MIDAS

Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series

I propose two variable addition test statistics aimed at the specification of a high-frequency predictor of a series observed at a lower frequency. Under the null, the high-frequency predictor is aggregated to the low frequency versus mixed-frequency alternatives. The first test statistic is similar to those in the extant literature, but I show its robustness to conditionally biased forecast error and cointegrated and deterministically trending covariates. It is feasible and consistent even if estimation is not feasible under the alternative.

Implementing Residual-Based KPSS Tests for Cointegration with Data Subject to Temporal Aggregation and Mixed Sampling Frequencies

We show how temporal aggregation affects the size and power of the DOLS residual-based KPSS test of the null of cointegration. Size is effectively controlled by setting the minimum number of leads equal to one -- as opposed to zero -- when selecting the lag/lead order of the DOLS regression, but at a cost to power in finite samples. If high-frequency data for one or more series are available, we show how to effectively utilize the high-frequency data to increase power while controlling size.

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