Year: 2025
Type: article
Abstract: We investigate a bracketing property that purports to yield upper- and lower bounds on the treatment effects obtained from a fixed effects (FE) and lagged dependent variable (LDV) model. Referencing both analytical results and a Monte Carlo simulation, we explore the conditions under which the bracketing property holds, confirming this to be the case when the data generating process (DGP) is characterized by either unobserved heterogeneity or feedback effects from a lagged dependent variable. However, when the DGP is characterized by both features simultaneously, we find that bracketing of the treatment effect only holds under certain conditions—but not in general. Practitioners can nevertheless obtain the lower bound estimate by referencing a model that includes both FE and an LDV. While the Nickell bias in the coefficient of the LDV is known to be of order $1/T$ , we show that the Nickell-type bias in the estimator of the treatment effect is of order $1/T^2$ .
Source: Political Analysis
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,550
Awards: G1393714528, G2005532712, G2890341565, Verbundvorhaben ARIADNE: Evidenzbasiertes Assessment für die Gestaltung der deutschen Energiewende - Teilvorhaben C0, G446978933, Verbundvorhaben ARIADNE2: Evidenzbasiertes Assessment für die Gestaltung der deutschen Energiewende auf dem Weg zur Klimaneutralität -Teilvorhaben: C0-2
OpenAlex ID: W4411151499