The Rosetta glossary¶

Part of The tsecon Guide to Time Series Econometrics. One table, four dialects. For each core time-series concept it gives the tsecon function and the closest call in statsmodels, R, and Stata — so you can read a method named in any of them and find its home here.

This is a lookup table, not a tutorial. If you know a method by its name in one package, scan the row and read across. The tsecon column lists only functions that ship today; where a concept is on the library's roadmap it is marked (roadmap). A dash (—) in another package's column means that package has no first-class equivalent (some are available only through user-written add-ons, noted where it matters). For the full narrative translations, see the statsmodels, R, and Stata guides.

Every tsecon name below is a real function. The canonical idiom — arrays in, a dict out — looks like this:

import numpy as np, tsecon
rng = np.random.default_rng(20)
y = np.cumsum(rng.standard_normal(300))               # a random walk
rep = tsecon.check_stationarity(y)
print(rep["quadrant"], "->", rep["recommendation"])   # UnitRoot -> Difference

Concept → package call¶

Concept	tsecon	statsmodels	R	Stata
Autocorrelation function	`acf`	`acf`	`acf` / `forecast::Acf`	`ac`, `corrgram`
Partial autocorrelation	`pacf`	`pacf`	`pacf`	`pac`
White-noise / portmanteau test	`ljung_box`	`acorr_ljungbox`	`Box.test`	`wntestq`
Normality test	`jarque_bera`	`jarque_bera`	`tseries::jarque.bera.test`	`sktest` (approx.)
ARCH / conditional-heteroskedasticity test	`arch_lm`	`het_arch`	`FinTS::ArchTest`	`estat archlm`
Unit-root test (ADF)	`adf`	`adfuller`	`urca::ur.df`, `tseries::adf.test`	`dfuller`
Stationarity test (KPSS)	`kpss`	`kpss`	`urca::ur.kpss`	`kpss`
Confirmatory stationarity workflow	`check_stationarity`	—	—	—
One-call diagnostic battery + model routing	`check_series`	—	—	—
Phillips-Perron test	(roadmap)	`PhillipsPerron`	`urca::ur.pp`	`pperron`
Heteroskedasticity test (White / Breusch-Pagan)	`heteroskedasticity_test`	`het_white`, `het_breuschpagan`	`lmtest::bptest`	`estat hettest`, `estat imtest, white`
Ramsey RESET functional-form test	`reset_test`	`linear_reset`	`lmtest::resettest`	`estat ovtest`
Chow known-break test	`chow_test`	—	`strucchange::sctest(type="Chow")`	`estat sbknown`
Unknown single-break sup-F (Quandt-Andrews)	`sup_f_test`	`breaks_cusumolsresid` (approx.)	`strucchange::Fstats`, `sctest(type="supF")`	`estat sbsingle`
Multiple structural breaks (Bai-Perron)	`bai_perron`	—	`strucchange::breakpoints`	—
CUSUM parameter-stability test	`cusum_test`	`breaks_cusumolsresid`	`strucchange::efp` (OLS-CUSUM)	`cusum`
HAC / Newey-West standard errors	`ols(se_type="hac")`	`cov_type="HAC"`	`sandwich::NeweyWest`	`newey`
ARIMA	`arima_fit`	`ARIMA`	`forecast::Arima`	`arima`
Seasonal ARIMA (SARIMA)	(roadmap)	`SARIMAX`	`forecast::auto.arima`	`arima ...(P,D,Q)`
Exponential smoothing / Theta	`theta_forecast`	`ETSModel`	`forecast::ets`, `thetaf`	`tssmooth`
GARCH family	`garch_fit`	`arch.arch_model`	`rugarch::ugarchfit`	`arch`
Multivariate GARCH (CCC / DCC)	`ccc_garch`, `dcc_garch`	—	`rmgarch::dccfit`	`mgarch ccc/dcc`
Score-driven volatility (GAS/DCS)	`gas_volatility`	—	`GAS::UniGASFit`	—
VAR	`var_fit`	`VAR`	`vars::VAR`	`var`
Impulse response (IRF)	`var_irf`	`.irf()`	`vars::irf`	`irf create`, `irf graph`
Forecast-error variance decomposition	`var_fevd`	`.fevd()`	`vars::fevd`	`irf table fevd`
Granger causality	`var_granger`	`test_causality`	`vars::causality`	`vargranger`
Cointegration rank (Johansen)	`johansen`	`coint_johansen`	`urca::ca.jo`	`vecrank`
Vector error-correction model	`vecm`	`VECM`	`urca::cajorls`, `vars::vec2var`	`vec`
Bayesian VAR (Minnesota)	`bvar_fit`, `bvar_irf_draws`	—	`BVAR::bvar`	`bayes: var`
Sign-restricted SVAR	`sign_restricted_svar`	—	`VARsignR`, `svars`	—
SVAR short-/long-run restrictions	(roadmap)	`SVAR`	`svars::id.*`	`svar`
FAVAR	`favar`	—	—	—
Connectedness (Diebold-Yilmaz)	`connectedness`	—	`frequencyConnectedness`	—
Local projection	`lp`	—	`lpirfs::lp_lin`	— (user `lp`)
Local projection with external IV (LP-IV)	`lp_iv`	—	`lpirfs::lp_lin_iv`	—
Integral multiplier (Ramey-Zubairy)	`lp_multiplier`	—	`lpirfs::lp_lin_iv` (approx.)	—
State-dependent local projection	`lp_state`	—	`lpirfs::lp_nl`	—
Smooth local projection (penalized B-spline)	`smooth_lp`	—	—	—
Quantile regression	`quantile_regression`	`QuantReg`	`quantreg::rq`	`qreg`, `sqreg`, `bsqreg`
Quantile local projection	`quantile_lp`	—	—	—
Growth-at-Risk (conditional quantiles)	`growth_at_risk`	—	—	—
Functional PCA of a curve panel	`functional_pca`	—	—	—
Functional shocks (FLP / FVAR)	`flp`, `flp_scenario`, `fvar_scenario`	—	—	—
Markov-switching model	`markov_switching_ar`	`MarkovAutoregression`	`MSwM::msmFit`	`mswitch`
HP filter	`hp_filter`	`hpfilter`	`mFilter::hpfilter`	`tsfilter hp`
Baxter-King / Christiano-Fitzgerald filter	`bk_filter`, `cf_filter`	`bkfilter`, `cffilter`	`mFilter::bkfilter`/`cffilter`	`tsfilter bk`/`cf`
Hamilton regression filter	`hamilton_filter`	—	`neverhpfilter::yth_filter`	—
Spectral density	`periodogram`, `welch`, `coherence`	`scipy.signal.*`	`spectrum`, `spec.pgram`	`psdensity`, `pergram`
Diebold-Mariano test	`dm_test`	—	`forecast::dm.test`	`dmariano` (user)
Clark-West / Giacomini-White test	`cw_test`, `gw_test`	—	`sandwich`+custom	—
Forecast accuracy measures	`accuracy`	—	`forecast::accuracy`	—
Rolling/expanding backtest	`backtest`	—	`forecast::tsCV`	`rolling:`
Realized variance / bipower	`realized_measures`	—	`highfrequency::rCov`, `rBPCov`	—
HAR-RV	`har_rv`	—	`HARModel::HARestimate`	—
Panel fixed effects	`panel_fe`	—	`plm(model="within")`	`xtreg, fe`
Driscoll-Kraay standard errors	`panel_fe(se_type="driscoll_kraay")`	—	`plm` + `vcovSCC`	`xtscc`
Mean-group / CCE-MG estimator	`panel_mean_group`	—	`plm::pmg(model="mg")`, `xtmg`	`xtpmg mg`, `xtmg cce`
Pooled mean group (PMG)	`panel_pmg`	—	`plm::pmg(model="pmg")`	`xtpmg pmg`
Mean-group panel VAR	`mean_group_var`	—	`panelvar` (approx.)	`pvar` (user)
Panel local projection	`panel_lp`	—	`lpirfs::lp_lin_panel`	—
Nowcast (dynamic factor model)	`dfm_nowcast`	`DynamicFactorMQ`	`nowcasting::nowcast`	`dfactor` (approx.)
News / update decomposition	`dfm_news`	—	`nowcasting`	—
Static factor model (PCA + Bai-Ng)	`factor_model`	—	—	—
MIDAS mixed-frequency regression	`weighted_midas`, `umidas`	—	`midasr::midas_r`	`midasreg` (user)
Linear IV-GMM (+ Hansen J)	`iv_gmm`	— (`linearmodels`)	`gmm::gmm`, `AER::ivreg`	`ivregress gmm`
Nonlinear GMM (custom moments)	`gmm_nonlinear`	—	`gmm::gmm`	`gmm`
Ridge / lasso / elastic net	`ridge`, `lasso`, `elastic_net`	—	`glmnet`	`lasso`, `elasticnet`
Adaptive lasso / penalized path	`adaptive_lasso`, `lasso_path`	—	`glmnet` (+ weights)	`lasso ..., selection()`
Leakage-safe time-series CV	`cv_splits`	—	`rsample::rolling_origin`	—
Yield curve (Nelson-Siegel / Svensson)	`nelson_siegel`, `svensson`	—	`YieldCurve::Nelson.Siegel`	—
Dynamic Nelson-Siegel (Diebold-Li)	`dynamic_ns`	—	`YieldCurve`	—
Arbitrage-free Nelson-Siegel (AFNS)	`afns_adjustment`	—	—	—
Kalman filter / smoother (local level)	`local_level_smooth`	`UnobservedComponents`	`dlm`, `KFAS`	`sspace`
Linear RE / DSGE solution (Blanchard-Kahn)	`dsge_solve`	—	`gEcon` (approx.)	`dsge`
Bootstrap resampling (block/stationary)	`bootstrap_indices`, `optimal_block_length`	—	`boot`, `np::b.star`	`bootstrap:`

How to read the roadmap gaps¶

A handful of concepts above are marked (roadmap) in the tsecon column — Phillips-Perron, seasonal ARIMA, and explicit short-/long-run SVAR restrictions are the notable ones. tsecon covers the identification frontier that the other packages mostly lack (sign restrictions, BVARs, local projections, FAVAR, nowcasting, quantile/growth-at-risk, Bai-Perron breaks, smooth LP, and functional shocks), and is still filling in some classical corners. The per-package guides spell out each gap and the nearest shipped substitute; nothing in the tsecon column is aspirational unless it carries the (roadmap) tag.