This paper compares quasi Monte Carlo methods, in particular so-called (t,m,s)-Nets, with classical Monte Carlo approaches for simulating econometric time-series.

A number of variations of seven causality tests of the absence of causal ordering are examined. The various of the tests considered account not only for stationarity, but also for integratedness and/or contegratedness among the variables in the model.

In this paper, the authors provide some asymptotic results for the J-type test on non-nested hypotheses proposed by Davidson and MacKinnon (1981), in the context of I(1) time series.

The predictive accuracy of various econometrics models, including random walks, vector autoregressive and vector error-correction models, are investigated using daily futures prices of 4 commodities (the S&P500 index, treasury bonds, gold and crude oil).