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 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*x1 2, x12 = x1*x2, etc. year93 = dummy variable for year,…

Data on Spanish Dairy Farms InputUnitsMeanStd. Dev. MinimumMaximum MilkMilk production (liters) 131,108 92,539 14,110727,281 Cows# of milking cows Labor# man-equivalent units LandHectares of land devoted to pasture and crops FeedTotal amount of feedstuffs fed to dairy cows (tons) 57,94147,9813, ,732 N = 247 farms, T = 6 years ( )

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 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

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 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 ; $ 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’y $ CREATE ; e = y – X’bols $ CALC ; s2 = e’e / (N – Col(X)) $ MATRIX ; Vols =s2 * ;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