Inference for Regression with Variables Generated from Unstructured Data

Laura Battaglia, Timothy Christensen, Stephen Hansen and Szymon Sacher

No 11119, CESifo Working Paper Series from CESifo

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.

Keywords: unstructured data; information retrieval; topic modeling; Hamiltonian Monte Carlo; measurement error (search for similar items in EconPapers)
JEL-codes: C11 C51 C55 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Inference for regression with variables generated from unstructured data (2024)
Working Paper: Inference for Regression with Variables Generated from Unstructured Data (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_11119

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