Publisher | ## Elsevier Publishing Company |

Publication Year | 2002 |

ISBN-13 | ## ISBN 9780124406568 |

ISBN-10 | 0124406564 |

Binding | ## Hardback |

Number of Pages | 195 Pages |

Language | (English) |

Subject | ## General science |

Applied econometric research" is concerned with the measurement of the parameters of economic relationships and with the prediction (by means of these parameters) of the values of economic variables

"Dependent variable" is the value of a function that is determined by the function and the value(s) chosen for its independent variable(s). The "generalized method of moments (GMM) estimation" has emerged over the last decade as providing a ready to use, flexible tool of application to a large number of econometric and economic models by relying on mild, plausible assumptions. "Panel, or longitudinal, data," are data on constant experimental units over a period of time. "Nonparametric methods" are any of various inferential procedures whose conclusions do not rely on assumptions about the distribution of the population of interest.

This book uses a GMM approach to make its presentation of panel data methods for weak model assumptions ("semiparametric"). These assumptions are useful because they can offer general approaches and explain real problems. So while the subjects covered by this book are narrower than those appearing in a comprehensive book on panel data, the utility of the material (that is, the book's ability to make accessible practical computation and implementation methods) is higher.

An economic system typically consists of many interdependent variables and the relationships among them. In estimating the equations of such systems, econometricians frequently encounter an obstacle known as "the identification problem." The latter is most easily illustrated by reference to the process of determination of price and output in a market. To model this process theeconometrician must develop a quantitative estimate of both the demand and supply functions. Typically the data used to estimate these functions are past observations of price and output determined by the points of intersection between the demand and supply curves. If, in the past, the supply curve has been shifting (due, say, to production cost changes) while the demand curve has remained fixed, the resultant intersection points trace out the demand function. If the demand curve has shifted (due, say, to income changes) while the supply curve has remained fixed, the intersection points trace out the supply curve. The most likely outcome is movement of both curves yielding a pattern of price, quantity intersection points from which the econometrician will be unable, without further information, to distinguish the demand curve from the supply curve or estimate the parameters of either. This is the identification problem.

Key Features

* Describes recent developments in panel-data econometrics

* Emphasizes estimation methods

* Focuses on practical implemention and computational feasibility of estimation methods

* Compares parametric and semiparametric approaches, highlighting advantages of new methods

* Provides distribution-free estimators for limited response models

* Includes standard programs with accompanying data sets on disk

* Presents computational steps and recent methods in panel data analysis

* Describes main theoretical ideas behind the generalized method of moments (GMM) estimation

"Dependent variable" is the value of a function that is determined by the function and the value(s) chosen for its independent variable(s). The "generalized method of moments (GMM) estimation" has emerged over the last decade as providing a ready to use, flexible tool of application to a large number of econometric and economic models by relying on mild, plausible assumptions. "Panel, or longitudinal, data," are data on constant experimental units over a period of time. "Nonparametric methods" are any of various inferential procedures whose conclusions do not rely on assumptions about the distribution of the population of interest.

This book uses a GMM approach to make its presentation of panel data methods for weak model assumptions ("semiparametric"). These assumptions are useful because they can offer general approaches and explain real problems. So while the subjects covered by this book are narrower than those appearing in a comprehensive book on panel data, the utility of the material (that is, the book's ability to make accessible practical computation and implementation methods) is higher.

An economic system typically consists of many interdependent variables and the relationships among them. In estimating the equations of such systems, econometricians frequently encounter an obstacle known as "the identification problem." The latter is most easily illustrated by reference to the process of determination of price and output in a market. To model this process theeconometrician must develop a quantitative estimate of both the demand and supply functions. Typically the data used to estimate these functions are past observations of price and output determined by the points of intersection between the demand and supply curves. If, in the past, the supply curve has been shifting (due, say, to production cost changes) while the demand curve has remained fixed, the resultant intersection points trace out the demand function. If the demand curve has shifted (due, say, to income changes) while the supply curve has remained fixed, the intersection points trace out the supply curve. The most likely outcome is movement of both curves yielding a pattern of price, quantity intersection points from which the econometrician will be unable, without further information, to distinguish the demand curve from the supply curve or estimate the parameters of either. This is the identification problem.

Key Features

* Describes recent developments in panel-data econometrics

* Emphasizes estimation methods

* Focuses on practical implemention and computational feasibility of estimation methods

* Compares parametric and semiparametric approaches, highlighting advantages of new methods

* Provides distribution-free estimators for limited response models

* Includes standard programs with accompanying data sets on disk

* Presents computational steps and recent methods in panel data analysis

* Describes main theoretical ideas behind the generalized method of moments (GMM) estimation

Author:## Myoung-Jae Lee

Publisher:## Elsevier Publishing Company