Raffaele D. Saggio
University of British Columbia
6000 Iona Drive
Institutional Affiliation: University of British Columbia
NBER Working Papers and Publications
|January 2020||Do Firm Effects Drift? Evidence from Washington Administrative Data|
with Marta Lachowska, Alexandre Mas, Stephen A. Woodbury: w26653
We study the time-series properties of firm effects in the two-way fixed effects models popularized by Abowd, Kramarz, and Margolis (1999) (AKM) using two approaches. The first—the rolling AKM approach (R-AKM)—estimates AKM models separately for successive two-year intervals. The second—the time-varying AKM approach (TV-AKM)—is an extension of the original AKM model that allows for unrestricted interactions of year and firm indicators. We apply to both approaches the leave-one-out methodology of Kline, Saggio and Sølvsten (2019) to correct for biases in the resulting variance components. Using administrative wage records from Washington State, we find, first, that firm effects for hourly wage rates and earnings are highly persistent. Specifically, the autocorrelation coefficient between fi...
|September 2019||Leave-out Estimation of Variance Components|
with Patrick Kline, Mikkel Sølvsten: w26244
We propose leave-out estimators of quadratic forms designed for the study of linear models with unrestricted heteroscedasticity. Applications include analysis of variance and tests of linear restrictions in models with many regressors. An approximation algorithm is provided that enables accurate computation of the estimator in very large datasets. We study the large sample properties of our estimator allowing the number of regressors to grow in proportion to the number of observations. Consistency is established in a variety of settings where plug-in methods and estimators predicated on homoscedasticity exhibit first-order biases. For quadratic forms of increasing rank, the limiting distribution can be represented by a linear combination of normal and non-central χ2 random varia...