Author: Jayalakshmi Krishnakumar
Publisher: Springer Science & Business Media
ISBN: 3642456472
Category : Business & Economics
Languages : en
Pages : 371
Book Description
Economists can rarely perform controlled experiments to generate data. Existing information in the form of real-life observations simply has to be utilized in the best possible way. Given this, it is advantageous to make use of the increasing availability and accessibility of combinations of time-series and cross-sectional data in the estimation of economic models. But such data call for a new methodology of estimation and hence for the development of new econometric models. This book proposes one such new model which introduces error components in a system of simultaneous equations to take into account the temporal and cross-sectional heterogeneity of panel data. After a substantial survey of panel data models, the newly proposed model is presented in detail and indirect estimations, full information and limited information estimations, and estimations with and without the assumption of normal distribution errors. These estimation methods are then applied using a computer to estimate a model of residential electricity demand using data on American households. The results are analysed both from an economic and from a statistical point of view.
Estimation of Simultaneous Equation Models with Error Components Structure
Author: Jayalakshmi Krishnakumar
Publisher: Springer Science & Business Media
ISBN: 3642456472
Category : Business & Economics
Languages : en
Pages : 371
Book Description
Economists can rarely perform controlled experiments to generate data. Existing information in the form of real-life observations simply has to be utilized in the best possible way. Given this, it is advantageous to make use of the increasing availability and accessibility of combinations of time-series and cross-sectional data in the estimation of economic models. But such data call for a new methodology of estimation and hence for the development of new econometric models. This book proposes one such new model which introduces error components in a system of simultaneous equations to take into account the temporal and cross-sectional heterogeneity of panel data. After a substantial survey of panel data models, the newly proposed model is presented in detail and indirect estimations, full information and limited information estimations, and estimations with and without the assumption of normal distribution errors. These estimation methods are then applied using a computer to estimate a model of residential electricity demand using data on American households. The results are analysed both from an economic and from a statistical point of view.
Publisher: Springer Science & Business Media
ISBN: 3642456472
Category : Business & Economics
Languages : en
Pages : 371
Book Description
Economists can rarely perform controlled experiments to generate data. Existing information in the form of real-life observations simply has to be utilized in the best possible way. Given this, it is advantageous to make use of the increasing availability and accessibility of combinations of time-series and cross-sectional data in the estimation of economic models. But such data call for a new methodology of estimation and hence for the development of new econometric models. This book proposes one such new model which introduces error components in a system of simultaneous equations to take into account the temporal and cross-sectional heterogeneity of panel data. After a substantial survey of panel data models, the newly proposed model is presented in detail and indirect estimations, full information and limited information estimations, and estimations with and without the assumption of normal distribution errors. These estimation methods are then applied using a computer to estimate a model of residential electricity demand using data on American households. The results are analysed both from an economic and from a statistical point of view.
Estimation of Simultaneous Linear Equation Models with Error Components Structure
Author: Jayalakshmi Varadharajan
Publisher:
ISBN:
Category :
Languages : en
Pages : 64
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 64
Book Description
Simultaneous Equation Models with Measurement Error
Author: Vincent J. Geraci
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 342
Book Description
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 342
Book Description
Contributions to a General Asymptotic Statistical Theory
Author: J. Pfanzagl
Publisher: Springer Science & Business Media
ISBN: 1461257697
Category : Mathematics
Languages : en
Pages : 324
Book Description
Publisher: Springer Science & Business Media
ISBN: 1461257697
Category : Mathematics
Languages : en
Pages : 324
Book Description
The Relative Efficiency of Instrumental Variables Estimators for the Linear Simultaneous Equations Model
Author: James McConnell Brundy
Publisher:
ISBN:
Category :
Languages : en
Pages : 298
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 298
Book Description
Estimation of Simultaneous Equations Error Components Models with an Application to a Model of Developing Country Foreign Trade
Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity
Author: Guido Imbens
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 60
Book Description
This paper investigates identification and inference in a nonparametric structural model with instrumental variables and non-additive errors. We allow for non-additive errors because the unobserved heterogeneity in marginal returns that often motivates concerns about endogeneity of choices requires objective functions that are non-additive in observed and unobserved components. We formulate several independence and monotonicity conditions that are sufficient for identification of a number of objects of interest, including the average conditional response, the average structural function, as well as the full structural response function. For inference we propose a two-step series estimator. The first step consists of estimating the conditional distribution of the endogenous regressor given the instrument. In the second step the estimated conditional distribution function is used as a regressor in a nonlinear control function approach. We establish rates of convergence, asymptotic normality, and give a consistent asymptotic variance estimator.
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 60
Book Description
This paper investigates identification and inference in a nonparametric structural model with instrumental variables and non-additive errors. We allow for non-additive errors because the unobserved heterogeneity in marginal returns that often motivates concerns about endogeneity of choices requires objective functions that are non-additive in observed and unobserved components. We formulate several independence and monotonicity conditions that are sufficient for identification of a number of objects of interest, including the average conditional response, the average structural function, as well as the full structural response function. For inference we propose a two-step series estimator. The first step consists of estimating the conditional distribution of the endogenous regressor given the instrument. In the second step the estimated conditional distribution function is used as a regressor in a nonlinear control function approach. We establish rates of convergence, asymptotic normality, and give a consistent asymptotic variance estimator.
Instrumental-variable Estimation of a Panel Data Model
Author: Donald J. Wyhowski
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 354
Book Description
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 354
Book Description
Simultaneous Equation Models with Spatially Autocorrelated Error Components
Author: Marius Amba
Publisher:
ISBN:
Category :
Languages : en
Pages : 42
Book Description
This paper develops estimators for simultaneous equations with spatial autoregressive or spatial moving average error components. We derive a limited information estimator and a full information estimator. We give the generalized method of moments to get each coefficient of the spatial dependence of each equation in spatial autoregressive case as well as spatial moving average case. The results of our Monte Carlo suggest that our estimators are consistent. When we estimate the coefficient of spatial dependence it seems better to use instrumental variables estimator that takes into account simultaneity. We also apply these set of estimators on real data.
Publisher:
ISBN:
Category :
Languages : en
Pages : 42
Book Description
This paper develops estimators for simultaneous equations with spatial autoregressive or spatial moving average error components. We derive a limited information estimator and a full information estimator. We give the generalized method of moments to get each coefficient of the spatial dependence of each equation in spatial autoregressive case as well as spatial moving average case. The results of our Monte Carlo suggest that our estimators are consistent. When we estimate the coefficient of spatial dependence it seems better to use instrumental variables estimator that takes into account simultaneity. We also apply these set of estimators on real data.
Applied Structural Equation Modelling for Researchers and Practitioners
Author: Indranarain Ramlall
Publisher: Emerald Group Publishing
ISBN: 1786358824
Category : Education
Languages : en
Pages : 152
Book Description
This book explains in a rigorous, concise and practical manner all the vital components embedded in structural equation modelling. Focusing on R and stata to implement and perform various structural equation models.
Publisher: Emerald Group Publishing
ISBN: 1786358824
Category : Education
Languages : en
Pages : 152
Book Description
This book explains in a rigorous, concise and practical manner all the vital components embedded in structural equation modelling. Focusing on R and stata to implement and perform various structural equation models.