Efficiency Measurement
William Greene
Stern School of Business
New York University
Current Versions
Executing
the Lab
Scripts
Lab Session 1
Introduction to
Frontier Modeling
with
LIMDEP/NLOGIT
Lab Session 1
Operating LIMDEP
 Basic Commands - Transformations
 Linear Regression/Panel Data Application:
Panel data on Spanish Dairy Farms
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Estimating the linear model
Testing a hypothesis
Examining residuals
Desktop
Entering Data for Analysis
IMPORT: ASCII, Excel Spreadsheets,
other formats
.txt, .csv, .txt
 READ: other programs
.dta (stata), .xls (excel)
 LOAD existing data sets in the form of
LIMDEP/NLOGIT ‘Project Files’ – SAVED
from earlier sessions or data
preparations
.lpj (nlogit, limdep, Stat Transfer)
 Internal data editor

Sample data set: dairy.lpj
Panel Data on Spanish Dairy Farms
 Use for a Production Function Study
 Raw: Milk,Cows,Land, Labor, Feed
 Transformed
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yit = log(Milk)
x1, x2, x3, x4 = logs of inputs
x11 = .5*x12, x12 = x1*x2, etc.
year93 = dummy variable for year,…
Data on Spanish Dairy Farms
N = 247 farms, T = 6 years (1993-1998)
Input
Units
Mean
Std.
Dev.
Minimum Maximum
Milk
Milk production (liters)
131,108
92,539
14,110
Cows
# of milking cows
2.12
11.27
4.5
82.3
Labor
# man-equivalent units
1.67
0.55
1.0
4.0
Land
Hectares
of
land
devoted to pasture and
crops.
12.99
6.17
2.0
45.1
Feed
Total
amount
of
feedstuffs fed to dairy
cows (tons)
57,941
47,981
3,924.1
4
376,732
727,281
Locate file Dairy.lpj
Project Window
Project window displays the data
set currently being analyzed:
Variables
Matrices
Other program related results
Instructing LIMDEP to do something

Menus – available but we
will generally not use them

Command language –
entered in an editor then
‘submitted’ to the program
Use File:New/OK for an Editing Window
Text Editing Window
Commands will be
entered in this
window and
submitted from here
Typing Commands in the Editor
When you open a .lim file, it creates a new editing window
for you. Submit the existing commands, modify them then
submit, or type new commands in the same window.
“Submitting” Commands

One line command

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
Place cursor on that line
Press “Go” button
More than one command or
command on more than one line

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Highlight all lines (like any text editor)
Press “Go” button
The GO Button
There is a STOP button also. You can use it to interrupt iterations
that seem to be going nowhere. It is red (active) during iterations.
Where Do Results Go?

On the screen in a third window that
is opened automatically

In a text file if you request it.

To an Excel CSV file if you EXPORT
them

Internally to matrices, variables, etc.
Standard Three Window Operation
Project
window
shows
variables
in the
data set
Commands
typed in
editing
window
Results
appear in
output
window
Command Structure

VERB ; instruction ; … ; … $
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Verb must be present
Semicolons always separate subcommands
ALL commands end with $
Case never matters in commands
 Spaces are always ignored
 Use as many lines as desired, but
commands must begin on a new line

Important Commands:

CREATE ; Variable = transformation $
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SAMPLE ; first
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Create ; LogMilk = Log(Milk) $
Create ; LMC = .5*Log(Milk)*Log(Cosw) $
Create ; … any algebraic transformation $
-
last
Sample ; 1 – 1000 $
Sample ; All $
REJECT ; condition

Reject ; Cows < 20 $
$
Model Command

Model ; Lhs = dependent variable
; Rhs = list of independent variables $
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Regress ; Lhs=Milk ; Rhs=ONE,Feed,Labor,Land $
ONE requests the constant term - mandatory
Typically many optional variations
Models are REGRESS, FRONTIER, PROBIT,
POISSON, LOGIT, TOBIT, … and over 100
others. All have the same form.
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Variants of models such as Poisson / NegBinomial
Several hundred different models altogether
Name Conventions
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CREATE ; Name = any function desired $
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Name is the name of a new variable
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No more than 8 characters in a name
The first character must be a letter
May not contain -,+,*,/.
Use letters A – Z, digits 0 – 9 and _
May contain _.
Two Useful Features
NAMELIST ; listname = a group of names $
Listname is any new name.
After the command, it is a synonym for the list
NAMELIST ; CobbDgls=One,LogK,LogL $
REGRESS ;Lhs = LogY ; Rhs = CobbDgls $
*
= All names
DSTAT ; RHS = * $
REGRESS ; Lhs = Q ; Rhs = One, LOG* $
A Useful Tool - Calculator
CALC ; List ; any expression $
CALC ; List ; 1 + 1 $
CALC ; List ; FTB ( .95,3,1482) $
(Critical point from F table)
CALC ; List ; Name = any expression $
Saves result with name so it can be
used later.
CALC ; Chisq=2*(LogL – Logl0) $
;LIST may be omitted. Then result is
computed but not displayed
Matrix Algebra
Large package; integrated into the program.
NAMELIST ; X = One,X1,X2,X3,X4 $
MATRIX
; bols = <X’X> * X’y $
CREATE
; e = y – X’bols $
CALC
; s2 = e’e / (N – Col(X)) $
MATRIX
; Vols =s2 * <X’X> ;Stat(bols,Vols,X) $
Over 100 matrix functions and all of matrix algebra
are supported. Use with CREATE, CALC, and model
estimators.
Regression Results
Model estimates on screen in the output
window
 Matrices B and VARB
 Scalar results
 New Variables if requested, e.g., residuals
 Retrievable table of regression results

Results on the Screen in the Output Window
Matrices B and
VARB. Double
click names to
open windows.
Use B and VARB
in other MATRIX
computations and
commands.
Scalar results
from a
regression can
also be used in
later
computations
Regression Analysis: Testing
Cobb-Douglas vs. Translog
NAMELIST ; cobbdgls = one,x1,x2,x3,x4 $
NAMELIST ; quadrtic =x11,x22,x33,x44,x12,x13,x14,x23,x24,x34 $
NAMELIST ; translog = cobbdgls,quadrtic $
DSTAT
; Rhs=*$
REGRESS ; Lhs = yit ; Rhs = cobbdgls $
CALC
; loglcd = logl ; rsqcd = rsqrd $
REGRESS ; Lhs = yit ; Rhs= translog $
CALC
; logltl = logl ; rsqtl = rsqrd $
CALC
; dfn = Col(translog) – Col(cobbdgls) $
CALC
; dfd = n – Col(translog) $
CALC
; list ; f=((rsqtl – rsqcd)/dfn) / ((1 - rsqtl)/dfd)$
CALC
; list ; cf = ftb(.95,dfn,dfd) $
CALC
; list ; chisq = 2*(logltl – loglcd) $
CALC
; list ; cc = Ctb(.95,dfn) $
Built in F and Chi squared tests
REGRESS ; Lhs = yit ; Rhs = translog ; test: quadrtic $
Exiting the Program
Save Your Work When You Exit
Lab Exercises with Dairy Farm Data
Fit a linear regression with robust
covariance matrix
 Fit the linear model using least absolute
deviations and quantile regression
 Test for time effects in the model
 Use a Wald test for the translog model
 Test for constant returns to scale
 Analyze residuals for nonnormality
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