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Financial, Macro and Micro Econometrics Using R
Financial, Macro and Micro Econometrics Using R, Volume 42, provides state-of-the-art information on important topics in econometrics, including multivariate GARCH, stochastic frontiers, fractional responses, specification testing and model selection, exogeneity testing, causal analysis and forecasting, GMM models, asset bubbles and crises, corporate investments, classification, forecasting, nonstandard problems, cointegration, financial market jumps and co-jumps, among other topics.
Essays in Econometrics
These essays by Clive W.J. Granger span more than four decades and cover major topics in spectral analysis, seasonality, nonlinearity, methodology, and forecasting. The introduction by Eric Gysels, Norman R. Swanson and Mark W. Watson places the essays in context and demonstrates their enduring value.
Essays in Econometrics
These essays by Clive W.J. Granger span more than four decades and cover major topics in spectral analysis, seasonality, nonlinearity, methodology, and forecasting. The introduction by Eric Gysels, Norman R. Swanson and Mark W. Watson places the essays in context and demonstrates their enduring value.
Econometric Theory and Practice
The essays in this book explore important theoretical and applied advances in econometrics.
Dynamic Models for Volatility and Heavy Tails
The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling.
Nonlinear Time Series Analysis of Economic and Financial Data
Nonlinear Time Series Analysis of Economic and Financial Data provides an examination of the flourishing interest that has developed in this area over the past decade. The constant theme throughout this work is that standard linear time series tools leave unexamined and unexploited economically significant features in frequently used data sets. The book comprises original contributions written by specialists in the field, and offers a combination of both applied and methodological papers. It will be useful to both seasoned veterans of nonlinear time series analysis and those searching for an informative panoramic look at front-line developments in the area.
On the Shoulders of Giants
This book provides an introduction to Suzanne Scotchmer's contributions to the economics of innovation, intellectual property incentives, and equilibrium theory.
Missing Data Methods
Part of the "Advances in Econometrics" series, this title contains chapters covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; and, Consistent Estimation and Orthogonality.
Essays in Econometrics
These are econometrician Clive W. J. Granger's major essays in causality, integration, cointegration, and long memory.
