Release on 2012-06-21 | by David F. Hendry,Bent Nielsen
A Likelihood Approach
Author: David F. Hendry,Bent Nielsen
Pubpsher: Princeton University Press
Category: Business & Economics
Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointegrated systems. In each setting, a statistical model is constructed to explain the observed variation in the data, with estimation and inference based on the likelihood function. Substantive issues are always addressed, showing how both statistical and economic assumptions can be tested and empirical results interpreted. Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied. Econometric Modeling is a self-contained introduction for advanced undergraduate or graduate students. Throughout, data illustrate and motivate the approach, and are available for computer-based teaching. Technical issues from probability theory and statistical theory are introduced only as needed. Nevertheless, the approach is rigorous, emphasizing the coherent formulation, estimation, and evaluation of econometric models relevant for empirical research.
This book surveys the theories, techniques (model- building and data collection), and applications of econometrics. KEY TOPICS: It focuses on those aspects of econometrics that are of major importance to readers and researchers interested in performing, evaluating, or understanding econometric studies in a variety of areas. It reviews matrix notation and the use of multivariate statistics; discusses the specification of the model and the development of data for its estimation; covers recent developments in econometric models, techniques, and applications; explains the estimation of single-equation models; and provides case studies of the applications of econometrics to a wide array of areas — including traditional areas such as the estimation of demand functions and production functions, and macroeconometric models.
Release on 2000 | by Lawrence Robert Klein,Shin?ichi Ichimura
Author: Lawrence Robert Klein,Shin?ichi Ichimura
Pubpsher: World Scientific
Category: Business & Economics
This is the very first book to offer seven substantial econometric models of the Chinese economy with the statistical data used, so that the reader will be able to reproduce them all and test them for any policy alternatives.The book presents up-to-date models produced both inside and outside China, so that readers can understand most of the advanced studies of the Chinese economy by Chinese experts at the present time. This is an invaluable reference for graduate students and scholars working on Chinese economic problems.
Presents the main statistical tools of econometrics, focusing specifically on modern econometric methodology. The authors unify the approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing. The choice of GMM is explained by its relevance in structural econometrics and its preeminent position in econometrics overall. Split into four parts, Part I explains general methods. Part II studies statistical models that are best suited for microeconomic data. Part III deals with dynamic models that are designed for macroeconomic and financial applications. In Part IV the authors synthesize a set of problems that are specific to statistical methods in structural econometrics, namely identification and over-identification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises. Nobel Laureate James A. Heckman offers a foreword to the work.
Release on 2012-12-06 | by M. Ray Perryman,James R. Schmidt
Author: M. Ray Perryman,James R. Schmidt
Pubpsher: Springer Science & Business Media
Category: Business & Economics
This book is the first volume of the International Series in Economic Model ing, a series designed to summarize current issues and procedures in applied modeling within various fields of economics and to offer new or alternative approaches to prevailing problems. In selecting the subject area for the first volume, we were attracted by the area to which applied modeling efforts are increasingly being drawn, regional economics and its associated subfields. Applied modeling is a broad rubric even when the focus is restricted to econometric modeling issues. Regional econometric modeling has posted a record of rapid growth during the last two decades and has become an established field of research and application. Econometric models of states and large urban areas have become commonplace, but the existence of such models does not signal an end to further development of regional econ ometric methods and models. Many issues such as structural specification, level of geographic detail, data constraints, forecasting integrity, and syn thesis with other regional modeling techniques will continue to be sources of concern and will prompt further research efforts. The chapters of this volume reflect many of these issues. A brief synopsis of each contribution is provided below: Richard Weber offers an overview of regional econometric models by discussing theoretical specification, nature of variables, and ultimate useful ness of such models. For an illustration, Weber describes the specification of the econometric model of New Jersey.
This book serves as a reference guide to econometrics modelling and forecasting. The book is divided into two parts i.e. Modelling and Forecasting, to make it easy for the reader to understand the topic.The first part of the book i.e. Modelling, throws light on the various econometric models. The models are very well explained to make it easier for anyone reading the book to grasp the concept. Various mathematical and statistical tools used with reference to econometrics models are also discussed. Chapter 4 discusses hypothesis testing which is of paramount importance in any research and will also act as a base for the next part of the book i.e. Forecasting. Various basic tests, which are used for hypothesis testing are also included in the chapter.The second part of the book i.e. Forecasting includes several different concepts such as forecasting principles, forecasting classification, forecasting accuracy evaluation and its industrial applications in depth. The concepts are enriched with relevant case studies. The case studies have been specially selected for the better understanding of the concepts. The book is written with a vision to guide the reader on structuring a forecasting problem. The book provides the necessary information to the reader so that the reader can design various forecasting methods and evaluate them efficiently. It answers important questions such as: * How to implement various forecasting methods in different situations and with different variables?* When to accept or reject the forecasts? The book takes the readers through a variety of forecasting methods, with a strong discussion on their strengths and weaknesses, and an analysis on how to use them efficiently. The book has been written with the objective of helping the readers/researchers select the most appropriate method for a given forecasting problem and ultimately, evaluate the chosen forecasting model. This is useful especially when selection of the most appropriate method for a particular situation is the most important criterion. This book also suggests what research on forecasting methods will have the greatest, and the least, payoff. Research on forecasting has grown in importance to a great extent in recent times due to the fact that application of forecasting techniques has been growing rapidly in the areas of the social, behavioral and management sciences. So much is known about forecasting methods, but little is applied. Why? Because what is known in one field is unknown in another or because it frequently contradicts our common sense or challenges our beliefs and our behavior. Hence, the book will also tell the researcher how to effectively use, evaluate and interpret different forecasting methods under different situations. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Overall, this book should serve as a standard source of reference for researchers in the fields of business, government, academia, and consulting.
Release on 2017-09-05 | by Fabrizio Carlevaro,C. Schlesser,M.-E. Binet,S. Durand,M. Paul
Author: Fabrizio Carlevaro,C. Schlesser,M.-E. Binet,S. Durand,M. Paul
Category: Business & Economics
This paper develops an econometric methodology devised to analyze a sample of time unbalanced panel data on residential water consumption in the French island La Reunion with the purpose to bring out the main determinants of household water consumption and estimate the importance of water consumption by uses. For this purpose, we specify a daily panel econometric model and derive, by performing a time aggregation, a general linear regression model accounting for water consumption data recorded on periods of any calendar date and time length. To estimate efficiently the parameters of this model we develop a feasible two step generalized least square method. Using the principle of best linear unbiaised prediction, we finally develop an approach allowing to consistenly break down the volume of water consumption recorded on household water bills by uses, namely by enforcing this estimated decomposition to add up to the observed total. The application of this methodology to a sample of 437 unbalanced panel observations shows the scope of this approach for the empirical analysis of actual data.