Julia FixedEffectsModels IV回归与R IV回归不匹配 [英] Julia FixedEffectsModels IV regression doesn't match R IV regression
问题描述
当我在R和Julia中运行(我认为是相同的回归)时，得到的结果非常不同.我认为这是因为IV回归正在使用一个指标变量来检测另一个指标变量，但我无法弄清楚是否做错了其他事情.
When I run (what I think is the same regression) in R and in Julia I get very different results. I think this is because the IV regression is using an indicator variable to instrument for another indicator variable but I can't figure out if I am doing something else wrong.
我尝试了几种不同的方法，但是数据开始是这样的:
I have tried several different methods, but the data starts like this:
rc D tau May_ret Jun_ret Jul_ret Aug_ret Sep_ret
1 -43 0 0 0.04529617 0.02106667 0.009868421 0.032573290 0.010473186
2 -19 0 0 0.01973333 0.05752213 -0.020920502 0.027521368 -0.029535865
3 74 1 1 0.33505189 0.04494382 -0.150537640 0.246835440 0.010152284
4 54 1 1 0.03602649 0.06168831 0.030581040 0.002611276 -0.027027028
5 22 1 1 -0.01584158 -0.08417509 -0.088235296 0.012903226 0.100000001
6 7 1 1 0.02484472 0.08000000 0.039548021 0.065217391 0.006122449
D和tau并不是完全线性的，但是它们很接近(并且tau被用作D的仪器).
D and tau are not perfectly linear, but they are close (and tau is being used to instrument for D).
我的R代码是
library(AER)
library(stargazer)
temp_df <- read.csv(file="output.csv")
addmay <- ivreg(May_ret ~ D*rc|tau*rc, data=temp_df)
addjun <- ivreg(Jun_ret ~ D*rc|tau*rc, data=temp_df)
addjul <- ivreg(Jul_ret ~ D*rc|tau*rc, data=temp_df)
addaug <- ivreg(Aug_ret ~ D*rc|tau*rc, data=temp_df)
addsep <- ivreg(Sep_ret ~ D*rc|tau*rc, data=temp_df)
stargazer(addmay, addjun, addjul, addaug, addsep, title="Addition Return Effect", align=TRUE, type="text", report="vc*t")
哪个生产
========================================================================================================
Dependent variable:
------------------------------------------------------------------------------------
May_ret Jun_ret Jul_ret Aug_ret Sep_ret
(1) (2) (3) (4) (5)
--------------------------------------------------------------------------------------------------------
D 0.006 0.033 -0.008 0.028 -0.044*
t = 0.304 t = 1.600 t = -0.353 t = 1.283 t = -1.943
rc -0.0001 -0.0001 -0.0003 -0.0003* 0.0002
t = -0.736 t = -0.730 t = -1.495 t = -1.784 t = 1.094
D:rc 0.0002 -0.0002 0.001*** 0.0002 0.0001
t = 0.817 t = -0.747 t = 2.660 t = 0.706 t = 0.392
Constant 0.0002 -0.021* -0.024* -0.021* 0.009
t = 0.014 t = -1.778 t = -1.831 t = -1.673 t = 0.654
--------------------------------------------------------------------------------------------------------
Observations 827 828 824 823 818
R2 0.003 0.007 0.007 0.026 0.012
Adjusted R2 -0.0005 0.004 0.004 0.023 0.009
Residual Std. Error 0.106 (df = 823) 0.111 (df = 824) 0.125 (df = 820) 0.116 (df = 819) 0.123 (df = 814)
========================================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01
我的Julia代码是
using FixedEffectModels, CSV, RegressionTables;
test_df = CSV.read("output.csv");
additions = Dict("May" => reg(test_df, @model(May_ret ~ (D*rc~tau*rc))),
"Jun" => reg(test_df, @model(Jun_ret ~ (D*rc~tau*rc))),
"Jul" => reg(test_df, @model(Jul_ret ~ (D*rc~tau*rc))),
"Aug" => reg(test_df, @model(Aug_ret ~ (D*rc~tau*rc))),
"Sep" => reg(test_df, @model(Sep_ret ~ (D*rc~tau*rc))));
regtable(additions["May"], additions["Jun"], additions["Jul"], additions["Aug"], additions["Sep"]; below_statistic=:tstat, regression_statistics=[:nobs, :r2, :f])
哪个生产
------------------------------------------------------------------
May_ret Jun_ret Jul_ret Aug_ret Sep_ret
-------- -------- -------- -------- --------
(1) (2) (3) (4) (5)
------------------------------------------------------------------
(Intercept) 0.008 -0.013** -0.006 -0.021 -0.004
(1.580) (-2.603) (-1.117) (-1.673) (-0.771)
D -0.004 0.023 -0.032 0.028 -0.027
(-0.268) (1.503) (-1.854) (1.283) (-1.627)
rc 0.000*** 0.000*** 0.000*** -0.000 0.000***
(NaN) (NaN) (NaN) (-1.784) (NaN)
D & rc 0.000 -0.000 0.001* 0.000 0.000
(0.496) (-1.372) (2.198) (0.706) (1.189)
------------------------------------------------------------------
Estimator IV IV IV IV IV
------------------------------------------------------------------
N 827 828 824 823 818
R2 0.000 0.008 0.004 0.026 0.004
F 0.200 6.306 9.604 8.526 7.380
------------------------------------------------------------------
可以看出，主要区别是FixedEffectsModels似乎从回归中删除了rc变量.我已经检查了多重共线性，但似乎不是问题所在.我的代码中有一部分我用错了吗?
As can be seen, the main difference is FixedEffectsModels seems to be dropping the rc variable from the regression. I have checked for multicollinearity but it does not seem like it is the problem. Is there a part of my code that I am using wrong?
我正在尝试复制张仁成，Harrison Hong，Inessa Liskovich的表4，回归不连续性和股票市场指数的价格效应，《金融研究评论》，第28卷，第1期，2015年1月，第212页–246， https://doi.org/10.1093/rfs/hhu041
I am trying to replicate Table 4 of Yen-Cheng Chang, Harrison Hong, Inessa Liskovich, Regression Discontinuity and the Price Effects of Stock Market Indexing, The Review of Financial Studies, Volume 28, Issue 1, January 2015, Pages 212–246, https://doi.org/10.1093/rfs/hhu041
它们提供了部分数据.我在此处上传了完整的数据: https://github.com/junder873/data.git
And they provide part of the data. I uploaded the full data here: https://github.com/junder873/data.git
推荐答案
经过一些工作，我想出了自己的答案. Julia FixedEffectModels在回归的内生和外生部分都包含了rc变量.解决此问题的正确方法是事先预先交互变量.这将创建正确的输出.例如:
After some work, I figured out my own answer. Julia FixedEffectModels was including the rc variable in both the endogenous and exogenous part of the regression. The correct way to fix this is to basically interact the variables beforehand. This will create the correct output. For example:
reg(test_df, @model(May_ret ~ rc + (rc_D + D~rc_tau+ tau)))
rc_D是rc.*D，rc_tau是rc.*tau的地方，基本上是创建两个新列并将它们手动添加到回归中，而不是让模型决定要添加哪些列.这样会产生相同的结果.
Where rc_D is rc.*D and rc_tau is rc.*tau, basically creating two new columns and adding them manually to the regression instead of letting the model decide which columns to add. This produces the same results.
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