In a quarterly time series, the PAR(1) model, \(y_t = \alpha_{s,1} y_{t-1} + \epsilon_t\) with
\(\epsilon_t ID(0,1)\), contains a unit root if \(g(\alpha) = \Pi_{s=1}^4 \alpha_{s,1} = 1\). To test
this hypothesis, a likelihood ratio test, LR
, is built as the logarithm of the ratio between the
residual sum of squares in the unrestricted and the restricted model, weighted by the number of
observations.
The unrestricted PAR model is estimated by OLS, whereas the model in which the null hypothesis is
imposed, i.e. \(\Pi_{s=1}^4 \alpha_{s,1} = 1\), is estimated by nonlinear least squares.
The critical values are reported in Osterwald-Lenum (1992), table 1.1 (for the case where \(p-r=1\)).
In this version, PAR models up to order 2 with seasonal intercepts are considered, since the function
fit.piar
does not allow for higher orders.