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Your interpretation goes in the right direction, but it does not sound fully correct to me. It's important to interpret any coefficient for political risk as a conditional estimate: It compares managerial risk-taking between different political risks given a certain level of all other covariables. In your case, this means in a given industry in a given year ...


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As this question has so many views and ranks highly in google search, I would like to add to the excellent answer before (+1) that one has to be careful about actually interpreting the effects as within effects. Probably the most common empirical mistake in applied work. There is a very nice paper on this (not mine): Mummolo, J., & Peterson, E. (2018). ...


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# Reproducing the OP's panel # N = 4 # T = 2 # 8 unit-time observations library(tidyr) library(dplyr) library(tibble) library(plm) set.seed(2021) df <- tibble( unit = c(1, 1, 2, 2, 3, 3, 4, 4), time = c(1, 2, 1, 2, 1, 2, 1, 2), y = runif(8, 0, 1), x = c(9454.5, 9368.55, 9454.5, 9368.55, 9454.5, 9368.55, 9454.5, 9368.55), z = c(11175.81, 11175....


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Notice the warning message at the top of the model summary: ALL 14 residuals are 0. This suggests the model was fit using only 14 observations. You're estimating 20 parameters using only 14 independent pieces of information. I am doing a multiple regression analysis and I wanted to inspect the time effect by using factor(Year) in R. You don't explicitly ...


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Following @dimitris answer In the logistic multi-level models the odds ratio is a cluster-specific, which means it is based on the assumption of holding the random effect constant as well as other covaraites in the model. This is particularly problematic for a cluster-level variable as it has no variation within clusters. But you can transform the cluster-...


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I am using 2016 as a reference point, so it seems to me at first glance that pooled cross-sectional data is the way to go with time dummies. Perhaps. It appears your data is more aptly described as repeated cross-sectional data. The survey repeatedly samples a new subset of individuals in each survey wave. You could certainly estimate a standard linear ...


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