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Random Coefficients Regression

Explore the basics of Random Coefficients Regression in statistical analysis, including Simple and Multiple Linear Regression models. Learn how to estimate Individual and Population Regression Parameters and Variance Parameters with practical examples and techniques.

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Random Coefficients Regression

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  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

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