This paper discusses two longstanding questions in growth econometrics which involve multiple hypothesis testing. In cross sectional GDP growth regressions many variables are simultaneously tested for significance. Similarly, when investigating pairwise convergence of output for $n$ countries,...

This paper examines whether, in addition to standard unit root and cointegration tests,panel approaches also produce test statistics behaving erratically when applied to PPP. We show that if appropriate tests (which are robust to cross-sectional dependence and more powerful) are used, any...

This paper suggests a combination procedure to exploit the imperfect correlation of cointegration tests to develop a more powerful meta test.To exemplify, we combine Engle and Granger (1987) and Johansen (1988) tests. Either of these underlying tests can be more powerful than the other one...

Using PIRLS (Progress in International Reading Literacy Study) data, we investigate which countries' schools can be be classified as significantly better or weaker than Germany's as regards the reading literacy of primary school children. The `standard' approach is to conduct separate tests for...

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. Nonstationarity volatility arises for instance when there are structural breaks in the innovation...

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.

Using PIRLS (Progress in International Reading Literacy Study) data, we investigate which countries' schools can be classified as significantly better or weaker than Germany's as regards the reading literacy of primary school children. The 'standard' approach is to conduct separate tests for...

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...