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__Abstract__ One of the fastest growing areas in empirical finance, and also one of the least rigorously analyzed, especially from a financial econometrics perspective, is the econometric analysis of financial derivatives, which are typically complicated and difficult to analyze. The purpose of this special issue of the journal on "Econometric Analysis of Financial Derivatives" is to highlight several areas of research by leading academics in which novel econometric, financial econometric, mathematical finance and empirical finance methods have contributed significantly to the econometric analysis of financial derivatives, including market-based estimation of stochastic volatility models, the fine structure of equity-index option dynamics, leverage and feedback effects in multifactor Wishart stochastic volatility for option pricing, option pricing with non-Gaussian scaling and infinite-state switching volatility, stock return and cash flow predictability: the role of volatility risk, the long and the short of the risk-return trade-off, What's beneath the surface? option pricing with multifrequency latent states, bootstrap score tests for fractional integration in heteroskedastic ARFIMA models, with an application to price dynamics in commodity spot and futures markets, a stochastic dominance approach to financial risk management strategies, empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction, non-linear dynamic model of the variance risk premium, pricing with finite dimensional dependence, quanto option pricing in the presence of fat tails and asymmetric dependence, smile from the past: a general option pricing framework with multiple volatility and leverage components, COMFORT: A common market factor non-Gaussian returns model, divided governments and futures prices, and model-based pricing for financial derivatives

In this compelling 1995 book, David Hendry and Mary Morgan bring together the classic papers of the pioneer econometricians. Together, these papers form the foundations of econometric thought. They are essential reading for anyone seeking to understand the aims, method and methodology of econometrics and the development of this statistical approach in economics. However, because they are technically straightforward, the book is also accessible to students and non-specialists. An editorial commentary places the readings in their historical context and indicates the continuing relevance of these early, yet highly sophisticated, works for current econometric analysis. While this book provides a companion volume to Mary Morgan's acclaimed The History of Econometric Ideas, the editors' commentary both adds to that earlier volume and also provides a stand-alone and synthetic account of the development of econometrics

The book provides graduate students and researchers with an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Proper count data probability models allow for rich inferences, both with respect to the stochastic count process that generated the data, and with respect to predicting the distribution of outcomes. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clu

This collection of papers represents the state of the art in the applicationof recent econometric methods to the analysis of financial markets. From a methodological point of view the main emphasis is on cointegration analysis and ARCH modelling. In cointegration analysis the links between long-runcomponents of time series are studied. The methods used can be applied to the determination of equilibrium relationships between the variables, whereas ARCH models are concerned with the measurement and analysis of changing variances in time series. These econometric models have been the most significant innovations for the empirical analysis of financial time series in recent years. Other econometric methods and models applied in the papers include factor analysis, vector autoregressions, and Markov-switching models. The papers cover a wide range of issues and theories in financial and international economics: the term structure ofinterest rates, exchange-rate determination, target-zone dynamics, stock-market efficiency, and option pricing

Given extensive use of individual level data in Health Economics, it has become increasingly important to understand the microeconometric techniques available to applied researchers. The purpose of this book is to give readers convenient access to a collection of recent contributions that contain innovative applications of microeconometric methods to data on health and health care. Contributions are selected from papers presented at the European Workshops on Econometrics and Health Economics and published in Health Economics. Topics covered include: Latent Variables Unobservable heterogeneity and selection problems Count data and survival analysis Flexible and semiparametric estimators for limited dependent variables Classical and simulation methods for panel data Publication marks the tenth anniversary of the Workshop series. Doctoral students and researchers in health economics and microeconomics will find this book invaluable. Researchers in related fields such as labour economics and biostatistics will also find the content of use

Chapter 1. Introduction: Significance and Role of Regional Economic Analysis in Japan -- Chapter 2. Study of Total Fertility Rate of Women in Employment, by Industrial Sector: Estimation of Japanese National and Prefectural Data Using the Own-Children Method -- Chapter 3. Analysis of the Impact of Overtourism in Japan: The Effects of Congestion Phenomena -- Chapter 4. Analysis of Environmental and Economic Effectiveness of Wind Power Generation in Japan -- Chapter 5. Economic Ripple Effects of Choice Factors and Disaster-Related Investments at Nagoya Port -- Chapter 6. Summary and Future Tasks.

Abstract.  Fisher's equation for the determination of the real rate of interest is studied from a fresh econometric perspective. Some new methods of data description for nonstationary time series are introduced. The methods provide a nonparametric mechanism for modelling the spatial densities of a time series that displays random wandering characteristics, like interest rates and inflation. Hazard rate functionals are also constructed, an asymptotic theory is given, and the techniques are illustrated in some empirical applications to real interest rates for the United States. The paper ends by calculating semiparametric estimates of long‐range dependence in U.S. real interest rates, using a new estimation procedure called modified log periodogram regression and new asymptotics that covers the nonstationary case. The empirical results indicate that the real rate of interest in the United States is (fractionally) nonstationary over 1934–1997 and over the more recent subperiods 1961–1985 and 1961–1997. Unit root nonstationarity and short memory stationarity are both strongly rejected for all these periods.

Introduction -- The One-Way Error Component Regression Model -- The Two-Way Error Component Regression Model -- Test of Hypotheses with Panel Data -- Heteroskedasticity and Serial Correlation in the Error Component Model -- Seemingly Unrelated Regressions with Error Components -- Simultaneous Equations with Error Components -- Dynamic Panel Data Models -- Unbalanced Panel Data Models -- Special Topics -- Limited Dependent Variables and Panel Data -- Nonstationary Panels -- Spatial Panel Data Models.