We propose a parsimonious stochasti

We propose a parsimonious stochastic model of reported earnings that links misreporting to performance shocks. Our main analytical prediction is that misreporting leads to a negative second-order autocorrelation in the residuals from a regression of current earnings on lagged earnings. We also propose a stylized dynamic model of earnings manipulation and demonstrate that both earnings smoothing and target-beating considerations result in the same predictions of negative second-order autocorrelations. Empirically, we find that the distribution of this measure is asymmetric around zero with 27 percent of the firms having significantly negative estimates. Using this measure, we specify a methodology to estimate the intensity of misreporting and to create estimates of unmanipulated earnings. Our estimates of unmanipulated earnings are more correlated with contemporaneous returns and have higher volatility than reported earnings. With respect to economic magnitude, we find that, in absolute terms, median misreporting is 0.7 percent of total assets. Moreover, firms in our sample subject to SEC AAERs have significantly higher estimates of manipulation intensity.