API Reference

This page lists contains a list of the essential end-user API functions and classes.

Volatility Modeling

High-level

arch_model(y[, x, mean, lags, vol, p, o, q, ...])

Initialization of common ARCH model specifications

Mean Specification

ConstantMean([y, hold_back, volatility, ...])	Constant mean model estimation and simulation.
ZeroMean([y, hold_back, volatility, ...])	Model with zero conditional mean estimation and simulation
HARX([y, x, lags, constant, use_rotated, ...])	Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation
ARX([y, x, lags, constant, hold_back, ...])	Autoregressive model with optional exogenous regressors estimation and simulation
LS([y, x, constant, hold_back, volatility, ...])	Least squares model estimation and simulation

Volatility Process Specification

GARCH([p, o, q, power])	GARCH and related model estimation
EGARCH([p, o, q])	EGARCH model estimation
HARCH([lags])	Heterogeneous ARCH process
FIGARCH([p, q, power, truncation])	FIGARCH model
MIDASHyperbolic([m, asym])	MIDAS Hyperbolic ARCH process
EWMAVariance([lam])	Exponentially Weighted Moving-Average (RiskMetrics) Variance process
RiskMetrics2006([tau0, tau1, kmax, rho])	RiskMetrics 2006 Variance process
ConstantVariance()	Constant volatility process
FixedVariance(variance[, unit_scale])	Fixed volatility process

Shock Distributions

Normal([random_state])	Standard normal distribution for use with ARCH models
StudentsT([random_state])	Standardized Student's distribution for use with ARCH models
SkewStudent([random_state])	Standardized Skewed Student's distribution for use with ARCH models
GeneralizedError([random_state])	Generalized Error distribution for use with ARCH models

Unit Root Testing

ADF(y[, lags, trend, max_lags, method, ...])	Augmented Dickey-Fuller unit root test
DFGLS(y[, lags, trend, max_lags, method, ...])	Elliott, Rothenberg and Stock's GLS version of the Dickey-Fuller test
PhillipsPerron(y[, lags, trend, test_type])	Phillips-Perron unit root test
ZivotAndrews(y[, lags, trend, trim, ...])	Zivot-Andrews structural-break unit-root test
VarianceRatio(y[, lags, trend, debiased, ...])	Variance Ratio test of a random walk.
KPSS(y[, lags, trend])	Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test

Cointegration Testing

engle_granger(y, x[, trend, lags, max_lags, ...])	Test for cointegration within a set of time series.
phillips_ouliaris(y, x[, trend, test_type, ...])	Test for cointegration within a set of time series.

Cointegrating Relationship Estimation

CanonicalCointegratingReg(y, x[, trend, x_trend])	Canonical Cointegrating Regression cointegrating vector estimation.
DynamicOLS(y, x[, trend, lags, leads, ...])	Dynamic OLS (DOLS) cointegrating vector estimation
FullyModifiedOLS(y, x[, trend, x_trend])	Fully Modified OLS cointegrating vector estimation.

Bootstraps

IIDBootstrap(*args, **kwargs)	Bootstrap using uniform resampling
IndependentSamplesBootstrap(*args, **kwargs)	Bootstrap where each input is independently resampled
StationaryBootstrap(block_size, *args, **kwargs)	Politis and Romano (1994) bootstrap with expon distributed block sizes
CircularBlockBootstrap(block_size, *args, ...)	Bootstrap using blocks of the same length with end-to-start wrap around
MovingBlockBootstrap(block_size, *args, **kwargs)	Bootstrap using blocks of the same length without wrap around

Block-length Selection

optimal_block_length(x)

Estimate optimal window length for time-series bootstraps

Testing with Multiple-Comparison

SPA(benchmark, models[, block_size, reps, ...])	Test of Superior Predictive Ability (SPA) of White and Hansen.
MCS(losses, size[, reps, block_size, ...])	Model Confidence Set (MCS) of Hansen, Lunde and Nason.
StepM(benchmark, models[, size, block_size, ...])	StepM multiple comparison procedure of Romano and Wolf.

Long-run Covariance (HAC) Estimation

Bartlett(x[, bandwidth, df_adjust, center, ...])	Bartlett's (Newey-West) kernel covariance estimation.
Parzen(x[, bandwidth, df_adjust, center, ...])	Parzen's kernel covariance estimation.
ParzenCauchy(x[, bandwidth, df_adjust, ...])	Parzen's Cauchy kernel covariance estimation.
ParzenGeometric(x[, bandwidth, df_adjust, ...])	Parzen's Geometric kernel covariance estimation.
ParzenRiesz(x[, bandwidth, df_adjust, ...])	Parzen-Reisz kernel covariance estimation.
QuadraticSpectral(x[, bandwidth, df_adjust, ...])	Quadratic-Spectral (Andrews') kernel covariance estimation.
TukeyHamming(x[, bandwidth, df_adjust, ...])	Tukey-Hamming kernel covariance estimation.
TukeyHanning(x[, bandwidth, df_adjust, ...])	Tukey-Hanning kernel covariance estimation.
TukeyParzen(x[, bandwidth, df_adjust, ...])	Tukey-Parzen kernel covariance estimation.