Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/300047 
Year of Publication: 
2024
Series/Report no.: 
CESifo Working Paper No. 11119
Publisher: 
CESifo GmbH, Munich
Abstract: 
The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as "data" in a downstream econometric model. We establish theoretical arguments for why this two-step strategy leads to biased inference in empirically plausible settings. More constructively, we propose a one-step strategy for valid inference that uses the upstream and downstream models jointly. The one-step strategy (i) substantially reduces bias in simulations; (ii) has quantitatively important effects in a leading application using CEO time-use data; and (iii) can be readily adapted by applied researchers.
Subjects: 
unstructured data
information retrieval
topic modeling
Hamiltonian Monte Carlo
measurement error
JEL: 
C11
C51
C55
Document Type: 
Working Paper
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