https://openeconometrics.com/methods/ Econometric methods in Python, one page each: what the estimator does, its OpenEconometrics function and Stata command, options, a worked example and sources. en Mon, 19 Oct 2026 00:00:00 +0000 https://openeconometrics.com/methods/arima https://openeconometrics.com/methods/arima Mon, 19 Oct 2026 00:00:00 +0000 Time series Fit ARIMA, seasonal ARIMA and regression models with ARMA errors in Python by exact maximum likelihood, check the residuals and forecast. https://openeconometrics.com/methods/difference-in-differences https://openeconometrics.com/methods/difference-in-differences Mon, 19 Oct 2026 00:00:00 +0000 Causal inference Estimate a two-way fixed-effects difference-in-differences model in Python, cluster by group, test parallel trends and map it to Stata's didregress. https://openeconometrics.com/methods/panel-fixed-effects-random-effects https://openeconometrics.com/methods/panel-fixed-effects-random-effects Mon, 19 Oct 2026 00:00:00 +0000 Panel data Fit fixed-effects, random-effects, between, first-difference and pooled panel regressions in Python with clustered or Driscoll–Kraay standard errors. https://openeconometrics.com/methods/hausman-test https://openeconometrics.com/methods/hausman-test Mon, 19 Oct 2026 00:00:00 +0000 Tests and inference Run the Hausman test of fixed against random effects in Python, read what it does and does not show, and use the cluster-robust Mundlak alternative. https://openeconometrics.com/methods/logit-probit https://openeconometrics.com/methods/logit-probit Mon, 19 Oct 2026 00:00:00 +0000 Limited and duration outcomes Fit logit and probit models in Python, compute average marginal effects with oe.margins, and map the workflow to Stata's logit, probit and margins. https://openeconometrics.com/methods/ols-robust-standard-errors https://openeconometrics.com/methods/ols-robust-standard-errors Mon, 19 Oct 2026 00:00:00 +0000 Linear models and IV Fit OLS and weighted least squares in Python with HC0–HC3, clustered or HAC standard errors, then run tests, linear combinations and margins. https://openeconometrics.com/methods/instrumental-variables https://openeconometrics.com/methods/instrumental-variables Mon, 19 Oct 2026 00:00:00 +0000 Linear models and IV Estimate instrumental-variables regressions in Python with 2SLS, LIML or GMM, read the weak-instrument and overidentification tests, and map them to ivregress. https://openeconometrics.com/methods/ordered-multinomial-logit https://openeconometrics.com/methods/ordered-multinomial-logit Mon, 19 Oct 2026 00:00:00 +0000 Limited and duration outcomes Fit ordered logit, ordered probit and multinomial logit models in Python, read cutpoints and category probabilities, and map them to ologit and mlogit. https://openeconometrics.com/methods/reghdfe-high-dimensional-fixed-effects https://openeconometrics.com/methods/reghdfe-high-dimensional-fixed-effects Mon, 19 Oct 2026 00:00:00 +0000 Panel data Absorb several high-dimensional fixed effects in a linear regression in Python, with singleton removal, exact degrees of freedom and clustered errors. https://openeconometrics.com/methods/staggered-difference-in-differences https://openeconometrics.com/methods/staggered-difference-in-differences Mon, 19 Oct 2026 00:00:00 +0000 Causal inference Estimate Callaway–Sant'Anna group-time ATTs for staggered adoption in Python, aggregate them by event time and see why a two-way fixed-effects DiD can mislead. https://openeconometrics.com/methods/var-vecm https://openeconometrics.com/methods/var-vecm Mon, 19 Oct 2026 00:00:00 +0000 Time series Fit vector autoregressions and Johansen error-correction models in Python, with lag selection, Granger causality tests, impulse responses and forecasts. https://openeconometrics.com/methods/wild-cluster-bootstrap https://openeconometrics.com/methods/wild-cluster-bootstrap Mon, 19 Oct 2026 00:00:00 +0000 Tests and inference Run restricted wild cluster bootstrap tests and confidence sets in Python when there are few clusters, the workflow of the boottest command in Stata. https://openeconometrics.com/methods/dynamic-panel-gmm https://openeconometrics.com/methods/dynamic-panel-gmm Mon, 19 Oct 2026 00:00:00 +0000 Panel data Estimate dynamic panel models in Python with Arellano–Bond difference GMM and Blundell–Bond system GMM, Windmeijer errors and AR(2) and Hansen tests. https://openeconometrics.com/methods/event-study https://openeconometrics.com/methods/event-study Mon, 19 Oct 2026 00:00:00 +0000 Causal inference Fit a two-way fixed-effects event study in Python, choose the leads, lags and reference period, test pre-trends and tabulate the dynamic effects. https://openeconometrics.com/methods/arch-garch https://openeconometrics.com/methods/arch-garch Mon, 19 Oct 2026 00:00:00 +0000 Time series Estimate ARCH, GARCH, GJR and EGARCH volatility models in Python by maximum likelihood, with Student-t errors, residual diagnostics and variance forecasts. https://openeconometrics.com/methods/multiple-testing https://openeconometrics.com/methods/multiple-testing Mon, 19 Oct 2026 00:00:00 +0000 Tests and inference Adjust p-values for multiple hypotheses in Python with Bonferroni, Holm, Benjamini–Hochberg or the Romano–Wolf stepdown, and choose between FWER and FDR. https://openeconometrics.com/methods/poisson-negative-binomial-ppml https://openeconometrics.com/methods/poisson-negative-binomial-ppml Mon, 19 Oct 2026 00:00:00 +0000 Limited and duration outcomes Fit Poisson and negative binomial count models and PPML gravity regressions with fixed effects in Python, and map them to poisson, nbreg and ppmlhdfe. https://openeconometrics.com/methods/quantile-regression https://openeconometrics.com/methods/quantile-regression Mon, 19 Oct 2026 00:00:00 +0000 Linear models and IV Estimate median and other conditional quantiles in Python with qreg, bsqreg and sqreg, and test whether a slope differs across the distribution. https://openeconometrics.com/methods/mixed-effects-models https://openeconometrics.com/methods/mixed-effects-models Mon, 19 Oct 2026 00:00:00 +0000 Panel data Fit linear mixed-effects (multilevel) models in Python by ML or REML, with random intercepts and slopes, nested levels, the ICC and BLUP predictions. https://openeconometrics.com/methods/regression-discontinuity https://openeconometrics.com/methods/regression-discontinuity Mon, 19 Oct 2026 00:00:00 +0000 Causal inference Estimate sharp and fuzzy regression discontinuity designs in Python with local polynomials, optimal bandwidths and robust bias-corrected intervals. https://openeconometrics.com/methods/seemingly-unrelated-regression https://openeconometrics.com/methods/seemingly-unrelated-regression Mon, 19 Oct 2026 00:00:00 +0000 Linear models and IV Estimate seemingly unrelated regressions and simultaneous-equation systems by three-stage least squares in Python, with cross-equation tests and constraints. https://openeconometrics.com/methods/tobit-truncated-regression https://openeconometrics.com/methods/tobit-truncated-regression Mon, 19 Oct 2026 00:00:00 +0000 Limited and duration outcomes Estimate Tobit models for censored outcomes and truncated regressions in Python, compute their marginal effects, and map them to Stata's tobit and truncreg. https://openeconometrics.com/methods/unit-root-tests https://openeconometrics.com/methods/unit-root-tests Mon, 19 Oct 2026 00:00:00 +0000 Time series Run augmented Dickey–Fuller, Phillips–Perron, DF-GLS, KPSS and Zivot–Andrews tests in Python and read their statistics, critical values and p-values. https://openeconometrics.com/methods/cointegration-ardl https://openeconometrics.com/methods/cointegration-ardl Mon, 19 Oct 2026 00:00:00 +0000 Time series Test for cointegration in Python with Engle–Granger and Johansen tests, and fit ARDL models with the bounds test and error-correction form. https://openeconometrics.com/methods/heckman-selection https://openeconometrics.com/methods/heckman-selection Mon, 19 Oct 2026 00:00:00 +0000 Limited and duration outcomes Correct for sample selection bias in Python with the Heckman model, by maximum likelihood or Heckman's two-step estimator, and test whether rho is zero. https://openeconometrics.com/methods/treatment-effects https://openeconometrics.com/methods/treatment-effects Mon, 19 Oct 2026 00:00:00 +0000 Causal inference Estimate ATE and ATET in Python by regression adjustment, inverse probability weighting, doubly robust AIPW or matching, and map each to Stata's teffects. https://openeconometrics.com/methods/survival-analysis https://openeconometrics.com/methods/survival-analysis Mon, 19 Oct 2026 00:00:00 +0000 Limited and duration outcomes Fit Cox proportional hazards and Weibull or other parametric survival models in Python, test proportional hazards, and map them to Stata's stcox and streg. https://openeconometrics.com/methods/synthetic-control https://openeconometrics.com/methods/synthetic-control Mon, 19 Oct 2026 00:00:00 +0000 Causal inference Estimate a synthetic control for one treated unit in Python, read its donor weights and placebo p-value, and compare it with synthetic DiD.