Inference for Regression with Variables Generated from Unstructured Data

Laura Battaglia, Tim Christensen, Stephen Hansen and Szymon Sacher

No 19115, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Date: 2024-05
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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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