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Statistical Functions#

Heteroscedascity#

tsa.het_arch(input_relation, eps, ts[, by, p])

Engle’s Test for Autoregressive Conditional Heteroscedasticity (ARCH).

ols.het_breuschpagan(input_relation, eps, X)

Uses the Breusch-Pagan to test a model for Heteroscedasticity.

ols.het_goldfeldquandt(input_relation, y, X)

Goldfeld-Quandt Homoscedasticity test.

ols.het_white(input_relation, eps, X)

White’s Lagrange Multiplier Test for Heteroscedasticity.

Multi-collinearity#

ols.variance_inflation_factor(input_relation, X)

Computes the variance inflation factor (VIF).

Normal Distribution#

norm.jarque_bera(input_relation, column)

Jarque-Bera test (Distribution Normality).

norm.kurtosistest(input_relation, column)

Test whether the kurtosis is different from the Normal distribution.

norm.skewtest(input_relation, column)

Test whether the skewness is different from the normal distribution.

norm.normaltest(input_relation, column)

This function tests the null hypothesis that a sample comes from a normal distribution.

Time Series - Stationarity/Trend#

tsa.mkt(input_relation, column, ts[, alpha])

Mann Kendall test (Time Series trend).

tsa.adfuller(input_relation, column, ts[, ...])

Augmented Dickey Fuller test (Time Series stationarity).

Time Series - Correlations#

tsa.cochrane_orcutt(model, input_relation, ts)

Performs a Cochrane-Orcutt estimation.

tsa.ljungbox(input_relation, column, ts[, ...])

Ljung–Box test (whether any of a group of autocorrelations of a time series are different from zero).

tsa.durbin_watson(input_relation, eps, ts[, by])

Durbin Watson test (residuals autocorrelation).

Time Series - Decomposition#

tsa.seasonal_decompose(input_relation, ...)

Performs a seasonal time series decomposition.