type="main" xml:id="obes12048-abs-0001" <title type="main">Abstract</title> <p>Cross-section regressions often examine many candidate regressors. We use multiple testing procedures (MTPs) controlling the false discovery rate (FDR) — the expected ratio of false to all rejections — so as not to erroneously select variables...</p>

<Para ID="Par1">This paper argues that typical applications of panel unit root tests should take possible nonstationarity in the volatility process of the innovations of the panel time series into account. Nonstationary volatility arises, for instance, when there are structural breaks in the innovation...</para>

This article proposes a new panel unit root test based on Simes’ (1986) classical intersection test. The test is robust to general patterns of cross-sectional dependence and yet is straightforward to implement, only requiring <italic>p</italic>-values of time series unit root tests of the series in the panel,...

The Cauchy estimator of an autoregressive root uses the sign of the first lag as instrumental variable. The resulting IV <italic>t</italic>-type statistic follows a standard normal limiting distribution under a unit root case even under unconditional heteroscedasticity, if the series to be tested has no...

We derive the null distribution of the nonlinear unit root test proposed in Kapetanios et al. [Kapetanios, G., Shin, Y., Snell, A., 2003. Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics 112, 359–379] when nonzero means or both means and deterministic trends...

We propose an IV panel unit root test robust to nonstationary error volatility. Its finite-sample performance is convincing even for many units and strong cross-correlation. An application to GDP prices illustrates the inferential impact of nonstationary volatility.

The paper examines the behavior of a generalized version of the nonlinear IV unit root test proposed by Chang (<CitationRef CitationID="CR6">2002</CitationRef>) when the series’ errors exhibit nonstationary volatility. The leading case of such nonstationary volatility concerns structural breaks in the error variance. We show that the...</citationref>

type="main" xml:id="jtsa12071-abs-0001"The distributions of cointegration tests are affected when the innovation variance varies over time. In panels, one must also pay attention to dependence among units. To obtain a panel cointegration test robust to both heteroskedasticity and dependence, we...