Getting p-values and t-values with asreg

Getting p-values and t-values with asreg

Category:Blog,Stata Programs

Xi asked the following quesiton:

How can I get p-values and t-values using asreg program?

Introduction to asreg

asreg is a Stata program, written by Dr. Attaullah Shah. The program is available for free and can be downloaded from SSC by typing the following on the Stata command window:

ssc install asreg

asreg was primarily written for rolling/moving / sliding window regressions. However, with the passage of time, several useful ideas were conceived by its creator and users. Therefore, more features were added to this program. 

Getting t-values after asreg

Consider the following example where we use the grunfeld dataset from the Stata web server. The dataset has 20 companies and 20 years of data for each company. We shall estimate the following regression model where the dependent variable is invest and independent variables are mvalue and kstock. Let’s estimate the regression model separately for each company, denoted by i .

In the following lines of code, the letters se after comma causes asreg to report the standard errors for each regression coefficient.

webuse grunfeld, clear
tsset
bys company: asreg invest mvalue kstock, se

asreg generates the regression coefficients, r-squared, adjusted r-squared, number of observations (_Nobs) and standard errors for each coefficient. This is enough information for producing t-values and p-values for the regression coefficients. The t-values can be generated by:

 
 gen t_values_Cons    =   _b_cons / _se_cons 
 gen t_values_mvalue =  _b_mvalue  /  _se_mvalue  
 gen t_values_kstock =  _b_kstock  /  _se_kstock 

Getting p-values after asreg

Getting p-values is just one step away. We need one additional bit of information from the regression estimates, that is the degrees of freedom. This is usually equal to the number of observations minus the number of parameters being estimated. Since we have two independent variables and one constant, the number of parameters being estimated are 3. asreg returns the number of observation in the variable _Nobs. Therefore, the term _Nobs – 3 in the following lines of code is a way to get the degrees of freedom.

 gen p_values_Cons = (2 * ttail(_Nobs-3), abs( _b_cons / _se_cons ))
 gen p_values_mvalue = (2 * ttail(_Nobs-3), abs( _b_mvalue / _se_mvalue ))
 gen p_values_kstock = (2 * ttail(_Nobs-3), abs( _b_kstock / _se_kstock ))

Verify the Results

Let’s estimate a regression for the first company and compare our estimates with those produced by the Stata regress command.

 reg invest mvalue kstock if company == 1
 list _b_cons  _se_cons p_values_Cons in 1

Full Code

 webuse grunfeld, clear
 tsset
 bys company: asreg invest mvalue kstock, se
 gen t_values_Cons   =   _b_cons / _se_cons 
 gen t_values_mvalue =  _b_mvalue  /  _se_mvalue  
 gen t_values_kstock =  _b_kstock  /  _se_kstock  
 gen p_values_Cons = (2 * ttail(_Nobs-3), abs( _b_cons / _se_cons ))
 gen p_values_mvalue = (2 * ttail(_Nobs-3), abs( _b_mvalue / _se_mvalue ))
 gen p_values_kstock = (2 * ttail(_Nobs-3), abs( _b_kstock / _se_kstock ))
 reg invest mvalue kstock if company == 1
 list _b_cons  _se_cons t_values_Cons p_values_Cons in 1