Rolling regressions, beta, t-statistics, and SE in Stata
asreg can easily estimate rolling regressions, betas, t-statistics and SE in Stata. To understand the syntax and basic use of asreg, you can watch this Youtube video. In this post, I show how to use asreg for reporting standard errors, fitted values, and t-statistics in a rolling window. To install asreg, type the following on [...]
Publication quality regression tables with asdoc in Stata – video example
Creating publication-quality tables in Stata with asdoc is as simple as adding asdoc to Stata commands as a prefix. asdoc can create two types of regression tables. The first type (call it detailed) is the detailed table that combines key statistics from the Stata's regression output with some additional statistics such as mean and standard deviation [...]
How to export high-quality table of correlations from Stata to MS Word
For creating a high-quality publication-ready table of correlations from Stata output, we need to install asdoc program from SSC first. ssc install asdoc, update Once the installation is complete, we shall add the word asdoc to the cor command of Stata. Since we estimate correlations among all numeric variables of a dataset by typing cor in [...]
How to use asdoc : a basic example
Using asdoc is pretty easy. You need to add just asdoc as a prefix to Stata commands. For example, we use the sum command to find summary statistics of all numeric variables in the dataset. We shall add just asdoc as a prefix to sum. Let us load the auto.dta set for practice and find summary [...]
asdoc : Sends Stata output to MS Word
About asdoc asdoc is a Stata program that makes it super-easy to send output from Stata to MS Word. asdoc creates high quality, publication-ready tables from various Stata commands such as summarize , correlate , tabstat , tabulate (cross-tabs), regress (regressions), ttest , table , and many more. [...]
Fama and MacBeth (1973) Fastest regression in Stata
The Fama-McBeth (1973) regression is a two-step procedure . The first step involves estimation of N cross-sectional regressions and the second step involves T time-series averages of the coefficients of the N-cross-sectional regressions. The standard errors are adjusted for cross-sectional dependence. This is generally an acceptable solution when there is a large number of cross-sectional [...]