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Volume 36 of Advances in Econometrics recognizes Aman Ullah's
significant contributions in many areas of econometrics and
celebrates his long productive career. The volume features original
papers on the theory and practice of econometrics that is related
to the work of Aman Ullah. Topics include
nonparametric/semiparametric econometrics; finite sample
econometrics; shrinkage methods; information/entropy econometrics;
model specification testing; robust inference; panel/spatial
models. Advances in Econometrics is a research annual whose
editorial policy is to publish original research articles that
contain enough details so that economists and econometricians who
are not experts in the topics will find them accessible and useful
in their research.
The Regression Discontinuity (RD) design is one of the most popular
and credible research designs for program evaluation and causal
inference. This volume 38 of Advances in Econometrics collects
twelve innovative and thought-provoking contributions to the RD
literature, covering a wide range of methodological and practical
topics. Some chapters touch on foundational methodological issues
such as identification, interpretation, implementation,
falsification testing, estimation and inference, while others focus
on more recent and related topics such as identification and
interpretation in a discontinuity-in-density framework, empirical
structural estimation, comparative RD methods, and extrapolation.
These chapters not only give new insights for current
methodological and empirical research, but also provide new bases
and frameworks for future work in this area. This volume
contributes to the rapidly expanding RD literature by bringing
together theoretical and applied econometricians, statisticians,
and social, behavioural and biomedical scientists, in the hope that
these interactions will further spark innovative practical
developments in this important and active research area.
These essays honor Professor Peter C.B. Phillips of Yale University
and his many contributions to the field of econometrics. Professor
Phillips's research spans many topics in econometrics including:
non-stationary time series and panel models partial identification
and weak instruments Bayesian model evaluation and prediction
financial econometrics and finite-sample statistical methods and
results. The papers in this volume reflect additions to and
amplifications of many of Professor Phillips' research
contributions. Some of the topics discussed in the volume include
panel macro-econometric modeling, efficient estimation and
inference in difference-in-difference models, limiting and
empirical distributions of IV estimates when some of the
instruments are endogenous, the use of stochastic dominance
techniques to examine conditional wage distributions of incumbents
and newly hired employees, long-horizon predictive tests in
financial markets, new developments in information matrix testing,
testing for co-integration in Markov switching error correction
models, and deviation information criteria for comparing vector
autoregressive models.
Vector autoregressive (VAR) models are among the most widely used
econometric tools in the fields of macroeconomics and financial
economics. Much of what we know about the response of the economy
to macroeconomic shocks and about how various shocks have
contributed to the evolution of macroeconomic and financial
aggregates is based on VAR models. VAR models also have been used
successfully for economic and business forecasting, for modelling
risk and volatility, and for the construction of forecast
scenarios. Since the introduction of VAR models by C.A. Sims in
1980, the VAR methodology has continuously evolved. Even today
important extensions and reinterpretations of the VAR framework are
being developed. Examples include VAR models for mixed-frequency
data, VAR models as approximations to DSGE models, factor-augmented
VAR models, new tools for the identification of structural shocks
in VAR models, panel VAR approaches, and time-varying parameter VAR
models. This volume collects contributions from some of the leading
VAR experts in the world on VAR methods and applications. Each
chapter highlights and synthesizes a new development in this
literature in a way that is accessible to practitioners, to
graduate students, and to readers in other fields.
Often applied econometricians are faced with working with data that
is less than ideal. The data may be observed with gaps in it, a
model may suggest variables that are observed at different
frequencies, and sometimes econometric results are very fragile to
the inclusion or omission of just a few observations in the sample.
Papers in this volume discuss new econometric techniques for
addressing these problems.
Studies in the Economics of Uncertainty presents some new
developments in the economics of uncertainty produced by leading
scholars in the field. The contributions to this Festschrift in
honor of Professor Josef Hadar of Southern Methodist University
cover a broad range of topics centered on the principle of
Stochastic Dominance. Topics covered range from theoretical and
statistical developments on Stochastic Dominance to new
applications of the Stochastic Dominance Theory. The intended
audience includes researchers interested in recent developments in
tools used for decision-making under uncertainty as well as
economists currently applying Stochastic Dominance principles to
the analysis of the Theory of Firm, International Trade, and the
Theory of Finance.
This book had its conception in 1975in a friendly tavern near the
School of Businessand PublicAdministration at the
UniversityofMissouri-Columbia. Two of the authors (Fomby and Hill)
were graduate students of the third (Johnson), and were (and are)
concerned about teaching econometrics effectively at the graduate
level. We decided then to write a book to serve as a comprehensive
text for graduate econometrics. Generally, the material included in
the bookand itsorganization have been governed by the question, "
Howcould the subject be best presented in a graduate class?" For
content, this has meant that we have tried to cover " all the bases
" and yet have not attempted to be encyclopedic. The intended
purpose has also affected the levelofmathematical rigor. We have
tended to prove only those results that are basic and/or relatively
straightforward. Proofs that would demand inordinant amounts of
class time have simply been referenced. The book is intended for a
two-semester course and paced to admit more extensive treatment of
areas of specific interest to the instructor and students. We have
great confidence in the ability, industry, and persistence of
graduate students in ferreting out and understanding the omitted
proofs and results. In the end, this is how one gains maturity and
a fuller appreciation for the subject in any case. It is assumed
that the readers of the book will have had an econometric methods
course, using texts like J. Johnston's Econometric Methods, 2nd ed.
The editors are pleased to offer the following papers to the reader
in recognition and appreciation of the contributions to our
literature made by Robert Engle and Sir Clive Granger, winners of
the 2003 Nobel Prize in Economics. The basic themes of this part of
Volume 20 of Advances in Econometrics are time varying betas of the
capital asset pricing model, analysis of predictive densities of
nonlinear models of stock returns, modelling multivariate dynamic
correlations, flexible seasonal time series models, estimation of
long-memory time series models, the application of the technique of
boosting in volatility forecasting, the use of different time
scales in GARCH modelling, out-of-sample evaluation of the Fed
Model in stock price valuation, structural change as an alternative
to long memory, the use of smooth transition auto-regressions in
stochastic volatility modelling, the analysis of the balanced-ness
of regressions analyzing Taylor-Type rules of the Fed Funds rate, a
mixture-of-experts approach for the estimation of stochastic
volatility, a modern assessment of Clives first published paper on
Sunspot activity, and a new class of models of tail-dependence in
time series subject to jumps.
*This Series: Aids in the diffusion of new econometric
techniques
* Emphasis is placed on expositional clarity and ease of
assimilation for readers who are unfamiliar with a given topic of a
volume
*Illustrates new concepts
The editors are pleased to offer the following papers to the reader
in recognition and appreciation of the contributions to our
literature made by Robert Engle and Sir Clive Granger, winners of
the 2003 Nobel Prize in Economics. The basic themes of this part of
Volume 20 of Advances in Econometrics are time varying betas of the
capital asset pricing model, analysis of predictive densities of
nonlinear models of stock returns, modelling multivariate dynamic
correlations, flexible seasonal time series models, estimation of
long-memory time series models, the application of the technique of
boosting in volatility forecasting, the use of different time
scales in GARCH modelling, out-of-sample evaluation of the Fed
Model in stock price valuation, structural change as an alternative
to long memory, the use of smooth transition auto-regressions in
stochastic volatility modelling, the analysis of the balanced-ness
of regressions analyzing Taylor-Type rules of the Fed Funds rate, a
mixture-of-experts approach for the estimation of stochastic
volatility, a modern assessment of Clives first published paper on
Sunspot activity, and a new class of models of tail-dependence in
time series subject to jumps.
*This Series: Aids in the diffusion of new econometric
techniques
*Emphasis is placed on expositional clarity and ease of
assimilation for readers who are unfamiliar with a given topic of a
volume
*Illustrates new concepts
The entropy concept was developed and used by Shannon in 1940 as a
measure of uncertainty in the context of information theory. In
1957 Jaynes made use of Shannon's entropy concept as a basis for
estimation and inference in problems that are ill-suited for
traditional statistical procedures. This volume consists of two
sections. The first section contains papers developing econometric
methods based on the entropy principle. An interesting array of
applications is presented in the second section of the volume.
The main theme of this volume is credit risk and credit
derivatives. Recent developments in financial markets show that
appropriate modeling and quantification of credit risk is
fundamental in the context of modern complex structured financial
products. The reader will find several points of view on credit
risk when looked at from the perspective of Econometrics and
Financial Mathematics. The volume consists of eleven contributions
by both practitioners and theoreticians with expertise in financial
markets, in general, and econometrics and mathematical finance in
particular. The challenge of modeling defaults and their
correlations is addressed, and new results on copula, reduced form
and structural models, and the top-down approach are presented.
After the so-called subprime crisis that hit global markets in the
summer of 2007, the volume is very timely and will be useful to
researchers in the area of credit risk.
1 Introduction.- 2 Review of Ordinary Least Squares and Generalized
Least Squares.- 3 Point Estimation and Tests of Hypotheses in Small
Samples.- 4 Large Sample Point Estimation and Tests of Hypotheses.-
5 Stochastic Regressors.- 6 Use of Prior Information.- 7
Preliminary Test and Stein-Rule Estimators.- 8 Feasible Generalized
Least Squares Estimation.- 9 Heteroscedasticity.- 10
Autocorrelation.- 11 Lagged Dependent Variables and
Autcorrelation.- 12 Unobservable Variables.- 13 Multicollinearity.-
14 Varying Coefficient Models.- 15 Models That Combine Time-Series
and Cross-Section Data.- 16 The Analysis of Models with Qualitative
or Censored Dependent Variables.- 17 Distributed Lags.- 18
Uncertainty in Model Specification and Selection.- 19 Introduction
to Simultaneous Equations Models.- 20 Identification.- 21 Limited
Information Estimation.- 22 Full Information Estimation.- 23
Reduced Form Estimation and Prediction in Simultaneous Equations
Models.- 24 Properties of Dynamic Simultaneous Equations Models.-
25 Special Topics in Simultaneous Equations.- Appendix Estimation
and Inference in Nonlinear Statistical Models.- A.1 Nonlinear
Optimization.- A.1.1 Method of Steepest Ascent.- A.1.2 The Method
of Newton.- A.1.3 Method of Quadratic Hill Climbing.- A.1.4
Numerical Differentiation.- A.2 Maximum Likelihood Estimation.-
A.2.1 Use of the Method of Newton.- A.2.2 Method of Scoring.- A.2.3
The Method of Berndt, Hall, Hall, and Hausman.- A.2.4 Asymptotic
Tests Based on the Maximum Likelihood Method.- A.2.4a The Wald
Test.- A.2.4b The Lagrange-Multiplier Test.- A.2.4c The Likelihood
Ratio Test Statistic.- A.2.4d Concluding Remarks.- A.3 Nonlinear
Regression.- A.4 Summary and Guide to Further Readings.- A.5
References.
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