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Author Archives: Attaullah Shah

  • 2

Export output of Table command from Stata to Word using asdoc

Category:Uncategorized

Exporting tables from table command was the most challenging part in asdoc programming. Nevertheless, asdoc does a pretty good job in exporting table from table command. asdoc accepts almost all options with table command, except cellwidth(#), stubwidth(#), and csepwidth(#).

 

7.1 One-way table

Example 54 : One-way table; frequencies shown by default

sysuse auto, clear
asdoc table rep78, title(Table of Freq. for Repairs) replace

 

Example 55 : One-way table; show count of non-missing observations for mpg}

asdoc table rep78, contents(n mpg) replace

Example 56 : One-way table; multiple statistics on mpg requested

asdoc table rep78, c(n mpg mean mpg sd mpg median mpg) replace

 

Example 57 : Add formatting – 2 decimals

asdoc table rep78, c(n mpg mean mpg sd mpg median mpg) dec(2) replace

 

7.2 Two-way table

Example 58 : Two-way table; frequencies shown by default

asdoc table rep78 foreign, replace

 

Example 59 : Two-way table; show means of mpg for each cell

asdoc table rep78 foreign, c(mean mpg) replace

 

Example 60 : Add formatting

asdoc table rep78 foreign, c(mean mpg) dec(2) center replace

 

Example 61 : Add row and column totals

asdoc table rep78 foreign, c(mean mpg) dec(2) center row col replace

 

7.3 Three-way table

Example 62 : Three-way table

webuse byssin, clear
asdoc table workplace smokes race [fw=pop], c(mean prob) replace

7.4 Four-way table

Example 65 : Four-way table with by()

webuse byssin1, clear
asdoc table workplace smokes race [fw=pop], by(sex) c(mean prob) replace

 

Example 66 : Four-way table with supercolumn, row, and column totals

asdoc table workplace smokes race [fw=pop], by(sex) c(mean prob) sc col row replace

  • 0

Customized tables using option row() of asdoc – Stata

Category:Uncategorized

This is rather a quick example of how to use option row() of asdoc for creating highly customized tables. We are interested in a table that is given bellow.

* Load example dataset
sysuse auto,clear

* Write the header row of the table with table title
asdoc, row(Dependent variable:domestic or foreign, Domestic mean/frequency, Domestic SD, Foreign mean/frequency, Foreign SD, t-test) title(Summary staticis) save(myfile) replace

* Add the second row : \i, adds an empty cell
asdoc, row( Model independent variables, \i, \i, \i, \i, \i) append

* Use a loop over each variable that include price, mpg, ...
foreach var of varlist price mpg rep78 headroom trunk weight length turn{
 
  * First summarize each variable for a given sample, that is if foregin is   zero
  qui sum `var' if foreign==0

  * Obtain the mean divided by frequency
  local mf=`r(mean)'/`r(N)'

   * Store the mf and standard deviation variable in accum macro
  asdoc, accum(`mf', `r(sd)')

* now repeat the same for the second sample, ie. when foreing is 1
  qui sum `var' if foreign==1
  local mf=`r(mean)'/`r(N)'
  asdoc, accum(`mf', `r(sd)')

* Conduct a two sample ttest using foregin as a grouping variable
  ttest `var', by(foreign)

* Obtain the t-statistics
  local t : di %9.3f = abs(`r(t)')

* Create significance stars
  if `r(p)'<=0.01 {
    local star "***" 
  } 
  else if `r(p)'<=0.05{ 
    local star "**" 
  } 
  else if `r(p)'<=0.1{
   local star "*"
  } 
  else {
   local star " " 
  } 
  local tstar `t'`star' 

* Add the t-value and stars to the accum macro
  asdoc, accum(`tstar') 

* Finally write this complete row where we first write the variable name
* and then all accumulated variables that are present in $accum macro.
asdoc, row(`var', $accum) 
}

  • 7

tabstat with asdoc in Stata

Category:asdoc,Uncategorized

asdoc makes some elegant tables when used with tabstat command. There are several custom-made routines in asdoc that creates clean tables from tabstat command. asdoc fully supports the command structure and options of tabstat. And, yes asdoc allows one additional statistics, that is, t-statistics alongside the allowed statistics in tabstat. For reporting purposes, asdoc categorizes tabstat commands in two groups:

(1) stats without a grouping variable

(2) stats over a grouping variable.

 

Tabstat Without-by

If statistics are less than variables, the table is transposed, i.e. statistics are shown in columns, while variables are shown in rows

 

Example 49 : One variable, many stats, including t-statistics

sysuse auto, clear  
asdoc tabstat price , stat(min max mean sd median p1 p99 tstat) replace 

 

Example 50 : Many variables, one statistic

asdoc tabstat price mpg rep78 headroom trunk weight length foreign , stat( mean) replace

 

Example 51 : Many variables, many statistics

asdoc tabstat price mpg rep78 headroom trunk weight length foreign , /// 
stat( max mean sd median p1 p99 tstat) replace

 

Tabstat with-by

 

Example 52 :


bysort foreign: asdoc tabstat price mpg rep78 headroom trunk weight length, stat(mean) replace


OR

asdoc tabstat price mpg rep78 headroom trunk weight length, ///
stat(mean) by(foreign) replace

 

Example 53 : By with many variables and many statistics

bysort foreign: asdoc tabstat price mpg rep78 headroom trunk weight length, ///
stat(mean sd p1 p99 tstat) replace

 



  • 7

Quick setup of Python with Stata 16

Category:Blog,Stata Programs Tags : 


With the announcement of Stata 16, Python commands can be executed directly from the Stata command prompt, do files or ado programs. That would definitely expand the possibilities of doing extraordinary things without leaving the Stata environment. However, this integration exposes Stata to all the problems of Python installations and its packages.

First of all, Python does not come as part of the Stata installation. Stata depends on the already installed version of Python. That would definitely make a Stata-Python code less portable. One solution might be the portable version of Python. Only time can tell what will work best in such situations.

In this short post, I am going to outline a few basic steps to get started with Python from Stata. These steps are mentioned below:


1.What Version of Python to Install

A number of options are available to install Python. Over the past 12 months, I found that the installation of Python using Anaconda is the least problematic one. And with Stata 16, this again came out true. The stand-alone version of Python did not work with Stata. Each time I tried to type python from the Stata command prompt, the error message generated by Stata was:

initialized          no
r(7100);

What I did was to uninstall the other version of Python and kept only the Anaconda installation.


2. Set the Installation path

Stata can search for any available Python installation, including the installation through Anaconda. To search and associate python with Stata, I typed the following from the Stata command prompt:

python search 
set python_exec  D:\Anaconda\python.exe, permanently

The first line of code finds the directory path and the Python executable file. The second line of code sets which Python version to use. Option permanently would save this path for future use as well. And that’s all.


3. Using Python

Once the above steps go without an error, we are ready to use Python. In the Stata command window, we can enter the Python environment by typing python, and the three greater than familiar symbol will appear on the screen

 . python
 --------- python (type end to exit) ------- 
>>>2+2
       4
 >>>end 
-------------------------------------------



  • 2

Fama and MacBeth regression with Shanken correction using asreg

Category:Uncategorized

If you are not yet familiar with asreg, here is a quick start. Implementing the Fama and MacBeth regression using asreg is super-fast and easy. Here are a few posts related to this implementation.

FMB regressions with asreg

FMB regression – what, how and where

FMB regressions with 25-portfolios – An example


The Shanken Correction

In applying standard OLS formulas to a cross-sectional regression, we assume that the right-hand variables β are fixed. The β in the cross-sectional regressions are not fixed, of course, but are estimated in the time-series regression.  Therefore, there might be a sampling error in the estimates of β.  Shanken (1992) suggested a correction to the standard errors of the estimates.


How to do it?

The focus in this post is on the Fama and MacBeth implementation with Shanken () correction. Like with many other commands using asreg, the Shanken correction is fairly easy. The following steps are needed:


1. Find a covariance matrix among the right hand-side variables and write it to a matrix. Suppose variables inour dataset inlcude rm_rf smb and hml, then to find the covariance and write it to a matrix, we would do the following:

cor rm_rf smb hml, cov
matrix S = r(C)  


2. Find the first stage lambda of the RHS variables.

bys portfolios: asreg excess_returns rm_rf smb hml
* Remove uncessary variables
 drop _Nobs _R2 _adjR2 _b_cons 


3. Fama and MacBeth regression: In this last stage, we would use the fmb and shanken option. The shanken option requires the covariance matrix that we created in step 1 above

asreg excess_returns _b_mmrf _b_smb _b_hml , fmb shanken(S)

 
 

Pricing

The asreg program is a freeware and can be downloaded from SSC. The Shanken correction is available for $100/model, plus a $50 for raw data processing (in case the data is not in Stata format and variables are not already constructed). For further details, please contact us at:

 

  attaullah.shah@imsciences.edu.pk
  Stata.Professor@gmail.com

 

See our full list of completed projects


References

  1. Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of political economy81(3), 607-636.
  2. Shanken, J. (1992). On the estimation of beta-pricing models. The review of financial studies5(1), 1-33.



  • 0

Fama – MacBeth (1973) procedure: What, how and where | asreg in Stata

Category:Uncategorized

Fama and MacBeth (1973) procedure can be used in testing asset pricing models and in other areas. In this post, my primary focus is on its use in testing asset pricing models.


FMB in asset pricing models

It is actually a three-step process. We would divide the time period into three parts.

1. The first step is to find the assets/portfolios betas in the first period. Some researchers would use these betas to classify assets into portfolios.

2. The second step is to find betas of these portfolios in the second period.

3. The third step is to find the portfolio returns in the third period and test whether the betas from the second period can explain these returns? This step involves:
(i) cross-sectional regressions of the portfolio returns on the portfolio betas in each period.
(ii) averaging coefficients from the cross-sectional regressions across time. The standard errors are adjusted for cross-sectional dependence.


What does asreg do in the above process


asreg with fmb option performs step 3(i) and 3(ii). 

asreg can also help in step (1) where individual betas need to be calculated for each stock. The command might look like
Code:

bys company: asreg returns market_returns if period == 1

This means that for typical asset pricing tests, the researcher has to do step (1) and (2) and arrange the data in a panel format, listing portfolio returns and betas as variables in columns. And then use asreg with fmb option, e.g.

keep if period == 3
xtset company month
asreg returns betas, fmb


Where else FMB regression can be used?

Fama and MacBeth (1973) procedure (i.e step 3(i) and (ii)) is also used in areas other than testing the asset pricing models. You can see one example in my paper, Table 3, column 8, page 264

Shah, Attaullah & Shah, Hamid Ali & Smith, Jason M. & Labianca, Giuseppe (Joe), 2017. “Judicial efficiency and capital structure: An international study,” Journal of Corporate Finance, Elsevier, vol. 44(C), pages 255-274.




  • 13

Export correlation table to Word with stars and significance level using asdoc

Category:Uncategorized

The updated version of asdoc can now create a table of correlation with significance levels starred at different levels. The new version can be installed by typing the following line in Stata.


Installation of the new version

net install asdoc, from(http://fintechprofessor.com) replace


An Example

sysuse auto, clear
asdoc pwcorr price mpg rep78 headroom trunk weight length turn , star(all) replace nonum


Explanation

Just like with any other Stata command, we would write asdoc as a prefix to the Stata command. In this case, the Stata command is pwcorr which is followed by the variable names. After the comma, we added option nonum, star(all) and replace. These are explained bellow:

star(all) = This option is used to report stars to signfy significance at different levels. These are: ***

  1. *** to show significance at 1% or bellow
  2. ** to show significance at 5% or bellow
  3. * to show significance at 10% or bellow

nonum = Without using this option, asdoc will report numeric numbers as column headers

replace = This option replaces any existing file

You would be interested in this blog entry where I show several useful options of asdoc that can be used with correlation tables.




  • 3

asdoc abbreviates / truncates my variable names and labels | Word to Stata

Category:asdoc

Stephen Okiya has asked the following question

I notice that the variable names are truncated in spite of using the option abb(100). Do you know why this is the case?


Answer:

asdoc uses the abbrev() function of Mata. For some reasons, the abbrev() function splits the following sentence in half, no matter which value we set for the abbreviation.

loc vari " Child's age when she/he was first fed something other than breast milk"

. dis abbrev("`vari'", 32)
 Child's age when she/he was firs

 . dis abbrev("`vari'", 100)
 Child's age when she/he was firs

However, we set the second argument of abbrev() function to missing, then the full sentence is show

. dis abbrev("`vari'", .)

Child's age when she/he was first fed something other than breast milk

Therefore, if we prefer not to abbreviate any name or label, just provide missing value for the abb() option of asdoc. So the following will show all the text

asdoc sum Q85, label abb(.)




  • 10

Fama and MacBeth regression over 25 Portfolios using asreg in Stata

Category:Uncategorized

Antonio has asked the following question

Dear Sir,
I was wondering how to run a Fama and MacBeth regression over 25 Portfolios. In accordance with your code, the first variable needs to be the dependent variable while the following variables are considered as independent variables. Basically, I would like to calculate the risk premium of a factor over the 25 value and size-sorted portfolios. Therefore in my case, I would have more dependent variables and just one dependent variable.
Thanks for your availability


Answer

To answer your question, I have preareed a dummy dataset, which you can download by typing the following in Stata command window.

use http://fintechprofessor.com/ff.dta, clear

So before running the Fama and MacBeth regressions, this is how the data needs to be structured.

The data is in a long format where the portfolios are tracked by a variable, called the panelvar. The portfolio returns are written in a separate variable, in our case, it is named as returns. The panelvar has values from 1, up to 25. The first 10 observations of the portfolios 1 and 2 look like:

. list in 1/10, noob
   +---------------------------------------------+
   |    mofd   P   returns       size        MTB |
   |---------------------------------------------|
   |  1993m6   1      .038    64.0125   5.224508 |
   |  1993m7   1     .0539   71.86839   4.505145 |
   |  1993m8   1    -.0639   27.82528   1.888283 |
   |  1993m9   1    -.0328   20.08383   7.730755 |
   | 1993m10   1     .0249   59.34985   8.961844 |
   |---------------------------------------------|
   | 1993m11   1     .0657   47.42625   3.766557 |
   | 1993m12   1     .0408   81.47429   5.148165 |
   |  1994m1   1     .0185   42.39914   5.375627 |
   |  1994m2   1     .0323   62.36839   4.882884 |
   |  1994m3   1   -.00598   64.79323   1.281697 |
   +---------------------------------------------+
 . list in 101/110, noob
   +---------------------------------------------+
   |    mofd   P   returns       size        MTB |
   |---------------------------------------------|
   |  1993m6   2     .0114   41.16883   4.549813 |
   |  1993m7   2    -.0158   10.09915   2.136258 |
   |  1993m8   2    .00616   73.43023   2.924793 |
   |  1993m9   2    -.0141   58.28651   7.608449 |
   | 1993m10   2     .0129    63.4972   1.137969 |
   |---------------------------------------------|
   | 1993m11   2    -.0223    16.1786   1.368057 |
   | 1993m12   2     .0322   64.10929   6.226629 |
   |  1994m1   2    -.0144   54.48264   7.883276 |
   |  1994m2   2     .0388   74.99379   1.362888 |
   |  1994m3   2     .0345   68.66164   7.102628 |
   +---------------------------------------------+



How to run the Fama and MacBeth regression

My asreg command is available on SSC, to download it, type:

ssc install asreg, replace

asreg can estimate three types of regressions: (1) cross-sectional regressions (2) rolling window regressions and (3) Fama and MacBeth regressions. You can read more details here.

Since our main focus here is on the Fama and MacBeth procedure, the discussion this point onwards will use option fmb of the asreg program. The syntax is:

asreg depvar indepvars, fmb

The data must be first declared as panel data with the xtset command. In our dataset, we have P as the panel variable and mofd as the time variable, therefore, to declare the data as panel data, the xtset command would be:

xtset P mofd

In our dataset, we have the variable returns as the dependent variable and size and MTB as the two independent variables. The command for the Fama and MacBeth regression would be:

. asreg returns  size MTB , fmb


Explanation

retunrs = The dependent variable

size and MTB = independent variables


  • 1

asdoc Unicode issue | Stata to MS Word

Category:Uncategorized

For those who are not yet familiar with asdoc, asdoc can be downloaded from SSC and can be used with almost all Stata commands. Here is a short blog post that shows how asdoc can be used with any Stata command. You can also watch several YouTube videos that show the use of asdoc


asdoc installation

 ssc install asdoc


The problem

Since asdoc uses RTF file format, any Unicode character passed from Stata to the RTF file will be incorrectly shown in the output file. For example, a variable has the following value label which is correctly shown on Stata screen, but when written with asdoc, it is distorted.

Stata view: Don’t know
asdoc view: 

The solution

If you have Stata 14 or higher, then the good news is that you can use unicode command to change the file encoding. Specifically, the subcommand convertfile of unicode command can be extremely useful here. In the following example, I shall download an example dataset, called unicode.dta from my site, and then tabulate the variable M25 with asdoc, write the results to Myfile.doc and convert the coding and write the results to a new file, Myfile_new.doc

use http://fintechprofessor.com/unicode.dta, clear
asdoc tab M25, replace

The file generated by asdoc looks like this, the last line of the file highlights the given issue.

Now lets try the unicode solution.

unicode convertfile  Myfile.doc  Myfile_new.doc, dstencoding(Windows-1252) replace