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Limit Theorems for Nonlinear Cointegrating Regression
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Limit Theorems for Nonlinear Cointegrating Regression Hardcover - 2015

by Qiying Wang


From the publisher

This book provides the limit theorems that can be used in the development of nonlinear cointegrating regression. The topics include weak convergence to a local time process, weak convergence to a mixture of normal distributions and weak convergence to stochastic integrals. This book also investigates estimation and inference theory in nonlinear cointegrating regression.The core context of this book comes from the author and his collaborator's current researches in past years, which is wide enough to cover the knowledge bases in nonlinear cointegrating regression. It may be used as a main reference book for future researchers.

From the jacket flap

This book provides the limit theorems that can be used in the development of nonlinear cointegrating regression. The topics include weak convergence to a local time process, weak convergence to a mixture of normal distributions and weak convergence to stochastic integrals. This book also investigates estimation and inference theory in nonlinear contegrating regression. The core context of this book comes from the author and his collaborator's current researches in past years, which is wide enough to cover the knowledge bases in nonlinear cointegrating regression. It may be used as a main reference book for future researchers.

Details

  • Title Limit Theorems for Nonlinear Cointegrating Regression
  • Author Qiying Wang
  • Binding Hardcover
  • Pages 272
  • Volumes 1
  • Language ENG
  • Publisher World Scientific Publishing Company
  • Date 2015-08-25
  • ISBN 9789814675628 / 9814675628
  • Weight 1.2 lbs (0.54 kg)
  • Dimensions 9.1 x 6 x 0.8 in (23.11 x 15.24 x 2.03 cm)
  • Library of Congress Catalog Number 2015017266
  • Dewey Decimal Code 519.536
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Limit Theorems for Nonlinear Cointegrating Regression (Nonlinear Time Series and Chaos)
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LIMIT THEOREMS FOR NONLINEAR COINTEGRATING REGRESSION (Nonlinear Time Series and Chaos)
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