Bootstrap Standard Error Stata
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Standard Error Stata Output
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Bootstrap Standard Error Estimates For Linear Regression
Stata Blog Stata Press Stata Journal Advanced search Site index Purchase Products Training Support Company >> Home >> Resources & support >> FAQs >> Bootstrap with panel data How do I obtain bootstrapped standard errors with panel data? Title Bootstrap with panel data Author Gustavo Sanchez, StataCorp In general, the bootstrap is used in statistics as a resampling method to approximate standard errors, confidence intervals, and p-values for test statistics, based on the sample data. This method is significantly helpful when the theoretical distribution of the test statistic is unknown. In Stata, you can use the bootstrap command or the vce(bootstrap) option (available for many estimation commands) to bootstrap the standard errors of the parameter estimates. We recommend using the vce() option whenever possible because it already accounts for the specific characteristics of the data. This adjustment is particularly relevant for panel data where the randomly sele
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Bootstrap Standard Error Heteroskedasticity
Proceedings Email alerts Statalist The Stata Blog Web resources Author Support Program Installation Qualification Tool Disciplines Company StataCorp Contact us Hours of operation Announcements Customer service Register Stata online Change registration Change address Subscribe http://www.stata.com/support/faqs/statistics/bootstrap-with-panel-data/ to Stata News Subscribe to email alerts International resellers Careers Our sites Statalist The Stata Blog Stata Press Stata Journal Advanced search Site index Purchase Products Training Support Company >> Home >> Resources & support >> FAQs >> Guidelines for bootstrap samples The following question and answer is based on an exchange that started on Statalist. How large should the bootstrapped samples be relative to the http://www.stata.com/support/faqs/statistics/bootstrapped-samples-guidelines/ total number of cases in the dataset? Title Guidelines for bootstrap samples Author William Gould, StataCorpJeff Pitblado, StataCorp Note: This FAQ has been updated for Stata 14. bootstrap is based on random draws, so results are different from previous versions because of the new 64-bit Mersenne Twister pseudorandom numbers. Question: I am running a negative binomial regression on a sample of 488 firms. For various reasons [...], I decided to use the bootstrapping procedure in Stata on my data. Are there general guidelines that have been proposed for how large the bootstrapped samples should be relative to the total number of cases in the dataset from which they are drawn? Answer: When using the bootstrap to estimate standard errors and to construct confidence intervals, the original sample size should be used. Consider a simple example where we wish to bootstrap the coefficient on foreign from a regression of weight and foreign on mpg from the automobile data. The sample size is 74, but suppose we draw only 37 observations (half of the observed sample size) each time we resample the data 2,000 times. . sysuse auto, clear . set seed 3957574 . bootstrap _b[foreign], size(37) reps(2000) dots: regress mpg weight foreign (running regress on estimation
about any statistic you can calculate. The results of almost all Stata commands can be bootstrapped immediately, and it's relatively straightforward to put any other results you've calculated in a form that can be bootstrapped. This article will show you how. If you're just looking https://www.ssc.wisc.edu/sscc/pubs/4-27.htm to bootstrap the results of a Stata command, all you'll need is a basic familiarity with Stata. However, if you need to calculate something else and then bootstrap it you'll need to write an official Stata program to do so. If you're not familiar with writing Stata programs (which are not the same as do files) you'll want to take a look at Programming in Stata, in particular the section on programs. Bootstrapping Results from Stata standard error Commands If there is a single Stata command that calculates the result you need, you can simply tell Stata to bootstrap the result of that command. As an example, load the automobile data that comes with Stata and consider trying to find the mean of the mpg variable. The summarize (sum) command will do exactly what you want: sysuse auto sum mpg But how will the bootstrap command find the number it needs in all that bootstrap standard error output? The answer is that you will tell it where to look in the return vector. The Return Vector In addition to the output you see on the screen or in your log, all Stata commands quietly put their results in a return vector. You can refer to this vector in subsequent commands, or in the case of bootstrap you can tell it what part of the return vector you care about. To see the current tables of the return vector, type return list The sum command is a basic command (as opposed to an estimation command) so its return vector is called r(). Looking over the list, you'll see that r(mean) is the number you want. You're now ready to actually carry out the bootstrap. The bootstrap Command Syntax The basic syntax for a bootstrap command is simple: bootstrap var=r(result): command Here var is simply what you want to call the quantity you're bootstrapping. You're welcome to choose any name you like as long as it meets the usual rules for a Stata variable name. In our case meanMPG would be appropriate. r(result)tells the bootstrap command to look in the r() vector for the particular result you're interested in. We're interested in r(mean). Finally command should be replaced by the actual command that calculates the result you want. In our case it's sum mpg. Putting this
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