Optimization Heuristics in Econometrics: Applications of Threshold Accepting

Many problems in statistics and econometrics offer themselves naturally to
the use of optimization heuristics. Standard methods applied to highly
complex problems often produce approximate results, of unknown quality,
based on heavy assumptions. Optimization heuristic methods provide
powerful results to many complex problems. combined with relatively
simple implementation.
∗ Offers a self–contained introduction to optimization heuristics in econometrics and statistics
∗ Features many examples of optimization heuristic methods applied to real problems
∗ Includes detailed coverage of the threshold accepting heuristic
∗ Provides suggestions for further reading
Split into three parts, the book opens with a general introduction to
optimization in statistics and econometrics, followed by detailed discussion
of a relatively new and very powerful optimization heuristic, threshold
accepting. The final part consists of many applications of the methods
described earlier, encompassing experimental design, model selection,
aggregation of tiime series, and censored quantile regression models.
Those researching and working in econometrics, statistics and operations
research are given the tools to apply optimization heuristic methods in their
work. Postgraduate students of statistics and econometrics will find the
book provides a good introduction to optimization heuristic methods.

Spis treści:
Preface.
Introduction.
OPTIMIZATION IN STATISTICS AND ECONOMETRICS.
Optimization in Economics.
Optimization in Statistics and Econometrics.
The Heuristic Optimization Paradigm.
HEURISTIC OPTIMIZATION: THRESHOLD ACCEPTING.
Optimization Methods.
The Global Optimization Heuristic Threshold Accepting.
Relative Performance of Threshold Accepting.
Tuning of Threshold Accepting.
A Practical Guide to the Implementation of Threshold Accepting.
APPLICATIONS IN STATISTICS AND ECONOMETRICS.
Introduction.
Experimental Design.
Identification of Multivariate Lag Structures.
Optimal Aggregation.
Censored Quantile Regression.
Continuous Global Optimization.
CONCLUSION AND OUTLOOK.
Conclusion.
Outlook for Further Research.
References.
List of Symbols.
Author Index.
Subject Index.

Okładka tylna:
Many problems in statistics and econometrics offer themselves naturally to
the use of optimization heuristics. Standard methods applied to highly
complex problems often produce approximate results, of unknown quality,
based on heavy assumptions. Optimization heuristic methods provide
powerful results to many complex problems. combined with relatively
simple implementation.
∗ Offers a self–contained introduction to optimization heuristics in econometrics and statistics
∗ Features many examples of optimization heuristic methods applied to real problems
∗ Includes detailed coverage of the threshold accepting heuristic
∗ Provides suggestions for further reading
Split into three parts, the book opens with a general introduction to
optimization in statistics and econometrics, followed by detailed discussion
of a relatively new and very powerful optimization heuristic, threshold
accepting. The final part consists of many applications of the methods
described earlier, encompassing experimental design, model selection,
aggregation of tiime series, and censored quantile regression models.
Those researching and working in econometrics, statistics and operations
research are given the tools to apply optimization heuristic methods in their
work. Postgraduate students of statistics and econometrics will find the
book provides a good introduction to optimization heuristic methods.