Random-effects and fixed-effects panel-data models do not allow me to use observable information of previous periods in my model. They are static. Dynamic panel-data models use current and past information.

For instance, I may model current health outcomes as a function of health outcomes in the past— a sensible modeling assumption— and of past observable and unobservable characteristics. We have fictional data for 1, people from to The outcome of interest is income incomeand the explanatory variables are years of schooling educ and an indicator for marital status married. Below, we fit an Arellano—Bond model using xtabond.

A couple of elements in the output table are different from what one would expect. The output includes a coefficient for the lagged value of the dependent variable that we did not specify in the command. In the Arellano—Bond framework, the value of the dependent variable in the previous period is a predictor for the current value of the dependent variable.

Stata includes the value of the dependent variable in the previous period for us. Another noteworthy aspect that appears in the table is the mention of 39 instruments in the header. This is followed by a footnote that refers to GMM and standard-type instruments. Here a bit of math will help us understand what is going on. As in the fixed-effects framework, we assume the time-invariant unobserved component is related to the regressors.

When unobservables and observables are correlated, we have an endogeneity problem that yields inconsistent parameter estimates if we use a conventional linear panel-data estimator. One solution is taking first-differences of the relationship of interest. However, the strategy of taking first-differences does not work. The second equation above illustrates one of our regressors is related to our unobservables.

The solution is instrumental variables. Which instrumental variables? Arellano—Bond suggest the second lags of the dependent variable and all the feasible lags thereafter. This generates the set of moment conditions defined by. This gives us 36 instruments which are what the table calls GMM-type instruments.

GMM has been explored in the blog post Estimating parameters by maximum likelihood and method of moments using mlexp and gmm and we will talk about it in a later post. The other three instruments are given by the first difference of the regressors educ and married and the constant.

### Ignore at your Peril: Dynamic Panel Data

This is no different from two-stage least squares, where we include the exogenous variables as part of our instrument list. We can test these conditions in Stata using estat abond. In essence, the differenced unobserved time-invariant component should be unrelated to the second lag of the dependent variable and the lags thereafter.

If this is not the case, we are back to the initial problem, endogeneity. Again, a bit of math will help us understand what is going on. The unobservable is serially correlated of order 1 but not serially correlated of orders 2 or beyond. Another way of saying this is that the differenced time-varying unobserved component is serially correlated with an order greater than 1.

We reject no autocorrelation of order 1 and cannot reject no autocorrelation of order 2. There is evidence that the Arellano—Bond model assumptions are satisfied. If this were not the case, we would have to look for different instruments.

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Essentially, we would have to fit a different dynamic model. This is what the xtdpd command allows us to do, but it is beyond the scope of this post.Login or Register Log in with. Forums FAQ. Search in titles only. Posts Latest Activity.

Page of 1. Filtered by:. Max Flower. Hello everyone, Here is my issue: Due to endogeneity issues with my variables I am thinking about using a System GMM regression using xtabond2. I never used xtabond2 before, only Fixed or Random Effects models for panel data. Setting : I have panel data for banks large N, small T and although I read several articles about the "xtabond2"-command I still don't know how to practically employ the command properly in Stata.

I read: "How to do xtabond2: [ Regarding the necessary instrumentsI want to choose them according to some papers that can be seen as role models for my work since I have similar data and research questions. In one paper they say about the instruments: "The first objective is that one set of instruments needs to comply with the identification of the GMM estimation method. We achieve this by exploiting the first lag difference of bank characteristics as instruments in the level equation and the second and the third lags of bank characteristics as instruments in the difference equation.

Implementation in Stata: So when it comes to using in the corresponding command in Stata, I am not sure how to actually do so. Based on similar papers, I want to use a two step System GMM regression with the Windmeijer correction, thererfore "twostep robust" should be added after the command. My understanding of the xtabond2 command is so far: xtabond2 depvar varlist of exogenous indepvarsgmmstyle instruments and endogenous vars ivstyle???

My questions: 1. Am I right that I fill the bracket after "gmmstyle " with the instruments? What variables need to come after "ivstyle "? Can you recommend a book that deals with the practical implementation of xtabond2?

Thank you so much! Tags: None. Enrique Pinzon StataCorp. Hello Max, I have written something on that topic that might be helpful. It is for xtabond instead of xtabond2 but the logic is similar.

Comment Post Cancel. Thank you so much Enrique! However, when I employ the Windmeijer correction in my "xtdpdsys" model, "estat abond" tells me that the model assumptions are not satisfied. As described on the page of your first link, I guess I have to look for different instruments and thus I have to fit a different dynamic model with the "xtdpd"-command. That is where the post of your second link comes into play.

Previous Next. Yes No. OK Cancel.The first is the Arellano-Bond estimator, which is also available with xtabond without the two-step finite-sample correction described below. The second is an augmented version outlined in Arellano and Bover and fully developed in Blundell and Bond Arellano and Bond developed a Generalized Method of Moments estimator that treats the model as a system of equations, one for each time period.

The predetermined and endogenous variables in first differences are instrumented with suitable lags of their own levels. Strictly exogenous regressors, as well as any other instruments, can enter the instrument matrix in the conventional instrumental variables fashion: in first differences, with one column per instrument.

A problem with the original Arellano-Bond estimator is that lagged levels are often poor instruments for first differences, especially for variables that are close to a random walk.

Arellano and Bover described how, if the original equations in levels were added to the system, additional moment conditions could be brought to bear to increase efficiency. In these equations, predetermined and endogenous variables in levels are instrumented with suitable lags of their own first differences. Blundell and Bond articulated the necessary assumptions for this augmented estimator more precisely and tested it with Monte Carlo simulations.

But though asymptotically more efficient, the two-step estimates of the standard errors tend to be severely downward biased Arellano and Bond ; Blundell and Bond To compensate, xtabond2, unlike xtabond, makes available a finite-sample correction to the two-step covariance matrix derived by Windmeijer This can make twostep robust more efficient than onestep robust, especially for system GMM.

Note: the routine requires an up-to-date version of Stata 7 with the 21jun update installed. Users of Stata version 10 25feb update or later can take advantage of speed improvements due to Mata. Language: Stata Requires: Stata version 7.

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Is your work missing from RePEc? Here is how to contribute. Questions or problems? Page updated Handle: RePEc:boc:bocode:sLogin or Register Log in with. Forums FAQ. Search in titles only. Posts Latest Activity. Page of 1. Filtered by:.

Tom Hardwick. Hi, I am currently investigating the relationship between financial development and GDP growth. My results are as below and I am trying to interpret my diagnostic test results for over identification of instruments.

Please can someone explain to me whether I should be looking at the Hansen or Sargan statistics. I have noticed that dropping robust SE usage will remove the Hansen statistic, does this mean I should be using Hansen under robust SE? Also, if both tests have the same null hypotheses, how can each p value be so different?

Should I be looking at both of the results to interpret? YEAR, eq level robust Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm. Warning: Number of instruments may be large relative to number of observations. Warning: Two-step estimated covariance matrix of moments is singular. Using a generalized inverse to calculate robust weighting matrix for Hansen test.

YEAR Hansen test of overid.Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv. Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. David Roodman Published Mathematics. The first is the Arellano-Bond estimator, which is also available with xtabond without the two-step finite-sample correction described below.

The second is an augmented version outlined in Arellano and Bover and fully developed in Blundell and Bond Arellano and Bond developed a Generalized Method of Moments estimator that treats the model as a system of equations, one for each time period.

Save to Library. Create Alert. Launch Research Feed. Share This Paper. Citations Publications citing this paper. Internationalization and organizational ambidexterity for sustainable performance : moderating effects of firm-specific advantages and competitive strategies Krishna Raj Bhandari Business Using panel econometric methods to estimate the effect of milk consumption on the mortality rate of prostate and ovarian cancer Tobias HagenStefanie Waldeck Economics Banking sector competition and its impact on banks' risk-taking and interest margins in the Central and East European countries Mustafa Arben Economics Regional Selective Assistance in Scotland: Does it make a difference to plant productivity?

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**(Stata 13): How to Estimate Two-Step System GMM #gmm #onestepgmm #twostepgmm #yeardummies**

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If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation. Please note that corrections may take a couple of weeks to filter through the various RePEc services. Economic literature: papersarticlessoftwarechaptersbooks. Registered: David Malin Roodman. The first is the Arellano-Bond estimator, which is also available with xtabond without the two-step finite-sample correction described below.

The second is an augmented version outlined in Arellano and Bover and fully developed in Blundell and Bond Arellano and Bond developed a Generalized Method of Moments estimator that treats the model as a system of equations, one for each time period.

The predetermined and endogenous variables in first differences are instrumented with suitable lags of their own levels. Strictly exogenous regressors, as well as any other instruments, can enter the instrument matrix in the conventional instrumental variables fashion: in first differences, with one column per instrument.

A problem with the original Arellano-Bond estimator is that lagged levels are often poor instruments for first differences, especially for variables that are close to a random walk.This working paper by CGD research fellow David Roodman provides an introduction to a particular class of econometric techniques, "dynamic panel estimators. The techniques discussed are specifically designed to extract causal lessons from data on a large number of individuals whether countries, firms or people each of which is observed only a few times, such as annually over five or ten years.

These techniques were developed in the s by authors such as Manuel Arellano, Richard Blundell and Olympia Bover, and have been widely applied to estimate everything from the impact of foreign aid to the importance of financial sector development to the effects of AIDS deaths on households. The present paper contributes to this literature pedagogically, by providing an original synthesis and exposition of the literature on these "dynamic panel estimators," and practically, by presenting the first implementation of some of these techniques in Stata.

Stata is designed to encourage users to develop new commands for it, which other users can then use or even modify. In this paper Roodman introduces abar and xtabond2, which is now one of the most frequently downloaded user-written Stata commands in the world. Stata's partially open-source architecture has encouraged the growth of a vibrant world-wide community of researchers, which benefits not only from improvements made to Stata by the parent corporation, but also from the voluntary contributions of other users.

Stata is arguably one of the best examples of a combination of private for-profit incentives and voluntary open-source incentives in the joint creation of a global public good.

A related paper, A Short Note on the Theme of Too Many Instrumentselaborates on an important warning in "How to Do xtabond2" about serious risks of accidental misuse of these estimators. Skip to main content. Our Experts. Attend an Event. Connect with Us. For Media.

Working Papers. David Roodman. December 6,

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