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Linear Regression Analysis in Econometrics Study Guide

This guide covers the fundamental concepts of linear regression analysis as applied in econometrics. It explains simple and multiple linear regression models, the Ordinary Least Squares (OLS) method for parameter estimation, and the critical assumptions required for unbiased and efficient estimators, such as the Gauss-Markov assumptions. The guide also details how to measure the goodness-of-fit of regression models using metrics like R-squared and Adjusted R-squared, and discusses potential issu

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Simple and Multiple Linear Regression Analysis

Review of Linear Regression Analysis

The goal of econometrics is to use sample data to obtain estimates of unknown population parameters through simple and multiple linear regression models.

  • Simple linear regression model
  • Multiple regression
  • Ordinary least squares (OLS)
  • OLS estimator
  • Zero conditional mean
  • Unbiasedness of OLS parameters
  • Homoskedasticity
  • Ceteris paribus interpretation
  • Partially out interpretation
  • Goodness-of-fit
  • Total sum of squares (SST)
  • Explained sum of squares (SSE)
  • Residual sum of squares (SSR)
  • Standard error of the regression (SER)
  • Root mean squared error (RMSE)
  • Adjusted R-squared

Assumptions for Unbiasedness and Multicollinearity

Assumptions for Unbiasedness in MLR

This review covers assumptions for unbiasedness in Multiple Linear Regression, including exogenous explanatory variables, omitted variable bias, perfect collinearity, and imperfect multicollinearity.

  • Exogenous explanatory variables
  • Endogenous variables
  • Omitted variable bias
  • Perfect Collinearity
  • Dummy variable trap
  • Imperfect Multicollinearity
  • Standard errors

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