In 1956, Solow proposed a neoclassical growth model in opposition or as an alternative to Keynesian growth models. The Solow model of economic growth provided foundations for models embedded in the new theory of economic growth, known as the theory of endogenous growth, such as the renowned growth models developed by Paul M. Romer and Robert E. Lucas in the 1980s and 90s. The augmentations of the Solow model described in this book, excepting the Phelps golden rules of capital accumulation and the Mankiw-Romer-Weil and Nonneman-Vanhoudt models, were developed by the authors over the last two decades. The book identifies six spheres of interest in modern macroeconomic theory: the impact of fiscal and monetary policy on growth; the effect of different returns to scale on production; the influence of mobility of factors of production among different countries on their development; the effect of population dynamics on growth; the periodicity of investment rates and their influence on growth; and the effect of exogenous shocks in the form of an epidemic. For each of these issues, the authors construct and analyze an appropriate growth model that focuses on the description of the specific macroeconomic problem. This book not only continues the neoclassical tradition of thought in economics focused on quantitative economic change but also, and to a significant extent, discusses alternative approaches to certain questions of economic growth, utilizing conclusions that can be drawn from the Solow model. It is a useful tool in analyzing contemporary issues related to growth.

This book presents a quarter of a century of empirical research on interest rates and a variety of asset prices. It will serve to deepen our understanding of asset price inflation. The book includes extensive analysis of the measurement of interest rates, with case studies from The Netherlands, Belgium and EMU, and emphasizes statistical measurement and the attempt to understand interest rate behaviour through statistical estimation. The book also includes an examination of historical interest rate development in the long run, both theoretically and empirically. In conclusion, Professor Fase also analyses the behaviour of bonds, stocks and investment in art and examines the factors indispensable for a monetary strategy designed to target inflation.

The rich, multi-faceted and multi-disciplinary field of matching-based market design is an active and important one due to its highly successful applications with economic and sociological impact. Its home is economics, but with intimate connections to algorithm design and operations research. With chapters contributed by over fifty top researchers from all three disciplines, this volume is unique in its breadth and depth, while still being a cohesive and unified picture of the field, suitable for the uninitiated as well as the expert. It explains the dominant ideas from computer science and economics underlying the most important results on market design and introduces the main algorithmic questions and combinatorial structures. Methodologies and applications from both the pre-Internet and post-Internet eras are covered in detail. Key chapters discuss the basic notions of efficiency, fairness and incentives, and the way market design seeks solutions guided by normative criteria borrowed from social choice theory.

Economists have long sought to develop quantitative models of economic behaviour, which blend economic theory with data evidence. Econometric modelling of economic time series has strived to achieve this by seeking to discover sustainable and interpretable relationships. This important two-volume collection focuses on a central method used in selecting such models, namely simplification of an initially general model that adequately characterizes the empirical evidence within the investigators' theoretical framework. The volumes feature a wealth of evidence that has accrued over the last five years displaying its excellent abilities for model selection, based on Monte Carlo studies of automatic algorithms. These also throw light on several major methodological issues, and prompt many new ideas, which are discussed. The collection will be valuable to all empirical economists and econometricians.

Showcasing fuzzy set theory, this book highlights the enormous potential of fuzzy logic in helping to analyse the complexity of a wide range of socio-economic patterns and behaviour. The contributions to this volume explore the most up-to-date fuzzy-set methods for the measurement of socio-economic phenomena in a multidimensional and/or dynamic perspective. Thus far, fuzzy-set theory has primarily been utilised in the social sciences in the field of poverty measurement. These chapters examine the latest work in this area, while also exploring further applications including social exclusion, the labour market, educational mismatch, sustainability, quality of life and violence against women. The authors demonstrate that real-world situations are often characterised by imprecision, uncertainty and vagueness, which cannot be properly described by the classical set theory which uses a simple true-false binary logic. By contrast, fuzzy-set theory has been shown to be a powerful tool for describing the multidimensionality and complexity of social phenomena. This book will be of significant interest to economists, statisticians and sociologists utilising quantitative methods to explore socio-economic phenomena.

"Econometrics textbooks see their subject as a set of techniques; Magnus and Morgan see it as a set of practices. A combination of controlled experiment and anthropology of science, Methodology and Tacit Knowledge gives a rare inside view of how econometricians work, why econometrics is an art and not a set of simple recipes, and why, like all artists, econometricians differ in their techniques and finished works. This is economic methodology at its best." Kevin Hoover, University of California, Davis

"The tacit knowledge experiment was a highly commendable initiative. Its exploration of the theme of how knowledge is acquired and used in applied econometrics is unique and produced some fascinating insights into this process." Adrian Pagan, Australian National University

"It is rare, perhaps unique, to find leading empirical economists face the prospect of modelling the same phenomena, with the same data within the same limited time frame. A valuable and illuminating experiment in comparative research methodologies, made all the more provocative when compared to the excellent original study by Tobin." Richard Blundell, University College London

This book will be of considerable interest to economists and to econometricians concerned about the methodology of their own discipline, and will provide valuable material for researchers in science studies and for teachers of econometrics.

Predicting foreign exchange rates has presented a long-standing challenge for economists. However, the recent advances in computational techniques, statistical methods, newer datasets on emerging market currencies, etc., offer some hope. While we are still unable to beat a driftless random walk model, there has been serious progress in the field. This book provides an in-depth assessment of the use of novel statistical approaches and machine learning tools in predicting foreign exchange rate movement. First, it offers a historical account of how exchange rate regimes have evolved over time, which is critical to understanding turning points in a historical time series. It then presents an overview of the previous attempts at modeling exchange rates, and how different methods fared during this process. At the core sections of the book, the author examines the time series characteristics of exchange rates and how contemporary statistics and machine learning can be useful in improving predictive power, compared to previous methods used. Exchange rate determination is an active research area, and this book will appeal to graduate-level students of international economics, international finance, open economy macroeconomics, and management. The book is written in a clear, engaging, and straightforward way, and will greatly improve access to this much-needed knowledge in the field.

The growth rate of national income has fluctuated widely in the United States since 1929. In this volume, Edward F. Denison uses the growth accounting methodology he pioneered and refined in earlier studies to track changes in the trend of output and its determinants. At every step he systematically distinguishes changes in the economy's ability to produce as measured by his series on potential national income from changes in the ratio of actual output to potential output. Using data for earlier years as a backdrop, Denison focuses on the dramatic decline in the growth of potential national income that started in 1974 and was further accentuated beginning in 1980, and on the pronounced decline from business cycle to business cycle in the average ratio of actual to potential output, a slide under way since 1969. The decline in growth rates has been especially pronounced in national income per person employed and other productivity measures as growth of total output has slowed despite a sharp acceleration in growth of employment and total hours at work. Denison organizes his discussion around eight table that divide 1929-82 into three long periods (the last, 1973-82) and seven shorter periods (the most recent, 1973-79 and 1979-82). These tables provide estimates of the sources of growth for eight output measures in each period. Denison stresses that the 1973-82 period of slow growth in unfinished. He observes no improvement in the productivity trend, only a weak cyclical recovery from a 1982 low. Sources-of-growth tables isolate the contributions made to growth between "input" and "output per unit of input." Even so, it is not possible to quantify separately the contribution of all determinants, and Denison evaluates qualitatively the effects of other developments on the productivity slowdown.

The essays in this special volume survey some of the most recent advances in the global analysis of dynamic models for economics, finance and the social sciences. They deal in particular with a range of topics from mathematical methods as well as numerous applications including recent developments on asset pricing, heterogeneous beliefs, global bifurcations in complementarity games, international subsidy games and issues in economic geography. A number of stochastic dynamic models are also analysed. The book is a collection of essays in honour of the 60th birthday of Laura Gardini.

Handbook of Field Experiments, Volume Two explains how to conduct experimental research, presents a catalog of research to date, and describes which areas remain to be explored. The new volume includes sections on field experiments in education in developing countries, how to design social protection programs, a section on how to combat poverty, and updates on data relating to the impact and determinants of health levels in low-income countries. Separating itself from circumscribed debates of specialists, this volume surpasses the many journal articles and narrowly-defined books written by practitioners. This ongoing series will be of particular interest to scholars working with experimental methods. Users will find results from politics, education, and more.

Praise for the first edition: [This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. ... [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space framework. -Statistics in Medicine What distinguishes this book from comparable introductory texts is the use of state-space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing, including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters. -MAA Reviews Introduction to Time Series Modeling with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series. The second edition makes it possible for readers to reproduce examples in this book by using the freely available R package TSSS to perform computations for their own real-world time series problems. This book employs the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle filter as convenient tools for recursive estimation for state-space models. Further, it also takes a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection, including the least squares method, the maximum likelihood method, recursive estimation for state-space models and model selection by AIC. Along with the standard stationary time series models, such as the AR and ARMA models, the book also introduces nonstationary time series models such as the locally stationary AR model, the trend model, the seasonal adjustment model, the time-varying coefficient AR model and nonlinear non-Gaussian state-space models. About the Author: Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.

Doing Statistical Analysis looks at three kinds of statistical research questions - descriptive, associational, and inferential - and shows students how to conduct statistical analyses and interpret the results. Keeping equations to a minimum, it uses a conversational style and relatable examples such as football, COVID-19, and tourism, to aid understanding. Each chapter contains practice exercises, and a section showing students how to reproduce the statistical results in the book using Stata and SPSS. Digital supplements consist of data sets in Stata, SPSS, and Excel, and a test bank for instructors. Its accessible approach means this is the ideal textbook for undergraduate students across the social and behavioral sciences needing to build their confidence with statistical analysis.

The collapse in commodity prices since 1980 has been a major cause of the economic crisis in a large number of developing countries. This book investigates whether the commodity-producing countries, by joint action, could have prevented the price collapse by appropriate supply management. The analysis is focused on the markets for the tropical beverage crops: coffee, cocoa, and tea. Using new econometric models for each market, the impact of alternative supply management schemes on supply, consumption, prices, and export earnings is simulated for the later 1980s. The results indicate that supply management by producing countries would, indeed, have been a viable alternative to the `free market' approach favoured by the developed countries. This has important implications for current international commodity policy, and, in particular, for future joint action by producing countries to overcome persistent commodity surpluses as a complement to needed diversification.

Ranking of Multivariate Populations: A Permutation Approach with Applications presents a novel permutation-based nonparametric approach for ranking several multivariate populations. Using data collected from both experimental and observation studies, it covers some of the most useful designs widely applied in research and industry investigations, such as multivariate analysis of variance (MANOVA) and multivariate randomized complete block (MRCB) designs. The first section of the book introduces the topic of ranking multivariate populations by presenting the main theoretical ideas and an in-depth literature review. The second section discusses a large number of real case studies from four specific research areas: new product development in industry, perceived quality of the indoor environment, customer satisfaction, and cytological and histological analysis by image processing. A web-based nonparametric combination global ranking software is also described. Designed for practitioners and postgraduate students in statistics and the applied sciences, this application-oriented book offers a practical guide to the reliable global ranking of multivariate items, such as products, processes, and services, in terms of the performance of all investigated products/prototypes.

This book addresses the functioning of financial markets, in particular the financial market model, and modelling. More specifically, the book provides a model of adaptive preference in the financial market, rather than the model of the adaptive financial market, which is mostly based on Popper's objective propensity for the singular, i.e., unrepeatable, event. As a result, the concept of preference, following Simon's theory of satisficing, is developed in a logical way with the goal of supplying a foundation for a robust theory of adaptive preference in financial market behavior. The book offers new insights into financial market logic, and psychology: 1) advocating for the priority of behavior over information - in opposition to traditional financial market theories; 2) constructing the processes of (co)evolution adaptive preference-financial market using the concept of fetal reaction norms - between financial market and adaptive preference; 3) presenting a new typology of information in the financial market, aimed at proving point (1) above, as well as edifying an explicative mechanism of the evolutionary nature and behavior of the (real) financial market; 4) presenting sufficient, and necessary, principles or assumptions for developing a theory of adaptive preference in the financial market; and 5) proposing a new interpretation of the pair genotype-phenotype in the financial market model. The book's distinguishing feature is its research method, which is mainly logically rather than historically or empirically based. As a result, the book is targeted at generating debate about the best and most scientifically beneficial method of approaching, analyzing, and modelling financial markets.

Tackling the cybersecurity challenge is a matter of survival for society at large. Cyber attacks are rapidly increasing in sophistication and magnitude-and in their destructive potential. New threats emerge regularly, the last few years having seen a ransomware boom and distributed denial-of-service attacks leveraging the Internet of Things. For organisations, the use of cybersecurity risk management is essential in order to manage these threats. Yet current frameworks have drawbacks which can lead to the suboptimal allocation of cybersecurity resources. Cyber insurance has been touted as part of the solution - based on the idea that insurers can incentivize companies to improve their cybersecurity by offering premium discounts - but cyber insurance levels remain limited. This is because companies have difficulty determining which cyber insurance products to purchase, and insurance companies struggle to accurately assess cyber risk and thus develop cyber insurance products. To deal with these challenges, this volume presents new models for cybersecurity risk management, partly based on the use of cyber insurance. It contains: A set of mathematical models for cybersecurity risk management, including (i) a model to assist companies in determining their optimal budget allocation between security products and cyber insurance and (ii) a model to assist insurers in designing cyber insurance products. The models use adversarial risk analysis to account for the behavior of threat actors (as well as the behavior of companies and insurers). To inform these models, we draw on psychological and behavioural economics studies of decision-making by individuals regarding cybersecurity and cyber insurance. We also draw on organizational decision-making studies involving cybersecurity and cyber insurance. Its theoretical and methodological findings will appeal to researchers across a wide range of cybersecurity-related disciplines including risk and decision analysis, analytics, technology management, actuarial sciences, behavioural sciences, and economics. The practical findings will help cybersecurity professionals and insurers enhance cybersecurity and cyber insurance, thus benefiting society as a whole. This book grew out of a two-year European Union-funded project under Horizons 2020, called CYBECO (Supporting Cyber Insurance from a Behavioral Choice Perspective).

This book aims to bring together studies using different data types (panel data, cross-sectional data and time series data) and different methods (for example, panel regression, nonlinear time series, chaos approach, deep learning, machine learning techniques among others) and to create a source for those interested in these topics and methods by addressing some selected applied econometrics topics which have been developed in recent years. It creates a common meeting ground for scientists who give econometrics education in Turkey to study, and contribute to the delivery of the authors' knowledge to the people who take interest. This book can also be useful for "Applied Economics and Econometrics" courses in postgraduate education as a material source

A unique and comprehensive source of information, the International Yearbook of Industrial Statistics is the only international publication providing economists, planners, policymakers and business people with worldwide statistics on current performance and trends in the manufacturing sector.Covering more than 120 countries/areas, the 1996 edition of the Yearbook contains data which are internationally comparable and much more detailed in industrial classification than those supplied in previous publications. This is the second issue of the annual publication which succeeds the UNIDO's Handbook of Industrial Statistics and, at the same time, replaces the United Nation's Industrial Statistics Yearbook, volume I (General Industrial Statistics). Information has been collected directly from national statistical sources and supplemented with estimates by UNIDO. The Yearbook is designed to facilitate international comparisons relating to manufacturing activity and industrial performance. It provides data which can be used to analyse patterns of growth, structural change and industrial performance in individual industries. Data on employment trends, wages and other key indicators are also presented. Finally, the detailed information presented here enables the user to study different aspects of industry which was not possible using the aggregate data previously available.

The volume contains articles that should appeal to readers with computational, modeling, theoretical, and applied interests. Methodological issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian thresholding methods in hierarchical graphical models, adaptive reversible jump MCMC, LASSO estimators, parameter expansion algorithms, the implementation of parameter and non-parameter-based approaches to variable selection, a survey of key results in objective Bayesian model selection methodology, and a careful look at the modeling of endogeneity in discrete data settings. Important contemporary questions are examined in applications in macroeconomics, finance, banking, labor economics, industrial organization, and transportation, among others, in which model uncertainty is a central consideration.

This is the eighth volume in a ten-volume set designed for publication in 1997. It reprints in book form a selection of the most important and influential articles on probability, econometrics and economic games which cumulatively have had a major impact on the development of modern economics. There are 242 articles, dating from 1936 to 1996. Many of them were originally published in relatively inaccessible journals and may not, therefore, be available in the archives of many university libraries. The volumes are available separately and also as a complete ten-volume set. The contributors include D. Ellsberg, R.M. Hogart, J.B. Kadane, B.O. Koopmans, E.L. Lehman, D.F. Nicholls, H. Rubin, T.J. Sarjent, L.H. Summers and C.R. Wymer. This particular volume deals with the time series models.

The Who, What, and Where of America is designed to provide a sampling of key demographic information. It covers the United States, every state, each metropolitan statistical area, and all the counties and cities with a population of 20,000 or more. Who: Age, Race and Ethnicity, and Household Structure What: Education, Employment, and Income Where: Migration, Housing, and Transportation Each part is preceded by highlights and ranking tables that show how areas diverge from the national norm. These research aids are invaluable for understanding data from the ACS and for highlighting what it tells us about who we are, what we do, and where we live. Each topic is divided into four tables revealing the results of the data collected from different types of geographic areas in the United States, generally with populations greater than 20,000. Table A. States Table B. Counties Table C. Metropolitan Areas Table D. Cities In this edition, you will find social and economic estimates on the ways American communities are changing with regard to the following: Age and race Health care coverage Marital history Education attainment Income and occupation Commute time to work Employment status Home values and monthly costs Veteran status Size of home or rental unit This title is the latest in the County and City Extra Series of publications from Bernan Press. Other titles include County and City Extra, County and City Extra: Special Decennial Census Edition, and Places, Towns, and Townships.

Computational Finance Using C and C#: Derivatives and Valuation, Second Edition provides derivatives pricing information for equity derivatives, interest rate derivatives, foreign exchange derivatives, and credit derivatives. By providing free access to code from a variety of computer languages, such as Visual Basic/Excel, C++, C, and C#, it gives readers stand-alone examples that they can explore before delving into creating their own applications. It is written for readers with backgrounds in basic calculus, linear algebra, and probability. Strong on mathematical theory, this second edition helps empower readers to solve their own problems. *Features new programming problems, examples, and exercises for each chapter. *Includes freely-accessible source code in languages such as C, C++, VBA, C#, and Excel.. *Includes a new chapter on the history of finance which also covers the 2008 credit crisis and the use of mortgage backed securities, CDSs and CDOs. *Emphasizes mathematical theory.

These essays honor Professor Peter C.B. Phillips of Yale University and his many contributions to the field of econometrics. Professor Phillips's research spans many topics in econometrics including: non-stationary time series and panel models partial identification and weak instruments Bayesian model evaluation and prediction financial econometrics and finite-sample statistical methods and results. The papers in this volume reflect additions to and amplifications of many of Professor Phillips' research contributions. Some of the topics discussed in the volume include panel macro-econometric modeling, efficient estimation and inference in difference-in-difference models, limiting and empirical distributions of IV estimates when some of the instruments are endogenous, the use of stochastic dominance techniques to examine conditional wage distributions of incumbents and newly hired employees, long-horizon predictive tests in financial markets, new developments in information matrix testing, testing for co-integration in Markov switching error correction models, and deviation information criteria for comparing vector autoregressive models.