Hi, i am taking a chance asking here, as my teacher seems to be having a nice vacation, not answering my email. I am writing my master thesis, but I have a hard time understanding which regression model to use.

The dataset I am using is of panel structure - 1,000 firms (500 Swedish, 100 Danish, 200 Norwegian and 200 Finish) with years ranging from 2004 to 2017. It is unbalanced and has gabs, because I have removed observations with missing values, book leverage above 1, total assets below 10 million dollars and market-to-book ratios above 10.

The regression I am running is:

The results from different versions of this model can be seen in the table below.

1. I do not know which model to trust?

2. I am confused to why the OLS estimated coeffecients (column 1) is the same as those from clustering the standard errors on both time and firm (column 9). I thought, that by clustering on these two dimensions, I would be able to remove serial correlation and heteroskedasticity and as such, the coeffecients would be different from those of OLS?

3. I am also confused to why the fixed effects regressions are so different from the OLS .

In general I find the litterature on this matter very unfullfilling as it is a LOT OF IFS and WHYS. There is never a clear answer to get. My teacher says - use fixed - but when I ask why, he can't answer. He is one of those corporate finance dudes, who just by default sticks to fixed effects. However, it does not provide me with the results I am looking for - the paper I am following use OLS with robust and Fama Macbeth and get results similar to those I get from doing this - however, the fixed effects model ruins the variable of importance - EFWAMB - as it turns small and insignificant.

So, if anybody could please take a moment and reflect upon my setting of data - the variables included - and come up with a good recommendation on which model to go with and why, by answering questions 1, 2 and 3 above, I would be more than greatfull.

In case you should ask for it, here are the different statacode used to estimate the models above:

OLS robust:
Fixed effects:
Fixed effects, cluster year:
Random effects:
Fama Macbeth cross-sectional:
The Fama Macbeth two path regression is estimated manually by first making 1000 time series regressions, which provides me with 5*1000 betas using:

I then do 14 cross-sectional regressions, one for each time period 2004 - 2017 with the estimated betas from above being the new independant variabes, which provides me with 5*14 new beta values (gamma) using:

I then open the gamme file, and take the average of the 14 betas in each row - this is my beta estimates reported in the model above. To get t-test, I simply divide this coefficient through with the square root of the variance of the betas divided by 14.

4. Why do I not get the same coefficients and t-stats as those calculated using the xtfmb command?

Best regards,

Morten

The dataset I am using is of panel structure - 1,000 firms (500 Swedish, 100 Danish, 200 Norwegian and 200 Finish) with years ranging from 2004 to 2017. It is unbalanced and has gabs, because I have removed observations with missing values, book leverage above 1, total assets below 10 million dollars and market-to-book ratios above 10.

The regression I am running is:

Code:

book leverage = EFWAMB(t-1) + Market-to-book(t-1) + Tangibility(t-1) + Profitability(t-1) + Size(t-1)

1. I do not know which model to trust?

2. I am confused to why the OLS estimated coeffecients (column 1) is the same as those from clustering the standard errors on both time and firm (column 9). I thought, that by clustering on these two dimensions, I would be able to remove serial correlation and heteroskedasticity and as such, the coeffecients would be different from those of OLS?

3. I am also confused to why the fixed effects regressions are so different from the OLS .

In general I find the litterature on this matter very unfullfilling as it is a LOT OF IFS and WHYS. There is never a clear answer to get. My teacher says - use fixed - but when I ask why, he can't answer. He is one of those corporate finance dudes, who just by default sticks to fixed effects. However, it does not provide me with the results I am looking for - the paper I am following use OLS with robust and Fama Macbeth and get results similar to those I get from doing this - however, the fixed effects model ruins the variable of importance - EFWAMB - as it turns small and insignificant.

So, if anybody could please take a moment and reflect upon my setting of data - the variables included - and come up with a good recommendation on which model to go with and why, by answering questions 1, 2 and 3 above, I would be more than greatfull.

In case you should ask for it, here are the different statacode used to estimate the models above:

OLS robust:

Code:

reg b_lev L1.efwamb L1.mb L1.tang L1.prof L1.size, robust

Code:

areg b_lev L1.efwamb L1.mb L1.tang L1.prof L1.size, absorb(gvkey)

Code:

xi: areg b_lev L1.efwamb L1.mb L1.tang L1.prof L1.size i.year, absorb(gvkey)

Code:

xtreg b_lev L1.efwamb L1.mb L1.tang L1.prof L1.size

Code:

xtfmb b_lev L1.efwamb L1.mb L1.tang L1.prof L1.size

Code:

statsby, by(gvkey) saving(betas): reg b_lev L1.efwamb L1.mb L1.tang L1.prof L1.size merge m:1 gvkey using betas drop _merge

Code:

statsby, by(year) saving(gamma): reg b_lev b1 b2 b3 b4 b5

4. Why do I not get the same coefficients and t-stats as those calculated using the xtfmb command?

Best regards,

Morten

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