A simple cointegrating rank test without vector autoregression

A Simple Cointegrating Rank Test without Vector Autoregression

This paper proposes a fully nonparametric test for cointegrating rank which does not require estimation of a vector autoregressive model. The test exploits the fact that the degeneracy in the moment matrix of the variables with mixed integration order corresponds to the notion of cointegration. With an appropriate standardization, the test statistics are shown to have a nuisance parameter free ...

Semiparametric cointegrating rank selection

Some convenient limit properties of usual information criteria are given for cointegrating rank selection. Allowing for a non-parametric short memory component and using a reduced rank regression with only a single lag, standard information criteria are shown to be weakly consistent in the choice of cointegrating rank provided the penalty coefficient Cn → ∞ and Cn/n → 0 as n → ∞. The limit dist...

A simple additivity test for conditionally heteroscedastic nonlinear autoregression

In this article we propose a test for additivity of a nonlinear conditionally heteroscedastic autoregressive model. A test is based on the unequal variance unbalanced design ANOVA scheme. Asymptotic distribution of the test statistic is derived and the test performance in finite samples is studied using simulation. To the best of our knowledge, this is the first additivity test for a conditiona...

Lecture 6 : Vector Autoregression ∗

Cointegrating MiDaS Regressions and a MiDaS Test

This paper introduces cointegrating mixed data sampling (CoMiDaS) regressions, generalizing nonlinear MiDaS regressions in the extant literature. Under a linear mixed-frequency data-generating process, MiDaS regressions provide a parsimoniously parameterized nonlinear alternative when the linear forecasting model is over-parameterized and may be infeasible. In spite of potential correlation of ...