Analysis of Microdata

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  • Author : Rainer Winkelmann
  • Publisher : Springer Science & Business Media
  • Pages : 313 pages
  • ISBN : 3540296077
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Analysis of Microdata

Download or Read online Analysis of Microdata full in PDF, ePub and kindle. this book written by Rainer Winkelmann and published by Springer Science & Business Media which was released on 21 September 2006 with total page 313 pages. We cannot guarantee that Analysis of Microdata book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. The availability of microdata has increased rapidly over the last decades, and standard statistical and econometric software packages for data analysis include ever more sophisticated modeling options. The goal of this book is to familiarize readers with a wide range of commonly used models, and thereby to enable them to become critical consumers of current empirical research, and to conduct their own empirical analyses. The focus of the book is on regression-type models in the context of large cross-section samples. In microdata applications, dependent variables often are qualitative and discrete, while in other cases, the sample is not randomly drawn from the population of interest and the dependent variable is censored or truncated. Hence, models and methods are required that go beyond the standard linear regression model and ordinary least squares. Maximum li- lihood estimation of conditional probability models and marginal probability e?ects are introduced here as the unifying principle for modeling, estimating and interpreting microdata relationships. We consider the limitation to m- imum likelihood sensible, from a pedagogical point of view if the book is to be used in a semester-long advanced undergraduate or graduate course, and from a practical point of view because maximum likelihood estimation is used in the overwhelming majority of current microdata research. In order to introduce and explain the models and methods, we refer to a number of illustrative applications. The main examples include the deter- nants of individual fertility, the intergenerational transmission of secondary schoolchoices,andthewageelasticityoffemalelaborsupply.

Analysis of Microdata

Analysis of Microdata
  • Author : Rainer Winkelmann,Stefan Boes
  • Publisher : Springer Science & Business Media
  • Release : 21 September 2006
GET THIS BOOK Analysis of Microdata

The availability of microdata has increased rapidly over the last decades, and standard statistical and econometric software packages for data analysis include ever more sophisticated modeling options. The goal of this book is to familiarize readers with a wide range of commonly used models, and thereby to enable them to become critical consumers of current empirical research, and to conduct their own empirical analyses. The focus of the book is on regression-type models in the context of large cross-section samples.

Macro and Microdata Analyses and Their Integration

Macro  and Microdata Analyses and Their Integration
  • Author : Nancy D. Ruggles,Richard Ruggles
  • Publisher : Edward Elgar Pub
  • Release : 16 April 1999
GET THIS BOOK Macro and Microdata Analyses and Their Integration

In this book, Nancy and Richard Ruggles demonstrate their unique grasp of the measurement and analysis of macro and micro data and elucidate ways of integrating the two data sets.Their analysis of macrodata is used to examine the economic growth of the United States from the 1920s to the present day. They focus particularly on recession and recovery between 1929 and 1974 and the measurement of short-run economic growth. They also examine the measurement of saving, investment and capital formation in

Spatial Econometrics using Microdata

Spatial Econometrics using Microdata
  • Author : Jean Dubé,Diègo Legros
  • Publisher : John Wiley & Sons
  • Release : 25 September 2014
GET THIS BOOK Spatial Econometrics using Microdata

This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data. Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency. The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the