Showing results 1 to 3 of approximately 3.(refine search)
Identification Through Sparsity in Factor Models
Factor models are generally subject to a rotational indeterminacy, meaning that individualfactors are only identified up to a rotation. In the presence of local factors, which only affecta subset of the outcomes, we show that the implied sparsity of the loading matrix can be usedto solve this rotational indeterminacy. We further prove that a rotation criterion based on the`1-norm of the loading matrix can be used to achieve identification even under approximatesparsity in the loading matrix. This enables us to consistently estimate individual factors, andto interpret them as structural ...
Pre-event Trends in the Panel Event-study Design
We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions to identify the causal effect of the policy by exploiting covariates related to the policy only through the confounds. Our model implies a set of moment equations that are linear in parameters. The effect of the policy can be estimated by 2SLS, and causal inference is valid even when endogeneity leads to pre-event trends (?pre-trends?) in the outcome. Alternative approaches perform poorly in our simulations
A Generalized Factor Model with Local Factors
I extend the theory on factor models by incorporating local factors into the model. Local factors only affect an unknown subset of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. I derive which factors are pervasive enough to be economically important and which factors are pervasive enough to be estimable using the common principal component estimator. I then introduce a new class of estimators to determine the number of those relevant factors. Unlike existing estimators, my estimators use not only the ...