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William Greene Stern School of Business New York University. Frontier Models and Efficiency Measurement Lab Session 1. 0Introduction 1 Efficiency Measurement 2 Frontier Functions 3 Stochastic Frontiers 4 Production and Cost 5 Heterogeneity 6 Model Extensions 7 Panel Data - PowerPoint PPT Presentation
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Frontier Models and Efficiency MeasurementLab Session 1
William Greene
Stern School of Business
New York University
0 Introduction1 Efficiency Measurement2 Frontier Functions3 Stochastic Frontiers4 Production and Cost5 Heterogeneity6 Model Extensions7 Panel Data8 Applications
Executing the Lab Scripts
Frontier Models and Efficiency Measurement
Lab Session 1: Operating NLOGIT
William Greene
Stern School of Business
New York University
0 Introduction1 Efficiency Measurement2 Frontier Functions3 Stochastic Frontiers4 Production and Cost5 Heterogeneity6 Model Extensions7 Panel Data8 Applications
Lab Session 1
Operating NLOGIT Basic Commands - Transformations Linear Regression/Panel Data Application:
Panel data on Spanish Dairy Farms 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
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
Input Units Mean Std. Dev.
Minimum
Maximum
Milk Milk production (liters)
131,108 92,539 14,110 727,281
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 3,924.14
376,732
N = 247 farms, T = 6 years (1993-1998)
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
Spacing and capitalization never matter. Just type instructions so they are easily readable and contain the right information.
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 Place cursor on that line Press “Go” button
More than one command or command on more than one line 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.
Project window shows variables in the data set
Results appear in output window
Commands typed in editing window
Standard Three Window Operation
Command Structure
VERB ; instruction ; … ; … $ 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 $
Create ; LogMilk = Log(Milk) $ Create ; LMC = .5*Log(Milk)*Log(Cosw) $ Create ; … any algebraic transformation $
SAMPLE ; first - last $ Sample ; 1 – 1000 $ Sample ; All $
REJECT ; condition $ Reject ; Cows < 20 $
Model Command Model ; Lhs = dependent variable
; Rhs = list of independent variables $ 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. Variants of models such as Poisson / NegBinomial Several hundred different models altogether
Model Command with Sample Definition
Model ; If [ condition ] ; Lhs = … ; Rhs = … ; etc. $
FRONTIER ; If [Year = 1988] ; Lhs = yit ; Rhs = one,x1,x2,x3,x4 ; Model = Rayleigh $
Name Conventions
CREATE ; Name = any function desired $
Name is the name of a new variable 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 FeaturesNAMELIST ; 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