In this paper we study the selection of the number of primitive shocks in exact and approximate factor models in the presence of structural instability. The empirical analysis shows that the estimated number of factors varies substantially across several selection methods and over the last 30 years in standard large macroeconomic and financial panels. Using Monte Carlo simulations, we suggest that the structural instability, in terms of both timevarying factor loadings and nonlinear factor representations, can alter the estimation of the number of factors and therefore provides an explanation for the empirical findings.

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