econometrics module
- btQuant.econometrics.adfTest(series, lags=None, regression='c', autolag='AIC')[source]
Augmented Dickey-Fuller test for unit root.
- Parameters:
series – time series
lags – number of lags (auto if None)
regression – ‘nc’ (no constant), ‘c’ (constant), ‘ct’ (constant+trend)
autolag – ‘AIC’ or ‘BIC’
- Returns:
dict with testStatistic, pValue, criticalValues, conclusion, optimalLag
- btQuant.econometrics.breuschPaganTest(y, X, addConst=True)[source]
Breusch-Pagan test for heteroskedasticity.
- Parameters:
y – dependent variable
X – independent variables
addConst – add constant term
- Returns:
dict with testStatistic, pValue, df, conclusion
- btQuant.econometrics.durbinWatson(residuals)[source]
Durbin-Watson statistic for autocorrelation. DW ~ 2: no autocorrelation 0 < DW < 2: positive autocorrelation 2 < DW < 4: negative autocorrelation
- Parameters:
residuals – residuals from regression
- Returns:
Durbin-Watson statistic
- Return type:
float
- btQuant.econometrics.grangerCausality(y, x, maxLag=4)[source]
Granger causality test: does x Granger-cause y?
- Parameters:
y – dependent variable (time series)
x – independent variable (time series)
maxLag – maximum lag to test
- Returns:
dict with lags, fStats, pValues, conclusion
- btQuant.econometrics.kpssTest(series, lags=None, regression='c')[source]
KPSS test for stationarity.
- Parameters:
series – time series
lags – number of lags (auto if None)
regression – ‘c’ (constant) or ‘ct’ (constant+trend)
- Returns:
dict with testStatistic, pValue, criticalValues, conclusion, lags
- btQuant.econometrics.ljungBox(residuals, lags=None)[source]
Ljung-Box test for autocorrelation in residuals.
- Parameters:
residuals – residuals from regression
lags – number of lags (default min(10, n//5))
- Returns:
dict with lags, autocorrelations, qStats, pValues, criticalValues
- btQuant.econometrics.ols(y, X, addConst=True, robust=False, covType='HC3')[source]
Ordinary least squares regression with optional robust standard errors.
- Parameters:
y – dependent variable (1D array)
X – independent variables (2D array or 1D for single predictor)
addConst – add constant term
robust – use robust standard errors
covType – ‘HC0’, ‘HC1’, ‘HC2’, ‘HC3’, ‘HC4’
- Returns:
dict with coefficients, std_errors, t_stats, p_values, r_squared, etc.