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Existing econometric approaches for studying price discovery presume that the number of markets are small, and their properties become suspect when this restriction is not met. They also require making identifying restrictions and are in many cases not suitable for statistical inference. The current paper takes these shortcomings as a starting point to develop a factor analytical approach that makes use of the cross-sectional variation of the data, yet is very user-friendly in that it does not involve any identifying restrictions or obstacles to inference.

The cross-section average (CA) augmentation approach of Pesaran (A simple panel unit root test in presence of cross-section dependence. Journal of Applied Econometrics 2007; 22: 265–312) and Pesaran et al. (Panel unit root test in the presence of a multifactor error structure. Journal of Econometrics 2013; 175: 94–115), and the principal components-based panel analysis of non-stationarity in idiosyncratic and common components (PANIC) of Bai and Ng (A PANIC attack on unit roots and cointegration. Econometrica 2004; 72: 1127–1177; Panel unit root tests with cross-section dependence: a further investigation. Econometric Theory 2010; 26: 1088–1114) are among the most popular ‘second-generation’ approaches for cross-section correlated panels. One feature of these approaches is that they have different strengths and weaknesses. The purpose of the current paper is to develop PANICCA, a combined approach that exploits the strengths of both CA and PANIC. (Less)