عنوان

Statistical inference in random coefficient regression models

(Number (ISBN

0387056033

(Number (ISBN

3540056033

(Number (ISBN

9780387056036

(Number (ISBN

9783540056034

Number

b545501

Title Proper

Statistical inference in random coefficient regression models

General Material Designation

[Book]

First Statement of Responsibility

P A V B Swamy

Name of Publisher, Distributor, etc.

Berlin

Date of Publication, Distribution, etc.

1971, 1971

Specific Material Designation and Extent of Item

209 s

Series Title

Lecture notes in operations research and mathematical systems, 55

Text of Note

[Bibl.s.204-209].

Text of Note

I -- Introduction.- 1.1 Purpose and Outline of the Study.- 1.2 Review of the Literature on Regression Models with Random and Fixed Coefficients.- 1.3 Conclusions.- II -- Efficient Methods of Estimating a Regression Equation with Equicorrelated Disturbances.- 2.1 Introduction.- 2.2 Some Useful Lemmas.- 2.3 A Regression Model with Equicorrelated Disturbances.- 2.4 Analysis of Time Series of Cross-Sections.- 2.5 Estimation When the Variance-Covariance Matrix of Disturbances is Singular.- 2.6 Estimation When the Remaining Effects are Heteroskedastic.- 2.7 Conclusions.- III -- Efficient Methods of Estimating the Error Components Regression Models.- 3.1 Introduction.- 3.2 Some Matrix Results.- 3.3 Covariance Estimators.- 3.4 Estimation of Error Components Models.- 3.5 A Class of Asymptotically Efficient Estimators.- 3.6 Small Sample Properties of the Pooled Estimator.- 3.7 A Comparison of the Efficiencies of Pooled and OLS Estimators.- 3.8 A Comparison of the Efficiency of Pooled Estimator with Those of its Components.- 3.9 Alternative Estimators of Slope Coefficients and the Regression on Lagged Values of the Dependent Variables.- 3.10 Analysis of an Error Components Model Under Alternative Assumptions.- 3.11 Maximum Likelihood Method of Estimating Error Components Model.- 3.12 Departures from the Basic Assumptions Underlying the Error Components Model.- 3.13 Conclusions.- IV -- Statistical Inference in Random Coefficient Regression Models Using Panel Data.- 4.1 Introduction.- 4.2 Setting the Problem.- 4.3 Efficient Methods of Estimating the Parameters of RCR Models.- 4.4 Estimation of Parameters in RCR Models when Disturbances are Serially Correlated.- 4.5 Problems Associated with the Estimation of RCR Models Using Aggregate Data.- 4.6 Forecasting with RCR Models.- 4.7 Relaxation of Assumptions Underlying RCR Models.- 4.8 Similarities Between RCR and Bayesian Assumptions.- 4.9 Empirical CES Production Function Free of Management Bias.- 4.10 Analysis of Mixed Models.- 4.11 Conclusions.- V -- A Random Coefficient Investment Model.- 5.1 Introduction.- 5.2 Grunfeld's Hypothesis of Micro Investment Behavior.- 5.3 Estimation and Testing of Random Coefficient Investment Model.- 5.4 Aggregate Investment Function.- 5.5 Comparison of Random Coefficient Model with Fixed Coefficient Macro Model.- 5.6 Comparison of Random Coefficient Model with Fixed Coefficient Micro Model.- 5.7 Conclusions.- VI -- Aggregate Consumption Function with Coefficients Random Across Countries.- 6.1 Introduction.- 6.2 Aggregate Consumption Model.- 6.3 Source and Nature of Data.- 6.4 Fixed Coefficient Approach.- 6.5 Random Coefficient Approach.- 6.6 Conclusions.- VII -- Miscellaneous Topics.- 7.1 Introduction.- 7.2 Identification.- 7.3 Incorporation of Prior Information in the Estimation of RCR Models.- 7.4 Conclusions.

P A V B Swamy

P A V B Swamy

Electronic name

[Book]

Y