# Research-Module-Econometrics-Interactive-Fixed-Effects **Repository Path**: owen560/Research-Module-Econometrics-Interactive-Fixed-Effects ## Basic Information - **Project Name**: Research-Module-Econometrics-Interactive-Fixed-Effects - **Description**: Code corresponding to my research module paper on the Interactive-Effects Estimator. - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2023-01-16 - **Last Updated**: 2023-01-16 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README This repository contains the code for my econometrics research module, which considers interactive-effects estimation in panel data with large cross-sectional and time dimensions, and aims at giving guidance on when and how to use interactive-effects models. Especially, this codes implements the Interactive-Effects Estimator (IFE) of [Bai (2009)](https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA6135) in R and corresponding Monte-Carlo Simulations descirbed in the main paper. Further, this repository provides the code that shows the effect of using the IFE estimator in a real-world data context by replicating a study by [Voigtlaender (2014)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1029312), who analyses intersectoral linkages and white-collar labor demand in U.S. manufacturing by relying on an additive-effects framework. I find that the use of the IFE model leads too much more consistent results across the inclusion of various control variables by utilizing additional information in the residuals and show that the original estimates understate the causal effect. 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