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1. The condition T>=2 refers to a model where the initial observation is observed for period 0, i.e. effectively you need at least 3 time periods when the first-differenced lagged dependent variable is instrumented with the second lag of the dependent variable in levels.
2. Almost everything you can do with xtabond2, you can also do with xtdpdgmm. Instrumenting for endogenous variables with the latter command works in a very similar way. Please see the help file or my 2019 London Stata Conference presentation:
3. With a binary dependent variable, you can still estimate a linear regression model. This is then labelled a linear probability model. Again, no difference between xtabond2 and xtdpdgmm here.
My name is Dinh, from Vietnam. Currently, I am doing a research related to the health effect of housing. My data is panel data with 5 waves and round 1900 observations of each wave.
The estimated model is constructed as follow:
Hi,t= b0*Hi,t-1+b0*Housingi,t+Xi,t*alpha+error term.
I also read your interesting paper (https://onlinelibrary-wiley-com.ezpr....1002/jae.2681). But I am still stuck with my data. Could I have some questions:
1. As you mentioned in your paper, T > =2, so is it ok to estimate the model by your command with T=3? I know that we need at least 5 years for xtabond2 if running two-step GMM.
2. In your paper, the second assumption is that X (like my function include vector X and Housing) is strictly exogenous. However, I am arguing that Housing condition is endogenous. If using xtabond2, I can add it into (gmm(H Housing, lag (2 3) collapse)). So I cannot use your command if I hold my assumption of endogenous housing?
3. Your command is only to use for linear dependent variable? No for binary variable? I also think xtabond2 is only for linear dependent variable. But I did read a paper using xtabond2 with binary dependent.
Thank you in advance, and I hope you all the best!
Dinh
If you are located in China, there might be firewall restrictions that prevent you from installing Stata packages directly from my website. As the new version is not yet available on SSC, I have just sent you the source files for a local installation by e-mail.
Dear sebastian Kripfganz,I have tried to update xtdpdgmm from http://www.kripfganz.de/stata/.But I failed to update it.I have sent en email to you.Can you send me the updated adofile and helpfile ?Thanks in advance.
There is a new update to version 2.3.2 now available on my personal website:
Code:
net install xtdpdgmm, from(http://www.kripfganz.de/stata/) replace
This update fixes an annoying bug that produced an error message when the command was run with the nl(noserial) option on a data set in which the panel identifier was named different than "id". (Apparently, all of my test data sets had a panel identifier variable named "id" which is why I never noticed this bug.) Many thanks to Luca Uberti for flagging this problem.
I used this opportunity to add another option to the estat mmsc postestimation command. The penalty term for the BIC and HQIC versions of the model and moment selection criteria depend on a measure of the sample size, ln(N) and ln(ln(N)), respectively. So far, for N the command always used the number of groups (panels). Now, there is a new option that allows to choose N either as the number of groups, n(groups), the number of clusters, n(cluster), or the total number of observations, n(obs).
If the xtdpdgmm command was run with the vce(cluster) option, the default for estat mmsc is now to use the number of clusters for N. Otherwise, it remains the number of groups. (There is actually a related issue with the conventional BIC after other estimation commands; see help bic note.)
I need your help regarding the model. ROA,๐๐ก= ๐ผROA๐ + ๐ROA๐๐กโ1 + โ ฮดROA_sic_t SIC_Year sic_t+ ฮตROA,๐๐ก;
where:
1)SIC_Year sic_t is the year-specific industry fixed effect.
2)Coefficient ๐ผROA๐ is the firm-specific constant
3)๐s are the first-order autoregressive coefficient estimates.
I have used XTABOND command as follows.
. xtabond ROA year2011-year2019 fyear
fyear is time variable in my dataset. It is unbalaced panel data. Gvkey are firm name codes, SIC variable is industry level codes.
In the studies it is written: "We use the deviations of the actual values from the forecasts (i.e., forecast errors) as the measure of abnormal profitability (aROAit) to find firms with greater-than-normal profitability (aROAit>0)"
How would you find forecast and their deviations from actual values? I tried to do it with the forecasting of Stata but as it is unbalanced panel data I receive error each time. With the predict comman I get some big numbers each time, so I am wondering if my Xtabond command was correct and if it was same as in study(not sure if I correctly do it with SIC codes)?
Assuming that the coefficients are at a comparable scale, you could probably use the suest command to combine the two regressions. Then you can test for equality of the coefficients with the test command. For this to work, you need to run the two xtdpdgmm regressions with the auxiliary option, store the results with estimates store, and then call suest with the two stored estimation results.
I have two equations of dynamic panel data. One is y=b0+b1x1+b2x2+b3x3, and the other is y=A0+A1x1+A2x4+A3x5. I get the coefficients through the xtdpdgmm. Now, I want to compare b2 and A3 to know which has a larger effect on y. Would you give me some advice?
xtdpdgmm does not provide standardized regression coefficients. You would need to standardize all of your variables manually before running the regression.
I can hardly imagine a situation in the context of dynamic panel models where this is meaningful. There is a lot of things that can go wrong and the interpretation of the results becomes anything than straightforward.
Hello Dr. Kripfganz, how can i get standard regression coefficents from your xtdpdgmm? As far as I know๏ผyour xtdpdgmm presents nonstandard coefficents.
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