Adv Econometrics Final 1.2

Y = B0 + B1T + B2X + u

The equation represents the true data-generating process of an outcome where Y is dependent on treatment T and spatial proximity factor X, with B1 and B2 as coefficients.

Omitted Variable Bias

A bias that occurs when a relevant variable is not included in a regression model, potentially leading to incorrect estimates of the effect of the included variables.

Conley Standard Errors

A method to correct standard errors for spatial correlation and heteroskedasticity in regression models, accounting for spatial dependence by estimating covariance between observations within a set distance.

Fixed Effects Models

Statistical models that control for unobservable time-invariant characteristics across units, helping to isolate causal effects of time-varying variables.

Parallel Trends Assumption

An assumption in difference-in-difference analysis stating that control and treatment groups would have followed similar trends over time in the absence of treatment.

Random Forest

A machine learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.

Mean Squared Error (MSE)

A commonly used metric for evaluating the performance of regression models, measuring the average of the squares of the errors.

Spatial Correlation in Errors

A phenomenon where errors are correlated across space, which can lead to underestimated standard errors and too much confidence in statistical significance.

Regression Discontinuity Design

A quasi-experimental design that estimates the causal effects of interventions by assigning treatment based on a cutoff point in a continuous variable.

Spatial Autocorrelation

The correlation of a variable with itself through space; when events that are geographically close are more similar than those farther apart.