UNIVERSITY OF CALIFORNIA, LOS ANGELES
Department of Economics

Economics 143 (Cameron) - Applied Regression Analysis

Classroom Handout #11: Comparing LIMDEP output to SHAZAM


Here is a LIMDEP program to compute descriptive statistics and run a simple regression using the same data set that is employed by SHAZAM in a previous handout.


read; nvar=2
    ; nobs=14
    ; names=income,expend
    ; file=mpc.dat $
 
dstats; rhs=* $
? LIMDEP does not provide an automatic constant term so we have to create one
create; one=1 $
regress; lhs=expend; rhs=one,income $

Here is what these LIMDEP commands produce for you:

 1
 +============================================================================+
 : LIMDEP Estimation Results                      Run log line    2  Page   1 :
 : Current sample contains      14 observations.                              :
 +============================================================================+

                              Descriptive Statistics
 Variable   Mean      Std. Dev.   Skew. Kurt.  Minimum     Maximum      Cases
 ----------------------------------------------------------------------------
 INCOME      155.0000     41.2777   0.2   1.8    100.0000    220.0000      14
 EXPEND      125.5714     35.2239   0.2   1.6     80.0000    180.0000      14
 1
 +============================================================================+
 : LIMDEP Estimation Results                      Run log line    5  Page   2 :
 : Current sample contains      14 observations.                              :
 +============================================================================+

 +-----------------------------------------------------------------------+
 | Ordinary    least squares regression    Weighting variable = ONE      |
 | Dependent variable is EXPEND    Mean =  125.57143, S.D. =     35.2239 |
 | Model size: Observations =      14, Parameters =   2, Deg.Fr.=     12 |
 | Residuals:  Sum of squares =    4643.9884, Std.Dev. =        19.67229 |
 | Fit:        R-squared = 0.71208, Adjusted R-squared =         0.68809 |
 | Model test: F[  1,     12] =   29.68,    Prob value =         0.00015 |
 | Diagnostic: Log-L =    -60.4950, Restricted(b=0) Log-L =     -69.2105 |
 |             Amemiya Pr. Crt.=  442.285, Akaike Info. Crt.=      8.928 |
 | Autocorrel: Durbin-Watson Statistic =   1.88173,   Rho =      0.05913 |
 +-----------------------------------------------------------------------+

   Variable  Coefficient   Standard Error  t-ratio  P[|T|>t]   Mean of X
   ---------------------------------------------------------------------
   Constant    13.957          21.152        0.660   0.52180
   INCOME     0.72009         0.13218        5.448   0.00015   155.0



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Updated: February 2, 1998
Prepared by: Trudy Ann Cameron