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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.
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 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
This volume is the result of an Advances in Econometrics conference
held in November of 2002 at Louisiana State University in
recognition of Halbert White's pioneering work published in
Econometrica in 1980 and 1982 on robust variance-covariance
estimation and quasi-maximum likelihood estimation. It contains 11
papers on a range of related topics including the estimation of
possibly misspecified error component and fixed effects panel
models, estimation and inference in possibly misspecified quantile
regression models, quasi-maximum likelihood estimation of linear
regression models with bounded and symmetric errors and
quasi-maximum likelihood estimation of models with parameter
dependencies between the mean vector and error variance-covariance
matrix. Other topics include GMM, HAC, Heckit, asymmetric GARCH,
Cross-Entropy, and multivariate deterministic trend estimation and
testing under various possible misspecifications.
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 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.
The 'Advances in Econometrics' series aims to publish annual
original scholarly econometrics papers on designated topics with
the intention of expanding the use of developed and emerging
econometric techniques by disseminating ideas on the theory and
practice of econometrics throughout the empirical economic,
business and social science literature.
This volume is a collection of methodological developments and
applications of simulation-based methods that were presented at a
workshop at Louisiana State University in November, 2009. The first
two papers are extensions of the GHK simulator: one reconsiders the
computation of the probabilities in a discrete choice model while
another example uses an adaptive version of sparse-grids
integration (SGI) instead of simulation. Two studies are focused
specifically on the methodology: the first compares the performance
of the maximum-simulated likelihood (MSL) approach with a proposed
composite marginal likelihood (CML) approach in multivariate
ordered-response situations, while the second examines methods of
testing for the presence of heterogeneity in the heterogeneity
model. Further topics examined include: education savings accounts,
parent contributions and education attainment; estimating the
effect of exchange rate flexibility on financial account openness;
estimating a fractional response model with a count endogenous
regressor; and modelling and forecasting volatility in a bayesian
approach.
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.
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.
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