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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.
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.
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.
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.
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)."
Summarizes the latest developments and techniques in the field and
highlights areas such as sample surveys, nonparametric analysis,
hypothesis testing, time series analysis, Bayesian inference, and
distribution theory for current applications in statistics,
economics, medicine, biology, engineering, sociology, psychology,
and information technology. Containing more than 800 contemporary
references to facilitate further study, the Handbook of Applied
Econometrics and Statistical Inference is an in-depth guide for
applied statisticians, econometricians, economists, sociologists,
psychologists, data analysts, biometricians, medical researchers,
and upper-level undergraduate and graduate-level students in these
disciplines.
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.
Untranslated regions (UTRs) are important regions in genes that are
situated at 3 and 5 ends and are involved in the regulation of gene
expression. UTRs on 3 end acts as complementary sequences for
binding of microRNAs. The length and sequence of UTRs varies from
species to species. In this work various conserved motifs in the
untranslated regions of the genes have been reported by using
software tool. Very little literature is available on the motif
studies in the UTRs. This book therefore provides the insight on
the presence of motifs with repeated single nucleotide in the 3 and
5 UTRs. Some coding genes were also analyzed for these motifs.
These motifs show interesting similarities in different species of
the genus Caenorhabditis. This finding could possibly be used in
the identification of various organisms of same genus. These
investigations could be helpful to scientific community working on
non-coding RNAs, genomics and bioinformatics. This book could be
helpful for students and teachers in the subject of Bioinformatics,
Genomics, Molecular Biology and Biotechnology.
Human rights, along with right to life as a most basic human right
of all, were expressly protected in the Constitutions of India and
Pakistan, when international human rights law was in its embryonic
form. No doubt, the new jurisprudence of human rights placed their
protection on a higher pedestal than other provisions of the
Constitution, and was welcomed warmly. However, judicial activism
was a sheer deviation from the spirit and norms of a constitutional
democracy, theory of separation of powers, and the limited
jurisdiction of the Supreme Court of India, under the Indian
Constitution. While in Pakistan, it has continuously been denied to
review a constitutional amendment, taking away a fundamental right,
on the ground that political questions were better to be solved on
the forum of Parliament, instead of Judiciary. Nonetheless, with
the passage of time, the horizons of right to life expanded,
assimilating a number of other human rights, missing in the
Constitutions of both Countries. They emerged as penumbra of right
to life, owing to the active judicial role, creating most of third
generation human rights.
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 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.
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.
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