Dear Stata community,
I want to estimate the effect of reforms on twelve EU countries on the net fund flows on investment funds. Each fund is matched to a another with respect to its country and performance and has been labeled as either cheap, equally expensive or expensive according to the costs of itself and its respective counterpart.
As already widely discussed in other topics, I created a dummy for time and treatment with this code:
My aim is to show that more expensive funds had lower net fund flows than cheap funds after being treated. Unfortunately I am unsure about which model and how to get the desired results.
I used this code:
1 Question) Even though I have three different values for costs, my regression gives me only two values. Do you have any idea, why this could be the case?
2) I thought about doing the regression for each costs level, which than can be compared, thus I would circumvent this problem. I used:
Is there any other possibilty to get this in one regression?
However I am not sure if my regression is the right choice for my problem. The Breusch/Pagan test for random effects suggests a FE Model, aswell as the Hausman Test.
But using
seems to misinterpret my data, since I don't want to rely only on individual fund variation of netfundflows, as I assume that variable costs has a major influence on netfundflows.
I therefore thought of the regress comand to control for the fe of costs but not on the individual level. I tried the following code:
I thought that this should absorb the fix effects on the individual level, but adds fixed effects for the costs variable? Am I right?
Since I get this results, I think I might have been wrong with this code.
Any comment would be highly appreciated
Best regards
Nils
I want to estimate the effect of reforms on twelve EU countries on the net fund flows on investment funds. Each fund is matched to a another with respect to its country and performance and has been labeled as either cheap, equally expensive or expensive according to the costs of itself and its respective counterpart.
As already widely discussed in other topics, I created a dummy for time and treatment with this code:
Code:
generate d_time = 0 if date < date("20071101","YMD") & date > date("20051101","YMD") replace d_time = 1 if date > date("20071101","YMD") & date < date("20091101","YMD") generate d_treat_all = 0 & date < date("20091101","YMD") replace d_treat_all = 1 if country <10 & date < date("20091101","YMD") replace d_treat_all = 1 if country == 11 & date < date("20091101","YMD")
I used this code:
Code:
xtset id xtset id date xtreg netfundflow i.costs d_time##d_treat, cluster(id)
HTML Code:
Linear regression Number of obs = 35916 F( 5, 1446) = 7.42 Prob > F = 0.0000 R-squared = 0.0006 Root MSE = 548.99 (Std. Err. adjusted for 1447 clusters in id) Robust netfundflow Coef. Std. Err. t P>t [95% Conf. Interval] costs High -16.27294 5.539093 -2.94 0.003 -27.13846 -5.407421 Equal -6.636716 6.54486 -1.01 0.311 -19.47515 6.201721 1.d_time 33.29411 12.3204 2.70 0.007 9.126352 57.46187 1.d_treat 6.112733 5.944947 1.03 0.304 -5.548909 17.77438 d_time#d_treat 1 1 -27.25072 12.22966 -2.23 0.026 -51.2405 -3.260937 _cons 5.901751 5.675747 1.04 0.299 -5.231828 17.03533
Code:
bysort costs: xtreg netfundflow i.costs d_time##d_treat, cluster(id)
However I am not sure if my regression is the right choice for my problem. The Breusch/Pagan test for random effects suggests a FE Model, aswell as the Hausman Test.
But using
Code:
xtreg netfundflow i.costs d_time##d_treat, cluster(id) fe
I therefore thought of the regress comand to control for the fe of costs but not on the individual level. I tried the following code:
Code:
reghdfe netfundflow i.costs i.d_treat##i.d_time, absorb(id) vce(cluster id costs)
Since I get this results, I think I might have been wrong with this code.
HTML Code:
(dropped 66 singleton observations) (converged in 1 iterations) note: 1.d_treat omitted because of collinearity Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied. WARNING: Missing F statistic (dropped variables due to collinearity or too few clusters). HDFE Linear regression Number of obs = 35,850 Absorbing 1 HDFE group F( 3, 2) = . Statistics robust to heteroskedasticity Prob > F = . R-squared = 0.0137 Adj R-squared = -0.0260 Number of clusters (id) = 1,381 Within R-sq. = 0.0009 Number of clusters (costs) = 3 Root MSE = 556.7085 (Std. Err. adjusted for 3 clusters in id costs) Robust netfundflow Coef. Std. Err. t P>t [95% Conf. Interval] costs High -61.58262 49.06534 -1.26 0.336 -272.6937 149.5285 Equal -80.7145 58.56219 -1.38 0.302 -332.6873 171.2583 1.d_treat 0 (empty) 1.d_time 45.10881 25.86124 1.74 0.223 -66.16312 156.3807 d_treat#d_time 1 1 -38.66821 21.77473 -1.78 0.218 -132.3573 55.02087 Absorbed degrees of freedom: Absorbed FE Num. Coefs. = Categories - Redundant id 0 1381 1381 * * = fixed effect nested within cluster; treated as redundant for DoF computation
Best regards
Nils
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