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Varying Coefficient Models

  • Thomas B. Fomby
  • Stanley R. Johnson
  • R. Carter Hill

Abstract

In this and the following chapter we consider ways to combine sets of data for the purpose of estimation that may violate a basic assumption of the linear regression model. Specifically, we will study cases when it cannot be assumed that the structural parameters are identical for all observations in a sample of data. Frequently these problems occur when a data set consists of series of observations over time on cross-sectional units. Many of the topics discussed in this chapter can or do apply directly to the pooling of time series and cross-sectional data, but the primary discussion of that topic is contained in Chapter 15. In this chapter we focus on models with parameters that vary in some systematic and/or random way across partitions of the sample data, even from observation to observation.

Keywords

Regression Function Coefficient Model American Statistical Association Random Coefficient Random Disturbance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1984

Authors and Affiliations

  • Thomas B. Fomby
    • 1
  • Stanley R. Johnson
    • 2
  • R. Carter Hill
    • 3
  1. 1.Department of EconomicsSouthern Methodist UniversityDallasUSA
  2. 2.The Center for Agricultural and Rural DevelopmentIowa State UniversityAmesUSA
  3. 3.Department of EconomicsLouisiana State UniversityBaton RougeUSA

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