This paper proposes simple Hausman-type tests to check for bias in the log-periodogram regression of a time series believed to be long memory. The statistics are asymptotically standard normal on the null hypothesis that no bias is present, and the tests are consistent.

We study the empirical behaviour of semi-parametric estimation for long-memory models when the true data generating process exhibits a change in persistence. Evidence for long memory is likely to be found. Procedures for discrimination between different models are proposed.

Linearity testing against smooth transition autoregressive (STAR) models when deterministic trends are potentially present in the data is considered in this work. Our findings show, in contrast to results recently reported in Zhang (2012), that linearity tests against STAR models lead to useful...

We investigate the OLS-based estimator s2 of the disturbance variance in an error component linear panel regression model when the disturbances are homoskedastic, but spatially correlated. Although consistent (Song and Lee, Econ. Lett. 2008), s2 can be arbitrarily biased towards zero in finite...

We propose a new test against a change in the probability of multivariate tail events. The test is based on partial sums of a suitably defined indicator function and detects multiple changes in joint tail probabilities better than a previously suggested competitor.

We show that some care is needed when inferring true unconditional correlations from observed conditional correlations, which is a frequent problem in empirical finance and elsewhere. We give a general formula for the relationship between the two and demonstrate its importance in the context of...

We show that structural changes in stochastic volatility models induce spurious persistence. Other than in GARCH-type models, implied persistence does not tend to unity with given size of the structural change and increasing sample size.

We show that the (Baillie and Chung, 2001) minimum distance estimates of the GARCH (1,1) model induce spurious persistence in the volatility when there are structural changes in the mean of the process.

Using an empirical likelihood approach, we show that generalized linear models can still be consistently estimated even if dependent variables are not missing at random, and derive a Hausman test by comparing this estimator to the standard one.