DYNAMIC SPECIFICATION TESTS FOR DYNAMIC FACTOR MODELS

We derive computationally simple and intuitive expressions for score tests of neglected serial correlation in common and idiosyncratic factors in dynamic factor models using frequency domain techniques. The implied time domain orthogonality conditions are analogous to the conditions obtained by treating the smoothed estimators of the innovations in the latent factors as if they were observed, but they account for their final estimation errors. Monte Carlo exercises confirm the finite sample reliability and power of our proposed tests. Finally, we illustrate their empirical usefulness in an application that constructs a monthly coincident indicator for the US from four macro series.