The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers, and data scientists seeking to perform effective analyses of networks, especially inference problems. It introduces the key results and ideas in an accessible, yet rigorous way. While a multi-contributor reference, the work is tightly focused and disciplined, providing latitude for varied specialties in one authorial voice.
In recent years econometricians have examined the problems of diagnostic testing, specification testing, semiparametric estimation and model selection. In addition researchers have considered whether to use model testing and model selection procedures to decide the models that best fit a particular dataset. This book explores both issues with application to various regression models, including the arbitrage pricing theory models. It is ideal as a reference for statistical sciences postgraduate students, academic researchers and policy makers in understanding the current status of model building and testing techniques.
This book presents statistical methods for analysis of the duration of events. The primary focus is on models for single-spell data, events in which individual agents are observed for a single duration. Some attention is also given to multiple-spell data. The first part of the book covers model specification, including both structural and reduced form models and models with and without neglected heterogeneity. The book next deals with likelihood based inference about such models, with sections on full and semiparametric specification. A final section treats graphical and numerical methods of specification testing. This is the first published exposition of current econometric methods for the study of duration data.
After all the research on agricultural risk to date, the treatment of risk in agricultural research is far from harmonious. Many competing risk models have been proposed. Some new methodologies are largely untested. Some of the leading empirical methodologies in agricultural economic research are poorly suited for problems with aggregate data where risk averse behavior is less likely to be important. This book is intended to (i) define the current state of the literature on agricultural risk research, (ii) provide a critical evaluation of economic risk research on agriculture to date and (iii) set a research agenda that will meet future needs and prospects. This type of research promises to become of increasing importance because agricultural policy in the United States and elsewhere has decidedly shifted from explicit income support objectives to risk-related motivations of helping farmers deal with risk. Beginning with the 1996 Farm Bill, the primary set of policy instruments from U.S. agriculture has shifted from target prices and set aside acreage to agricultural crop insurance. Because this book is intended to have specific implications for U.S. agricultural policy, it has a decidedly domestic scope, but clearly many of the issues have application abroad. For each of the papers and topics included in this volume, individuals have been selected to give the strongest and broadest possible treatment of each facet of the problem. The result is this comprehensive reference book on the economics of agricultural risk.
The Economic Impact Group (EIG) was created to support the work on the DCFR with insights from law and economics. It brings together a number of leading European law and economics scholars. The Group looked at the main elements of the DCFR with two questions in mind: from an economic perspective, is it sensible to harmonize private law across Europe for this specific element, and is the solution chosen in the DCFR optimal? This book presents the outcome of the work of the EIG. It deals with key issues such as the function of contract law, contract formation, good faith, non-discrimination, specific performance versus damages, standard contractual terms and consumer protection in contract law. The EIG complements the work of the drafters of the DCFR with insightful and critical assessments, based on the well-established law and economics literature.
Using Applied Econometrics with SAS: Modeling Demand, Supply, and Risk, you will quickly master SAS applications for implementing and estimating standard models in the field of econometrics. This guide introduces you to the major theories underpinning applied demand and production economics. For each of its three main topics--demand, supply, and risk--a concise theoretical orientation leads directly into consideration of specific economic models and econometric techniques, collectively covering the following: Double-log demand systems Linear expenditure systems Almost ideal demand systems Rotterdam models Random parameters logit demand models Frequency-severity models Compound distribution models Cobb-Douglas production functions Translogarithmic cost functions Generalized Leontief cost functions Density estimation techniques Copula models SAS procedures that facilitate estimation of demand, supply, and risk models include the following, among others: PROC MODEL PROC COPULA PROC SEVERITY PROC KDE PROC LOGISTIC PROC HPCDM PROC IML PROC REG PROC COUNTREG PROC QLIM An empirical example, SAS programming code, and a complete data set accompany each econometric model, empowering you to practice these techniques while reading. Examples are drawn from both major scholarly studies and business applications so that professors, graduate students, government economic researchers, agricultural analysts, actuaries, and underwriters, among others, will immediately benefit.
“A riveting account that reaches beyond the market landscape to say something universal about risk and triumph, about hubris and failure.”—The New York Times NAMED ONE OF THE BEST BOOKS OF THE YEAR BY BUSINESSWEEK In this business classic—now with a new Afterword in which the author draws parallels to the recent financial crisis—Roger Lowenstein captures the gripping roller-coaster ride of Long-Term Capital Management. Drawing on confidential internal memos and interviews with dozens of key players, Lowenstein explains not just how the fund made and lost its money but also how the personalities of Long-Term’s partners, the arrogance of their mathematical certainties, and the culture of Wall Street itself contributed to both their rise and their fall. When it was founded in 1993, Long-Term was hailed as the most impressive hedge fund in history. But after four years in which the firm dazzled Wall Street as a $100 billion moneymaking juggernaut, it suddenly suffered catastrophic losses that jeopardized not only the biggest banks on Wall Street but the stability of the financial system itself. The dramatic story of Long-Term’s fall is now a chilling harbinger of the crisis that would strike all of Wall Street, from Lehman Brothers to AIG, a decade later. In his new Afterword, Lowenstein shows that LTCM’s implosion should be seen not as a one-off drama but as a template for market meltdowns in an age of instability—and as a wake-up call that Wall Street and government alike tragically ignored. Praise for When Genius Failed “[Roger] Lowenstein has written a squalid and fascinating tale of world-class greed and, above all, hubris.”—BusinessWeek “Compelling . . . The fund was long cloaked in secrecy, making the story of its rise . . . and its ultimate destruction that much more fascinating.”—The Washington Post “Story-telling journalism at its best.”—The Economist
Recent economic growth in China and other Asian countries has led to increased commodity demand which has caused price rises and accompanying price fluctuations not only for crude oil but also for the many other raw materials. Such trends mean that world commodity markets are once again under intense scrutiny. This book provides new insights into the modeling and forecasting of primary commodity prices by featuring comprehensive applications of the most recent methods of statistical time series analysis. The latter utilize econometric methods concerned with structural breaks, unobserved components, chaotic discovery, long memory, heteroskedasticity, wavelet estimation and fractional integration. Relevant tests employed include neural networks, correlation dimensions, Lyapunov exponents, fractional integration and rescaled range. The price forecasting involves structural time series trend plus cycle and cyclical trend models. Practical applications focus on the price behaviour of more than twenty international commodity markets.
