Pop_c float %9.0g popcl Categorized population Marriage long %12.0gc Number of marriages > label data "1980 Census data by state: v2" * Now the three categories are presented as low, medium and high * Then we attach the value label popcl to the variable pop_c > label define popcl 1 "low" 2 "medium" 3 "high" Let’s label them as low, medium and high. * Remember we categorized pop_c into three categories: 1,2 and 3 Poplt5 long %12.0gc Pop, label variable pop0_17 "Pop, label variable pop_c "Categorized population" For instance, I may model current health outcomes as a function of health outcomes in the past a sensible modeling. Dynamic panel-data models use current and past information. Here we create another new variable called pop_c2 then do the recode in the same manner as we did for pop_c. Random-effects and fixed-effects panel-data models do not allow me to use observable information of previous periods in my model. We can use the -recode- command to recode variables as well. Then we create a new variable called pop_c and transform the original variable pop into three categories.
Here we create the youth population variable again, but this time we make it into thousands and replace the one we just created. replace-: replace contents of existing variables > order state state2 region pop poplt5 pop0_17 * Summary statistics for the three variables Poplt5 long %12.0gc Pop, generate pop0_17 = poplt5 + pop5_17 State2 str2 %-2s Two-letter state abbreviation Variable name type format label variable label In this post, I touched on the interpretation of a couple of results from estimation and postestimation from xtabond that will help you understand your output.Contains data from /Applications/Stata/ado/base/c/census.dta This is what the xtdpd command allows us to do, but it is beyond the scope of this post.ĭynamic panel-data models provide a useful research framework. Essentially, we would have to fit a different dynamic model. If this were not the case, we would have to look for different instruments. There is evidence that the Arellano–Bond model assumptions are satisfied. We reject no autocorrelation of order 1 and cannot reject no autocorrelation of order 2. For the example above,Īrellano-Bond test for zero autocorrelation in first-differenced errors
Stata 12 se maximum variables serial#
Another way of saying this is that the differenced time-varying unobserved component is serially correlated with an order greater than 1.Įstat abond provides a test for the serial correlation structure. Here a bit of math will help us understand what is going on. This is followed by a footnote that refers to GMM and standard-type instruments. Another noteworthy aspect that appears in the table is the mention of 39 instruments in the header. Stata includes the value of the dependent variable in the previous period for us. 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. The output includes a coefficient for the lagged value of the dependent variable that we did not specify in the command. 4224403Ī couple of elements in the output table are different from what one would expect. Number of instruments = 39 Wald chi2(3) = 3113.63 Group variable: id Number of groups = 1,000 xtabond income married educ, vce(robust)Īrellano-Bond dynamic panel-data estimation Number of obs = 8,000 Below, we fit an Arellano–Bond model using xtabond. The outcome of interest is income ( income), and the explanatory variables are years of schooling ( educ) and an indicator for marital status ( married). We have fictional data for 1,000 people from 1991 to 2000. Today I will provide information that will help you interpret the estimation and postestimation results from Stata’s Arellano–Bond estimator xtabond, the most common linear dynamic panel-data estimator. 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. Random-effects and fixed-effects panel-data models do not allow me to use observable information of previous periods in my model.