I have a regression of some mutual fund returns on ETF, x_family, ETF#X_family as well as time and benchmark fixed effect. the significance of the ETF#X_family variable is of my interest.
I was not sure if I have to use both time and benchmark clustering. I can assume that fixed effects capture the time and benchmark effects by demeaning. However, the returns at time t might be correlated across (12) benchmarks (I m confident about this) and even across all observation (48) at time t. for example, observation in benchmark 1 might be correlated with the ones in benchmark 2 (at t). Based on this justification, I applied cgmwildboot as
the results are weired. All variables (including the FEs) p-values are 1 but I have some confidence intervals that I can calculate the standard errors (~CI/4).
I also tried cgmreg and it gives me an error "variance matrix is non-symmetric or highly singular"
My question is that if I need to time clustering for this problem and if I should, how should I interpret the cgmwildboot 1 pvalues.
Thank you in advance
I was not sure if I have to use both time and benchmark clustering. I can assume that fixed effects capture the time and benchmark effects by demeaning. However, the returns at time t might be correlated across (12) benchmarks (I m confident about this) and even across all observation (48) at time t. for example, observation in benchmark 1 might be correlated with the ones in benchmark 2 (at t). Based on this justification, I applied cgmwildboot as
Code:
cgmwildboot return Xfamily ETF ETF#Xfamily benchmark_* date*, cluster(benchmark date) bootcluster(benchmark) seed(999)
I also tried cgmreg and it gives me an error "variance matrix is non-symmetric or highly singular"
My question is that if I need to time clustering for this problem and if I should, how should I interpret the cgmwildboot 1 pvalues.
Thank you in advance
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