Nonlinear Error Correction Models with An Application To Commodity Prices

Existing tests for nonlinearity in vector error correction models are highly intensive computationally and have nuisance parameters in the asymptotic distribution, what calls for cumbersome bootstrap calculations in order to assess the distribution. Our work proposes a consistent test which is implementable in any statistical package and has Chi-Squared asymptotics. Moreover, Monte Carlo experiments show that in small samples our test has nice size and power properties, often better than the preexisting tests. We also provide a condition under which a two step estimator for the model parameters is consistent and asymptotically normal. Application to international agricultural commodities prices show evidence of nonlinear adjustment to the long run equilibrium on the wheat prices.