# arch.unitroot.cointegration.CanonicalCointegratingReg¶

class arch.unitroot.cointegration.CanonicalCointegratingReg(y, x, trend='c', x_trend=None)[source]

Canonical Cointegrating Regression cointegrating vector estimation.

Parameters
ynumpy:array_like

The left-hand-side variable in the cointegrating regression.

xnumpy:array_like

The right-hand-side variables in the cointegrating regression.

trend{{“n”,”c”,”ct”,”ctt”}}, default “c”

Trend to include in the cointegrating regression. Trends are:

• “n”: No deterministic terms

• “c”: Constant

• “ct”: Constant and linear trend

• “ctt”: Constant, linear and quadratic trends

x_trend{None,”c”,”ct”,”ctt”}, default None

Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is “ct”, x_trend must be either “ct” or “ctt”.

Notes

The cointegrating vector is estimated from the regressions

$\begin{split}Y_t & = D_{1t} \delta + X_t \beta + \eta_{1t} \\ X_t & = D_{1t} \Gamma_1 + D_{2t}\Gamma_2 + \epsilon_{2t} \\ \eta_{2t} & = \Delta \epsilon_{2t}\end{split}$

or if estimated in differences, the last two lines are

$\Delta X_t = \Delta D_{1t} \Gamma_1 + \Delta D_{2t} \Gamma_2 + \eta_{2t}$

Define the vector of residuals as $$\eta = (\eta_{1t},\eta'_{2t})'$$, and the long-run covariance

$\Omega = \sum_{h=-\infty}^{\infty} E[\eta_t\eta_{t-h}']$

and the one-sided long-run covariance matrix

$\Lambda_0 = \sum_{h=0}^\infty E[\eta_t\eta_{t-h}']$

The covariance matrices are partitioned into a block form

$\begin{split}\Omega = \left[\begin{array}{cc} \omega_{11} & \omega_{12} \\ \omega'_{12} & \Omega_{22} \end{array} \right]\end{split}$

The cointegrating vector is then estimated using modified data

$\begin{split}X^\star_t & = X_t - \hat{\Lambda}_2'\hat{\Sigma}^{-1}\hat{\eta}_t \\ Y^\star_t & = Y_t - (\hat{\Sigma}^{-1} \hat{\Lambda}_2 \hat{\beta} + \hat{\kappa})' \hat{\eta}_t\end{split}$

where $$\hat{\kappa} = (0,\hat{\Omega}_{22}^{-1}\hat{\Omega}'_{12})$$ and the regression

$Y^\star_t = D_{1t} \delta + X^\star_t \beta + \eta^\star_{1t}$

See [1] for further details.

References

1

Park, J. Y. (1992). Canonical cointegrating regressions. Econometrica: Journal of the Econometric Society, 119-143.

Methods

 fit([kernel, bandwidth, force_int, diff, …]) Estimate the cointegrating vector.

Methods

 fit([kernel, bandwidth, force_int, diff, …]) Estimate the cointegrating vector.