This paper presents specification tests that are applicable after estimating a dynamic model from panel data by the generalized method of moments (GMM), and studies the practical performance of these procedures using both generated and real data. Our GMM estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables. We propose a test of serial correlation based on the GMM residuals and compare this with Sargan tests of over-identifying restrictions and Hausman specification tests.

This paper shows that, with (partial) irreversibility, higher uncertainty reduces the impact effect of demand shocks on investment.Uncertainty increases real option values making firms more cautious when investing or disinvesting.This is confirmed both numerically for a model with a rich mix of adjustment costs, time-varying uncertainty, and aggregation over investment decisions and time, and also empirically for a panel of manufacturing firms.These cautionary effects of uncertainty are large -going from the lower quartile to the upper quartile of the uncertainty distribution typically halves the first year investment response to demand shocks.This implies the responsiveness of firms to any given policy stimulus may be much lower in periods of high uncertainty, such as after major shocks like OPEC I and 9/11.