1. Random Coefficients Regression
RPD – Section 18.3

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

3. 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

4. Estimating Individual/Population Regression Parameters

5. Estimating Variance Parameters - I

6. Estimating Variance Parameters - II

7. 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)

8. Air Revenue Data II