading7. Conclusion

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Book SynopsisA well-researched and vital study offering a systematic framework to explain the optimal use of exhaustible natural resources, written by a former Minister of Finance in Saudi Arabia, and an authority on the economic development strategy of the country. A critical text for energy economists, policy-makers and diplomats.

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Book SynopsisDr. Bill Carlson is professor emeritus of economics at St. Olaf College, where he taught for 31 years, serving several times as department chair and in various administrative functions, including director of academic computing. He has also held leave assignments with the U.S. government and the University of Minnesota in addition to lecturing at many different universities. He was elected an honorary member of Phi Beta Kappa. In addition, he spent 10 years in private industry and contract research prior to beginning his career at St. Olaf. His education includes engineering degrees from Michigan Technological University (BS) and from the Illinois Institute of Technology (MS) and a PhD in quantitative management from the Rackham Graduate School at the University of Michigan. Numerous research projects related to management, highway safety, and statistical education have produced more than50 publications. He received the Metropolitan Insurance Award of Merit for Safet

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Book SynopsisWinner of the 2017 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes. Adversarial Risk Analysis develops methods for allocating defensive or offensive resources against intelligent adversaries. Many examples throughout illustrate the application of the ARA approach to a variety of games and strategic situations.Focuses on the recent subfield of decision analysis, ARA Compares ideas from decision theory and game theoryUses multi-agent influence diagrams (MAIDs) throughout to help readers visualize complex information structuresApplies the ARA approach to simultaneous games, auctions, sequential games, and defend-attack gamesContains an extended case study based on a real application in railway security, whichTrade Review"This well-written and concise text is an introduction to the field of adversarial risk analysis (ARA), which is a form of decision and risk analysis which incorporates uncertainty and game theory to model strategies of an adversary…There is an appropriate amount of detail throughout the book, making it suitable for a reference text as well as a book which may be read cover to cover and it is both thought provoking and enlightening."—Matthew Craven, Plymouth University, Journal of the Royal Statistical Society, Series A, January 2017 "Here, Banks (Duke Univ.), Rios (IBM), and Insua (ICMAT-CSIC, Spain) identify three categories of uncertainty for the strategist: aleatory uncertainty—nondeterminism of outcomes even after players make choices; epistemic uncertainty—hidden information concerning opponents' preferences, beliefs, and capabilities; and concept uncertainty—hidden information concerning opponents' strategies. Adversarial risk analysis, a new field with roots in modern efforts to defeat terrorism, provides a framework, in principle, to cope with these uncertainties. Solving the models seems generally intractable, but the heart of the book, the first of its kind, offers exemplary case studies. Summing up: Recommended. Lower-division undergraduates and above; informed general audiences."—D. V. Feldman, University of New Hampshire, Durham, USA, for CHOICE, March 2016 Table of ContentsGames and Decisions. Simultaneous Games. Auctions. Sequential Games. Variations on Sequential Defend-Attack Games. A Security Case Study. Other Issues. Solutions to Selected Exercises. References. Index.

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Book SynopsisFrom the Foreword:Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies.---Sartaj Sahni, University of Florida, USABig Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seaTable of ContentsBig Data Management. Big Data Design, implementation, evaluation and services. Big Data as integration of technologies. Big Data analytics and visualization. Query processing and indexing. Elasticity for data management systems. Self-adaptive and energy-efficient mechanisms. Performance evaluation. Security, privacy, trust, data ownership and risk simulations. Processing. Techniques, algorithms and innovative methods of processing. Business and economic models. Adoption cases, frameworks and user evaluations. Data-intensive and scalable computing on hybrid infrastructures. MapReduce based computations. Many-Task Computing in the Cloud. Streaming and real-time processing. Big Data systems and applications for multidisciplinary applications.

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Book SynopsisHealth Econometrics Using Stata by Partha Deb, Edward C. Norton, and Willard G. Manning provides an excellent overview of the methods used to analyze data on healthcare expenditure and use. Aimed at researchers, graduate students, and practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results. Each method is discussed in the context of an example using an extract from the Medical Expenditure Panel Survey.After the overview chapters, the book provides excellent introductions to a series of topics aimed specifically at those analyzing healthcare expenditure and use data. The basic topics of linear regression, the generalized linear model, and log and Box-Cox models are covered with a tight focus on the problems presented by these data. Using this foundation, the authors cover the more advanced topics of models for continuous outcome with mass points, count models, and models for heterogeneous effects. Finally, they discuss endogeneity and how to address inference questions using data from complex surveys.The authors use their formidable experience to guide readers toward useful methods and away from less recommended ones. Their discussion of "health econometric myths" and the chapter presenting a framework for approaching health econometric estimation problems are especially useful for this aspect.Table of ContentsIntroduction Framework MEPS data The linear regression model: Specification and checks Generalized linear models Log and Box–Cox models Models for continuous outcomes with mass at zero Count models Models for heterogeneous effects Endogeneity Design effects

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Book SynopsisCounty and City Extra: Special Decennial Census Edition is an essential single-volume source for Census 2020 information. This edition contains easy-to-read geographic summaries of the United States population by race, Hispanic origin, and housing status. It provides the most up-to-date census data for each state, county, metropolitan area, congressional district, and all cities with a population of 25,000 or more. It complements the popular and trusted County and City Extra: Annual Metro, City, and County Data Book, also published by Bernan Press.Features of this publication include:Census data on all states, counties, metropolitan areas, and congressional districts, as well as on cities and towns with populations above 25,000Key data on over 5,000 geographic areasRanking tables which present each geography type by various subjects Data from previous censuses for comparative purposes Color maps that help the user understand the data

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Book SynopsisWhen you want only one source of information about your city or county, turn to County and City Extra.This trusted reference compiles information from many sources to provide all the key demographic and economic data for every state, county, metropolitan area, congressional district, and for all cities in the United States with a 2010 population of 25,000 or more. In one volume, you can conveniently find data from 1990 to 2021 in easy-to-read tables. The annual updating of County and City Extra for 30 years ensures its stature as a reliable and authoritative source for information. No other resource compiles this amount of detailed information into one place.Subjects covered in County and City Extra include: Population by age and race Government finances Income and poverty Manufacturing, trade, and services Crime Housing Education Immigration and migration Labor force and employment

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Book SynopsisThis book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.Trade Review“‘This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. … It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.’ … The book can be recommended to all readers, who are interested in this field.” (Ludwig Paditz, zbMath 1414.62001, 2019)“This book is a rigorous, mathematically clear and self-contained and quite complete text on time series analysis, suitable both for graduate courses and as a reference book for researchers and users of stochastic temporal models.” (Nazaré Mendes Lopes, Mathematical Reviews, December, 2018)“Beran (Univ. of Konstanz, Germany) presents the mathematical foundations of time series analysis at a level suitable for advanced graduate students and researchers in statistics. The presentation is extremely concise … . the book gives definitions, theorems, and proofs, along with a few exercises and solutions. … it may be useful to graduate students and researchers as a reference.” (B. Borchers, Choice, Vol. 56 (03), November, 2018)​Table of Contents1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 What is a time series? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Time series versus iid data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Typical assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1 Fundamental properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1.1 Ergodic property with a constant limit . . . . . . . . . . . . . . . . . . . 52.1.2 Strict Stationarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.1.3 Weak Stationarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.1.4 Weak stationarity and Hilbert spaces . . . . . . . . . . . . . . . . . . . . 92.1.5 Ergodic processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.1.6 Sufficient conditions for the a.s. ergodic property with a constant limit. . . . . . . . . . . 262.1.7 Sufficient conditions for the L2-ergodic property with a constant limit . .. . . . .. . . 272.2 Specific assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.2.1 Gaussian processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.2.2 Linear processes in L2(Ω) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.2.3 Linear processes with E(X2t ) = ∞ . . . . . . . . . . . . . . . . . . . . . . 342.2.4 Multivariate linear processes . . . . . . . . . . . . . . . . . . . . . . . . . . . 372.2.5 Invertibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382.2.6 Restrictions on the dependence structure . . . . . . . . . . . . . . . . . 493 Defining probability measures for time series . . . . . . . . . . . . . . . . . . . . . . 553.1 Finite dimensional distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553.2 Transformations and equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563.3 Conditions on the expected value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573.4 Conditions on the autocovariance function . . . . . . . . . . . . . . . . . . . . . . 583.4.1 Positive semidefinite functions . . . . . . . . . . . . . . . . . . . . . . . . . 593.4.2 Spectral distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.4.3 Calculation and properties of F and f . . . . . . . . . . . . . . . . .4 Spectral representation of univariate time series . . . . . . . . . . . . . . . . . . . 814.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 814.2 Harmonic processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 814.3 Extension to general processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 844.3.1 Stochastic integrals with respect to Z . . . . . . . . . . . . . . . . . . . . 844.3.2 Existence and definition of Z . . . . . . . . . . . . . . . . . . . . . . . . . . 894.3.3 Interpretation of the spectral representation . . . . . . . . . . . . . . 974.4 Further properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984.4.1 Relationship between ReZ and ImZ . . . . . . . . . . . . . . . . . . . . 984.4.2 Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994.4.3 Overtones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994.4.4 Why are frequencies restricted to the range [-π,π]? . . . . . . . 1004.5 Linear filters and the spectral representation . . . . . . . . . . . . . . . . . . . . 1034.5.1 Effect on the spectral representation . . . . . . . . . . . . . . . . . . . . . 1034.5.2 Elimination of Frequency Bands . . . . . . . . . . . . . . . . . . . . . . . 1075 Spectral representation of real valued vector time series . . . . . . . . . . . . 1095.1 Cross-spectrum and spectral representation . . . . . . . . . . . . . . . . . . . . . 1095.2 Coherence and phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1166 Univariate ARMA processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1276.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1276.2 Stationary solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1276.3 Causal stationary solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1316.4 Causal invertible stationary solution . . . . . . . . . . . . . . . . . . . . . . . . . . . 1336.5 Autocovariances of ARMA processes . . . . . . . . . . . . . . . . . . . . . . . . . . 1346.5.1 Calculation by integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1346.5.2 Calculation using the autocovariance generating function . . . 1356.5.3 Calculation using the Wold representation . . . . . . . . . . . . . . . 1386.5.4 Recursive calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1396.5.5 Asymptotic decay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1406.6 Integrated, seasonal and fractional ARMA and ARIMA processes . . 1476.6.1 Integrated processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1476.6.2 Seasonal ARMA processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1476.6.3 Fractional ARIMA processes . . . . . . . . . . . . . . . . . . . . . . . . . . 1486.7 Unit roots, spurious correlation, cointegration . . . . . . . . . . . . . . . . . . . 1597 Generalized autoregressive processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1637.1 Definition of generalized autoregressive processes . . . . . . . . . . . . . . . 1637.2 Stationary solution of generalized autoregressive equations . . . . . . . . 1647.3 Definition of VARMA processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1687.4 Stationary solution of VARMA equations . . . . . . . . . . . . . . . . . . . . . . 1697.5 Definition of GARCH processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1717.6 Stationary solution of GARCH equations . . . . . . . . . . . . . . . . . . . . . . . 1727.7 Definition of ARCH(∞) processes . . . . . . . . . . . . . . . . . . . . .7.8 Stationary solution of ARCH(∞) equations . . . . . . . . . . . . . . . . . . . . . 1778 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1818.1 Best linear prediction given an infinite past . . . . . . . . . . . . . . . . . . . . . 1818.2 Predictability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1828.3 Construction of the Wold decomposition from f . . . . . . . . . . . . . . . . . 1878.4 Best linear prediction given a finite past . . . . . . . . . . . . . . . . . . . . . . . . 1909 Inference for µ, γ and F . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1959.1 Location estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1959.2 Linear regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1979.3 Nonparametric estimation of γ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2059.4 Nonparametric estimation of f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21110 Parametric estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22710.1 Gaussian and quasi maximum likelihood estimation . . . . . . . . . . . . . . 22710.2 Whittle approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22910.3 Autoregressive approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23210.4 Model choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

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Book SynopsisNon-extensive Entropy Econometrics for Low Frequency Series provides a new and robust power-law-based, non-extensive entropy econometrics approach to the economic modelling of ill-behaved inverse problems. Particular attention is paid to national account-based general equilibrium models known for their relative complexity.In theoretical terms, the approach generalizes Gibbs-Shannon-Golan entropy models, which are useful for describing ergodic phenomena. In essence, this entropy econometrics approach constitutes a junction of two distinct concepts: Jayne’s maximum entropy principle and the Bayesian generalized method of moments. Rival econometric techniques are not conceptually adapted to solving complex inverse problems or are seriously limited when it comes to practical implementation. Recent literature showed that amplitude and frequency of macroeconomic fluctuations do not substantially diverge from many other extreme events, natural or human-related, once they are explained in the same time (or space) scale. Non-extensive entropy is a precious device for econometric modelling even in the case of low frequency series, since outputs evolving within the Gaussian attractor correspond to the Tsallis entropy limiting case of Tsallis q-parameter around unity. This book introduces a sub-discipline called Non-extensive Entropy Econometrics or, using a recent expression, Superstar Generalised Econometrics. It demonstrates, using national accounts-based models, that this approach facilitates solving nonlinear, complex inverse problems, previously considered intractable, such as the constant elasticity of substitution class of functions. This new proposed approach could extend the frontier of theoretical and applied econometrics.

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Book SynopsisThis text disputes the laissez-faire direction of both economic theory and practice that has gained prominence since the mid-1970s. Dissenting voices, the author argues, have been drowned out by a sea of circular arguments and complex mathematical models that ignore real-world conditions.

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Book SynopsisVolume 32 of the NBER Macroeconomics Annual features six theoretical and empirical studies of important issues in contemporary macroeconomics, and a keynote address by former IMF chief economist Olivier Blanchard. In one study, SeHyoun Ahn, Greg Kaplan, Benjamin Moll, Thomas Winberry, and Christian Wolf examine the dynamics of consumption expenditures in non-representative-agent macroeconomic models. In another, John Cochrane asks which macro models most naturally explain the post-financial-crisis macroeconomic environment, which is characterized by the co-existence of low and nonvolatile inflation rates, near-zero short-term interest rates, and an explosion in monetary aggregates. Manuel Adelino, Antoinette Schoar, and Felipe Severino examine the causes of the lending boom that precipitated the recent U.S. financial crisis and Great Recession. Steven Durlauf and Ananth Seshadri investigate whether increases in income inequality cause lower levels of economic mobility and opportunity. Charles Manski explores the formation of expectations, considering the efficacy of directly measuring beliefs through surveys as an alternative to making the assumption of rational expectations. In the final research paper, Efraim Benmelech and Nittai Bergman analyze the sharp declines in debt issuance and the evaporation of market liquidity that coincide with most financial crises. Blanchard's keynote address discusses which distortions are central to understanding short-run macroeconomic fluctuations.

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Book SynopsisDemographic studies help make sense of key aspects of the economy, offering insight into trends in fertility, mortality, immigration, and labor force participation, as well as age, gender, and race-specific trends in health and disability. This book explores the connections between demography and economics.

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Book SynopsisThis groundbreaking book extends traditional approaches of risk measurement and portfolio optimization by combining distributional models with risk or performance measures into one framework.Table of ContentsPreface xiii Acknowledgments xv About the Authors xvii Chapter 1 Concepts of Probability 1 1.1 Introduction 1 1.2 Basic Concepts 2 1.3 Discrete Probability Distributions 2 1.3.1 Bernoulli Distribution 3 1.3.2 Binomial Distribution 3 1.3.3 Poisson Distribution 4 1.4 Continuous Probability Distributions 5 1.4.1 Probability Distribution Function, Probability Density Function, and Cumulative Distribution Function 5 1.4.2 The Normal Distribution 8 1.4.3 Exponential Distribution 10 1.4.4 Student’s t-distribution 11 1.4.5 Extreme Value Distribution 12 1.4.6 Generalized Extreme Value Distribution 12 1.5 Statistical Moments and Quantiles 13 1.5.1 Location 13 1.5.2 Dispersion 13 1.5.3 Asymmetry 13 1.5.4 Concentration in Tails 14 1.5.5 Statistical Moments 14 1.5.6 Quantiles 16 1.5.7 Sample Moments 16 1.6 Joint Probability Distributions 17 1.6.1 Conditional Probability 18 1.6.2 Definition of Joint Probability Distributions 19 1.6.3 Marginal Distributions 19 1.6.4 Dependence of Random Variables 20 1.6.5 Covariance and Correlation 20 1.6.6 Multivariate Normal Distribution 21 1.6.7 Elliptical Distributions 23 1.6.8 Copula Functions 25 1.7 Probabilistic Inequalities 30 1.7.1 Chebyshev’s Inequality 30 1.7.2 Fréchet-Hoeffding Inequality 31 1.8 Summary 32 Chapter 2 Optimization 35 2.1 Introduction 35 2.2 Unconstrained Optimization 36 2.2.1 Minima and Maxima of a Differentiable Function 37 2.2.2 Convex Functions 40 2.2.3 Quasiconvex Functions 46 2.3 Constrained Optimization 48 2.3.1 Lagrange Multipliers 49 2.3.2 Convex Programming 52 2.3.3 Linear Programming 55 2.3.4 Quadratic Programming 57 2.4 Summary 58 Chapter 3 Probability Metrics 61 3.1 Introduction 61 3.2 Measuring Distances: The Discrete Case 62 3.2.1 Sets of Characteristics 63 3.2.2 Distribution Functions 64 3.2.3 Joint Distribution 68 3.3 Primary, Simple, and Compound Metrics 72 3.3.1 Axiomatic Construction 73 3.3.2 Primary Metrics 74 3.3.3 Simple Metrics 75 3.3.4 Compound Metrics 84 3.3.5 Minimal and Maximal Metrics 86 3.4 Summary 90 3.5 Technical Appendix 90 3.5.1 Remarks on the Axiomatic Construction of Probability Metrics 91 3.5.2 Examples of Probability Distances 94 3.5.3 Minimal and Maximal Distances 99 Chapter 4 Ideal Probability Metrics 103 4.1 Introduction 103 4.2 The Classical Central Limit Theorem 105 4.2.1 The Binomial Approximation to the Normal Distribution 105 4.2.2 The General Case 112 4.2.3 Estimating the Distance from the Limit Distribution 118 4.3 The Generalized Central Limit Theorem 120 4.3.1 Stable Distributions 120 4.3.2 Modeling Financial Assets with Stable Distributions 122 4.4 Construction of Ideal Probability Metrics 124 4.4.1 Definition 125 4.4.2 Examples 126 4.5 Summary 131 4.6 Technical Appendix 131 4.6.1 The CLT Conditions 131 4.6.2 Remarks on Ideal Metrics 133 Chapter 5 Choice under Uncertainty 139 5.1 Introduction 139 5.2 Expected Utility Theory 141 5.2.1 St. Petersburg Paradox 141 5.2.2 The von Neumann–Morgenstern Expected Utility Theory 143 5.2.3 Types of Utility Functions 145 5.3 Stochastic Dominance 147 5.3.1 First-Order Stochastic Dominance 148 5.3.2 Second-Order Stochastic Dominance 149 5.3.3 Rothschild-Stiglitz Stochastic Dominance 150 5.3.4 Third-Order Stochastic Dominance 152 5.3.5 Efficient Sets and the Portfolio Choice Problem 154 5.3.6 Return versus Payoff 154 5.4 Probability Metrics and Stochastic Dominance 157 5.5 Summary 161 5.6 Technical Appendix 161 5.6.1 The Axioms of Choice 161 5.6.2 Stochastic Dominance Relations of Order n 163 5.6.3 Return versus Payoff and Stochastic Dominance 164 5.6.4 Other Stochastic Dominance Relations 166 Chapter 6 Risk and Uncertainty 171 6.1 Introduction 171 6.2 Measures of Dispersion 174 6.2.1 Standard Deviation 174 6.2.2 Mean Absolute Deviation 176 6.2.3 Semistandard Deviation 177 6.2.4 Axiomatic Description 178 6.2.5 Deviation Measures 179 6.3 Probability Metrics and Dispersion Measures 180 6.4 Measures of Risk 181 6.4.1 Value-at-Risk 182 6.4.2 Computing Portfolio VaR in Practice 186 6.4.3 Backtesting of VaR 192 6.4.4 Coherent Risk Measures 194 6.5 Risk Measures and Dispersion Measures 198 6.6 Risk Measures and Stochastic Orders 199 6.7 Summary 200 6.8 Technical Appendix 201 6.8.1 Convex Risk Measures 201 6.8.2 Probability Metrics and Deviation Measures 202 Chapter 7 Average Value-at-Risk 207 7.1 Introduction 207 7.2 Average Value-at-Risk 208 7.3 AVaR Estimation from a Sample 214 7.4 Computing Portfolio AVaR in Practice 216 7.4.1 The Multivariate Normal Assumption 216 7.4.2 The Historical Method 217 7.4.3 The Hybrid Method 217 7.4.4 The Monte Carlo Method 218 7.5 Backtesting of AVaR 220 7.6 Spectral Risk Measures 222 7.7 Risk Measures and Probability Metrics 224 7.8 Summary 227 7.9 Technical Appendix 227 7.9.1 Characteristics of Conditional Loss Distributions 228 7.9.2 Higher-Order AVaR 230 7.9.3 The Minimization Formula for AVaR 232 7.9.4 AVaR for Stable Distributions 235 7.9.5 ETL versus AVaR 236 7.9.6 Remarks on Spectral Risk Measures 241 Chapter 8 Optimal Portfolios 245 8.1 Introduction 245 8.2 Mean-Variance Analysis 247 8.2.1 Mean-Variance Optimization Problems 247 8.2.2 The Mean-Variance Efficient Frontier 251 8.2.3 Mean-Variance Analysis and SSD 254 8.2.4 Adding a Risk-Free Asset 256 8.3 Mean-Risk Analysis 258 8.3.1 Mean-Risk Optimization Problems 259 8.3.2 The Mean-Risk Efficient Frontier 262 8.3.3 Mean-Risk Analysis and SSD 266 8.3.4 Risk versus Dispersion Measures 267 8.4 Summary 274 8.5 Technical Appendix 274 8.5.1 Types of Constraints 274 8.5.2 Quadratic Approximations to Utility Functions 276 8.5.3 Solving Mean-Variance Problems in Practice 278 8.5.4 Solving Mean-Risk Problems in Practice 279 8.5.5 Reward-Risk Analysis 281 Chapter 9 Benchmark Tracking Problems 287 9.1 Introduction 287 9.2 The Tracking Error Problem 288 9.3 Relation to Probability Metrics 292 9.4 Examples of r.d. Metrics 296 9.5 Numerical Example 300 9.6 Summary 304 9.7 Technical Appendix 304 9.7.1 Deviation Measures and r.d. Metrics 305 9.7.2 Remarks on the Axioms 305 9.7.3 Minimal r.d. Metrics 307 9.7.4 Limit Cases of L∗p(X, Y) and Θ∗p(X, Y) 310 9.7.5 Computing r.d. Metrics in Practice 311 Chapter 10 Performance Measures 317 10.1 Introduction 317 10.2 Reward-to-Risk Ratios 318 10.2.1 RR Ratios and the Efficient Portfolios 320 10.2.2 Limitations in the Application of Reward-to-Risk Ratios 324 10.2.3 The STARR 325 10.2.4 The Sortino Ratio 329 10.2.5 The Sortino-Satchell Ratio 330 10.2.6 A One-Sided Variability Ratio 331 10.2.7 The Rachev Ratio 332 10.3 Reward-to-Variability Ratios 333 10.3.1 RV Ratios and the Efficient Portfolios 335 10.3.2 The Sharpe Ratio 337 10.3.3 The Capital Market Line and the Sharpe Ratio 340 10.4 Summary 343 10.5 Technical Appendix 343 10.5.1 Extensions of STARR 343 10.5.2 Quasiconcave Performance Measures 345 10.5.3 The Capital Market Line and Quasiconcave Ratios 353 10.5.4 Nonquasiconcave Performance Measures 356 10.5.5 Probability Metrics and Performance Measures 357 Index 361

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Book SynopsisAutoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empirical background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be implemented using econometric software, model construction, fitting and forecasting and model evaluation and selection. Key Features: Presents a comprehensive overview of both the theory and the practical applications of ARCH, an increasingly popular financial modelling technique. Assumes no prior knowledge of ARCH models; the basics such as model construction are introduced, before proceeding to more complex applications such as value-at-risk, option pricing and model evaluation. Uses empirical examples to demonstrate how the recent developments inTrade Review"Numerous articles on the Autoregressive Conditional Heteroskedastic (ARCH) process, an increasingly popular financial modeling technique, exist in various international journals. Now Xekalaki and Degiannakis (both statistics, Athens U. of Economics and Business, Greece) provide a thorough treatment of the ARCH theory and its practical applications, in a textbook for postgraduate and final-year undergraduate students which could serve as reference work for academics and financial market professionals." (Book News Inc, November 2010) Table of ContentsPrologue. Notation. 1 What is an ARCH process? 1.1 Introduction. 1.2 The Autoregressive Conditionally Heteroskedastic Process. 1.3 The Leverage Effect. 1.4 The Non-trading Period Effect. 1.5 Non-synchronous Trading Effect. 1.6 The Relationship between Conditional Variance and Conditional Mean. 2 ARCH Volatility Specifications. 2.1 Model Specifications. 2.2 Methods of Estimation. 2.3. Estimating the GARCH Model with EViews 6: An Empirical Example.. 2.4. Asymmetric Conditional Volatility Specifications. 2.5. Simulating ARCH Models Using EViews. 2.6. Estimating Asymmetric ARCH Models with G@RCH 4.2 OxMetrics – An Empirical Example.. 2.7. Misspecification Tests. 2.8 Other ARCH Volatility Specifications. 2.9 Other Methods of Volatility Modeling. 2.10 Interpretation of the ARCH Process. 3 Fractionally Integrated ARCH Models. 3.1 Fractionally Integrated ARCH Model Specifications. 3.2 Estimating Fractionally Integrated ARCH Models Using G@RCH 4.2 OxMetrics – An Empirical Example. 3.3 A More Detailed Investigation of the Normality of the Standardized Residuals – Goodness-of-fit Tests. 4 Volatility Forecasting: An Empirical Example Using EViews 6. 4.1 One-step-ahead Volatility Forecasting. 4.2 Ten-step-ahead Volatility Forecasting. 5 Other Distributional Assumptions. 5.1 Non-Normally Distributed Standardized Innovations. 5.2 Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using G@RCH 4.2 OxMetrics – An Empirical Example. 5.3 Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using EViews 6 – An Empirical Example. 5.4 Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using EViews 6 – The LogL Object. 6 Volatility Forecasting: An Empirical Example Using G@RCH Ox. 7 Intra-Day Realized Volatility Models. 7.1 Realized Volatility. 7.2 Intra-Day Volatility Models. 7.3 Intra-Day Realized Volatility & ARFIMAX Models in G@RCH 4.2 OxMetrics – An Empirical example. 8 Applications in Value-at-Risk, Expected Shortfalls, Options Pricing. 8.1 One-day-ahead Value-at-Risk Forecasting. 8.2 One-day-ahead Expected Shortfalls Forecasting. 8.3 FTSE100 Index: One-step-ahead Value-at-Risk and Expected Shortfall Forecasting. 8.4 Multi-period Value-at-Risk and Expected Shortfalls Forecasting. 8.5 ARCH Volatility Forecasts in Black and Scholes Option Pricing. 8.6 ARCH Option Pricing Formulas. 9 Implied Volatility Indices and ARCH Models. 9.1 Implied Volatility. 9.2 The VIX Index. 9.3 The Implied Volatility Index as an Explanatory Variable. 9.4 ARFIMAX Modeling for Implied Volatility Index. 10 ARCH Model Evaluation and Selection. 10.1 Evaluation of ARCH Models. 10.2 Selection of ARCH Models. 10.3 Application of Loss Functions as Methods of Model Selection.. 10.4 The SPA Test for VaR and Expected Shortfalls. 11 Multivariate ARCH Models. 11.1 Model Specifications. 11.2 Maximum Likelihood Estimation. 11.3 Estimating Multivariate ARCH Models Using EViews 6. 11.4 Estimating Multivariate ARCH Models Using G@RCH 5.0. 11.5 Evaluation of Multivariate ARCH Models. References. Author Index. Subject Index.

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Book SynopsisThe text has been thoroughly updated to incorporate recent developments and includes three major new chapters on: time series modelling in the financial economics area, the Harvey approach to structural time series modelling and cointegration, and panel data models and non--stationary time series.Table of ContentsPreface. 1. Introduction and Overview. Some Initial Concepts. Forecasting. Outline of the Book. 2. Short- and Long-run Models. Long-run Models. Stationary and Non-stationary Time Series. Spurious Regressions. Cointegration. Short-run Models. Conclusion. 3. Testing for Unit Roots. The Dickey–Fuller Test. Augmented Dickey–Fuller Test. Power and Level of Unit Root Tests. Structural Breaks and Unit Root Tests. Seasonal Unit Roots. Structural Breaks and Seasonal Unit Root Tests. Periodic Integration and Unit Root-testing. Conclusion on Unit Root Tests. 4. Cointegration in Single Equations. The Engle–Granger (EG) Approach. Testing for Cointegration with a Structural Break. Alternative Approaches. Problems with the Single Equation Approach. Estimating the Short-run Dynamic Model. Seasonal Cointegration. Periodic Cointegration. Asymmetric Tests for Cointegration. Conclusion s. 5. Cointegration in Multivariate Systems. The Johansen Approach. Testing the Order of Integration of the Variables. Formulation of the Dynamic Model. Testing for Reduced Rank. Deterministic Components in the Multivariate Model. Testing of Weak Exogeneity and VECM with Exogenous I (l) Variables. Testing for Linear Hypotheses on Cointegration Relations. Testing for Unique Cointegration Vectors. Joint Tests of Restrictions on α and β Seasonal Unit Roots. Seasonal Cointegration. Conclusions. Appendix 1: Programming in SHAZAM. 6. Modelling the Short-run Multivariate System. Introduction. Estimating the Long-run Cointegration Relationships. Parsimonious VECM. Conditional PVECM. Structural Modelling. Structural Macroeconomic Modelling. 7. Panel Data Models and Cointegration. Introduction. Panel Data and Modelling Techniques. Panel Unit Root Tests. Testing for Cointegration in Panels. Estimating Panel Cointegration Models. Conclusion on Testing for Unit Roots and Cointegration in Panel Data. 8. Modelling and Forecasting Financial Times Series. Introduction. ARCH and GARCH. Multivariate GARCH. Estimation and Testing. An Empirical Application of ARCH and GARCH Models. ARCH-M. Asymmetric GARCH Models. Integrated and Fractionally Integrated GARCH Models. Conditional Heteroscedasticity, Unit Roots and Cointegration. Forecasting with GARCH Models. Further Methods for Forecast Evaluation. Conclusions on Modelling and Forecasting Financial Time Series. Appendix: Cointegration Analysis Using the Johansen Technique: A Practitioner’s Guide to PcGive 10.1. Statistical Appendix. References. Index.

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