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Originally published in 1981, this book considers one particular
area of econometrics- the linear model- where significant recent
advances have been made. It considers both single and multiequation
models with varying co-efficients, explains the various theories
and techniques connected with these and goes on to describe the
various applications of the models. Whilst the detailed explanation
of the models will interest primarily econometrics specialists, the
implications of the advances outlined and the applications of the
models will intrest a wide range of economists.
Features Provide a state-of-the-art description of the
physiological, biochemical, and molecular status of the
understanding of abiotic stress in plants. Addressing factors that
are threatening future food production and providing potential
solutions of these factors. Design to cater to the needs of those
students engaged in the field of environmental sciences, soil
sciences, agricultural microbiology, plant pathology, and agronomy.
New strategies have pointed in this book for the better crop
productivity and yield. Understanding of new techniques pointed out
in this book will open the possibility of genetic engineering in
crop plants with the concomitant improved stress tolerance.
This work examines theoretical issues, as well as practical
developments in statistical inference related to econometric models
and analysis. This work offers discussions on such areas as the
function of statistics in aggregation, income inequality, poverty,
health, spatial econometrics, panel and survey data, bootstrapping
and time series.
Summarizing developments and techniques in the field, this
reference covers sample surveys, nonparametric analysis, hypothesis
testing, time series analysis, Bayesian inference, and distribution
theory for applications in statistics, economics, medicine,
biology, engineering, sociology, psychology, and information
technology. It supplies a geometric proof of an extended
Gauss-Markov theorem, approaches for the design and implementation
of sample surveys, advances in the theory of Neyman's smooth test,
and methods for pre-test and biased estimation. It includes
discussions ofsample size requirements for estimation in SUR
models, innovative developments in nonparametric models, and more.
Handbook of Empirical Economics and Finance explores the latest
developments in the analysis and modeling of economic and financial
data. Well-recognized econometric experts discuss the rapidly
growing research in economics and finance and offer insight on the
future direction of these fields. Focusing on micro models, the
first group of chapters describes the statistical issues involved
in the analysis of econometric models with cross-sectional data
often arising in microeconomics. The book then illustrates time
series models that are extensively used in empirical macroeconomics
and finance. The last set of chapters explores the types of panel
data and spatial models that are becoming increasingly significant
in analyzing complex economic behavior and policy evaluations. This
handbook brings together both background material and new
methodological and applied results that are extremely important to
the current and future frontiers in empirical economics and
finance. It emphasizes inferential issues that transpire in the
analysis of cross-sectional, time series, and panel data-based
empirical models in economics, finance, and related disciplines.
Originally published in 1981, this book considers one particular
area of econometrics- the linear model- where significant recent
advances have been made. It considers both single and multiequation
models with varying co-efficients, explains the various theories
and techniques connected with these and goes on to describe the
various applications of the models. Whilst the detailed explanation
of the models will interest primarily econometrics specialists, the
implications of the advances outlined and the applications of the
models will intrest a wide range of economists.
Handbook of Empirical Economics and Finance explores the latest
developments in the analysis and modeling of economic and financial
data. Well-recognized econometric experts discuss the rapidly
growing research in economics and finance and offer insight on the
future direction of these fields. Focusing on micro models, the
first group of chapters describes the statistical issues involved
in the analysis of econometric models with cross-sectional data
often arising in microeconomics. The book then illustrates time
series models that are extensively used in empirical macroeconomics
and finance. The last set of chapters explores the types of panel
data and spatial models that are becoming increasingly significant
in analyzing complex economic behavior and policy evaluations. This
handbook brings together both background material and new
methodological and applied results that are extremely important to
the current and future frontiers in empirical economics and
finance. It emphasizes inferential issues that transpire in the
analysis of cross-sectional, time series, and panel data-based
empirical models in economics, finance, and related disciplines.
This work examines theoretical issues, as well as practical
developments in statistical inference related to econometric models
and analysis. This work offers discussions on such areas as the
function of statistics in aggregation, income inequality, poverty,
health, spatial econometrics, panel and survey data, bootstrapping
and time series.
Originally published in 1981, this book considers one particular
area of econometrics- the linear model- where significant recent
advances have been made. It considers both single and multiequation
models with varying co-efficients, explains the various theories
and techniques connected with these and goes on to describe the
various applications of the models. Whilst the detailed explanation
of the models will interest primarily econometrics specialists, the
implications of the advances outlined and the applications of the
models will intrest a wide range of economists.
This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah,
contains the latest research on nonparametric and semiparametric
econometrics and statistics. These data-driven models seek to
replace the "classical " parametric models of the past, which were
rigid and often linear. Chapters by leading international
econometricians and statisticians highlight the interface between
econometrics and statistical methods for nonparametric and
semiparametric procedures. They provide a balanced view of new
developments in the analysis and modeling of applied sciences with
cross-section, time series, panel, and spatial data sets. The major
topics of the volume include: the methodology of semiparametric
models and special regressor methods; inverse, ill-posed, and
well-posed problems; different methodologies related to additive
models; sieve regression estimators, nonparametric and
semiparametric regression models, and the true error of competing
approximate models; support vector machines and their modeling of
default probability; series estimation of stochastic processes and
some of their applications in Econometrics; identification,
estimation, and specification problems in a class of semilinear
time series models; nonparametric and semiparametric techniques
applied to nonstationary or near nonstationary variables; the
estimation of a set of regression equations; and a new approach to
the analysis of nonparametric models with exogenous treatment
assignment.
Over the last three decades much research in empirical and
theoretical economics has been carried on under various
assumptions. For example a parametric functional form of the
regression model, the heteroskedasticity, and the autocorrelation
is always as sumed, usually linear. Also, the errors are assumed to
follow certain parametric distri butions, often normal. A
disadvantage of parametric econometrics based on these assumptions
is that it may not be robust to the slight data inconsistency with
the particular parametric specification. Indeed any
misspecification in the functional form may lead to erroneous
conclusions. In view of these problems, recently there has been
significant interest in 'the semiparametric/nonparametric
approaches to econometrics. The semiparametric approach considers
econometric models where one component has a parametric and the
other, which is unknown, a nonparametric specification (Manski 1984
and Horowitz and Neumann 1987, among others). The purely non
parametric approach, on the other hand, does not specify any
component of the model a priori. The main ingredient of this
approach is the data based estimation of the unknown joint density
due to Rosenblatt (1956). Since then, especially in the last
decade, a vast amount of literature has appeared on nonparametric
estimation in statistics journals. However, this literature is
mostly highly technical and this may partly be the reason why very
little is known about it in econometrics, although see Bierens
(1987) and Ullah (1988)."
Info-metrics is a framework for modeling, reasoning, and drawing
inferences under conditions of noisy and insufficient information.
It is an interdisciplinary framework situated at the intersection
of information theory, statistical inference, and decision-making
under uncertainty. In Advances in Info-Metrics, Min Chen, J.
Michael Dunn, Amos Golan, and Aman Ullah bring together a group of
thirty experts to expand the study of info-metrics across the
sciences and demonstrate how to solve problems using this
interdisciplinary framework. Building on the theoretical
underpinnings of info-metrics, the volume sheds new light on
statistical inference, information, and general problem solving.
The book explores the basis of information-theoretic inference and
its mathematical and philosophical foundations. It emphasizes the
interrelationship between information and inference and includes
explanations of model building, theory creation, estimation,
prediction, and decision making. Each of the nineteen chapters
provides the necessary tools for using the info-metrics framework
to solve a problem. The collection covers recent developments in
the field, as well as many new cross-disciplinary case studies and
examples. Designed to be accessible for researchers, graduate
students, and practitioners across disciplines, this book provides
a clear, hands-on experience for readers interested in solving
problems when presented with incomplete and imperfect information.
This book provides a comprehensive and unified treatment of finite
sample statistics and econometrics, a field that has evolved in the
last five decades. Within this framework, this is the first book
which discusses the basic analytical tools of finite sample
econometrics, and explores their applications to models covered in
a first year graduate course in econometrics, including repression
functions, dynamic models, forecasting, simultaneous equations
models, panel data models, and censored models. Both linear and
nonlinear models, as well as models with normal and non-normal
errors, are studied. Finite sample results are extremely useful for
applied researchers doing proper econometric analysis with small or
moderately large sample data. Finite sample econometrics also
provides the results for very large (asymptotic) samples. This book
provides simple and intuitive presentations of difficult concepts,
unified and heuristic developments of methods, and applications to
various econometric models. It provides a new perspective on
teaching and research in econometrics, statistics, and other
applied subjects.
This book systematically and thoroughly covers the vast literature
on the nonparametric and semiparametric statistics and econometrics
that has evolved over the last five decades. Within this framework
this is the first book to discuss the principles of the
nonparametric approach to the topics covered in a first year
graduate course in econometrics, e.g. regression function,
heteroskedasticity, simultaneous equations models, logit-probit and
censored models. Nonparametric and semiparametric methods
potentially offer considerable reward to applied researchers, owing
to the methods' ability to adapt to many unknown features of the
data. Professors Pagan and Ullah provide intuitive explanations of
difficult concepts, heuristic developments of theory, and empirical
examples emphasizing the usefulness of the modern nonparametric
approach. The book should provide a new perspective on teaching and
research in applied subjects in general and econometrics and
statistics in particular.
This book systematically and thoroughly covers a vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the past five decades. Within this framework, this is the first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics, e.g., regression function, heteroskedasticity, simultaneous equations models, logit-probit and censored models. Professors Pagan and Ullah provide intuitive explanations of difficult concepts, heuristic developments of theory, and empirical examples emphasizing the usefulness of modern nonparametric approach. The book should provide a new perspective on teaching and research in applied subjects in general and econometrics and statistics in particular.
This book provides a comprehensive and unified treatment of finite
sample statistics and econometrics, a field that has evolved in the
last five decades. Within this framework, this is the first book
which discusses the basic analytical tools of finite sample
econometrics, and explores their applications to models covered in
a first year graduate course in econometrics, including repression
functions, dynamic models, forecasting, simultaneous equations
models, panel data models, and censored models. Both linear and
nonlinear models, as well as models with normal and non-normal
errors, are studied.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly
expanding series in which leading econometricians assess recent
developments in such areas as stochastic probability, panel and
time series data analysis, modeling, and cointegration. In both
hardback and affordable paperback, each volume explains the nature
and applicability of a topic in greater depth than possible in
introductory textbooks or single journal articles. Each definitive
work is formatted to be as accessible and convenient for those who
are not familiar with the detailed primary literature.
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