Random Coefficients Regression

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Presentation transcript:

Random Coefficients Regression RPD – Section 18.3

Basic Model Simple Linear Regression where each of n experimental units is observed at t points in time (typically)

General Model (Gumpertz and Pantula (1989)) Possibly Multiple Linear Regression where each of n experimental units is observed at t points in time, based on regression with k parameters

Estimating Individual/Population Regression Parameters

Estimating Variance Parameters - I

Estimating Variance Parameters - II

Example – Annual Air Revenues for 10 Markets Random Sample of n = 10 large air markets (City Pairs), each observed over 5 years Y = ln(Average Fare * Average weekly Passengers) X = Year (1996/7=0, 2000/1=4) – Note: All Cities have same levels of X (not necessary for the method)

Air Revenue Data II