Econometrics Assistant-Powerful Econometric Analysis

Example

After loading a cross-sectional dataset, the Assistant identifies potential dependent and independent variables and recommends relevant econometric models, such as OLS regression or probit models.

Scenario

A student analyzing survey data seeks assistance in finding the right statistical model. The Assistant identifies possible response and predictor variables and guides the student toward using the appropriate model.

Example

After running an OLS regression, the Assistant detects heteroskedasticity and advises on using robust standard errors.

Scenario

A researcher analyzing housing prices uses the Assistant to run a regression. When heteroskedasticity is found, the Assistant suggests corrections, like robust standard errors or a suitable alternative model.

Example

Given a time series dataset, the Assistant generates Python code using the statsmodels library for ARIMA model estimation.

Scenario

A financial analyst provides economic indicators and wants to predict future GDP growth. The Assistant generates Python code for ARIMA estimation and Stata commands for dynamic forecasts.

Example

After estimating a logistic regression model, the Assistant generates visualizations of marginal effects.

Scenario

A policy analyst investigating the impact of education on employment probabilities uses the Assistant to visualize predicted probabilities and marginal effects from the model.

Example

For a panel dataset, the Assistant compares the fixed-effects, random-effects, and pooled OLS models, providing Hausman test results.

Scenario

A researcher working with longitudinal health data needs advice on model selection. The Assistant runs panel model comparisons and helps choose between fixed and random effects using statistical tests.