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The difficulty of predicting returns has recently motivated researchers to start looking for tests that are either more powerful or robust to more features of the data. Unfortunately, the way that these tests work typically involves trading robustness for power or vice versa. The current paper takes this as its starting point to develop a new panel-based approach to predictability that is both robust and powerful. Specifically, while the panel route to increased power is not new, the way in which the cross-section variation is exploited also to achieve robustness with respect to the predictor is. The result is two new tests that enable asymptotically standard normal and chi-squared inference across a wide range of empirically relevant scenarios in which the predictor may be stationary, moderately non-stationary, nearly non-stationary, or indeed unit root non-stationary. The type of cross-section dependence that can be permitted in the predictor is also very general, and can be weak or strong, although we do require that the cross-section dependence in the regression errors is of the strong form. What is more, this generality comes at no cost in terms of complicated test construction. The new tests are therefore very user-friendly.

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This paper considers estimation of factor-augmented panel data regression models. One of the most popular approaches towards this end is the common correlated effects (CCE) estimator of Pesaran (Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 2006Econometrica, , 74, 967-1012Econometrica, , 2006. For the pooled version of this estimator to be consistent, either the number of observables must be larger than the number of unobserved common factors, or the factor loadings must be distributed independently of each other. This is a problem in the typical application involving only a small number of regressors and/or correlated loadings. The current paper proposes a simple extension to the CCE procedure by which both requirements can be relaxed. The CCE approach is based on taking the cross-section average of the observables as an estimator of the common factors. The idea put forth in the current paper is to consider not only the average but also other cross-section combinations. Asymptotic properties of the resulting combination-augmented CCE (C 3 E) estimator are provided and tested in small samples using both simulated and real data. which can be estimated consistently using least squares (LS). If, however, x i is correlated with F, then consistency will be Our very good friend and co-author Jean-Pierre Urbain passed away shortly after the submission of the first version of this paper. 268 SUPPORTING INFORMATIONAdditional supporting information may be found online in the Supporting Information section at the end of the article.How to cite this article: Karabiyik H, Urbain J-P, Westerlund J. CCE estimation of factor-augmented regression models with more factors than observables. J Appl Econ. 2019;34:268-284. https://doi.

This paper examines the source of price discovery for Islamic stocks. We pair a large number of Islamic stocks to country-specific index futures and estimate price discovery using a vector error correction model. The results obtained using data for 19 countries suggest that for most countries (63% of the sample) price discovery is dominated by the spot market. We show that for these countries, a mean-variance investor makes annualized average profit of 4.91% compared to an average buy-and-hold profit of 2.97% per annum.