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Frontier Models and Efficiency Measurement Lab Session 1PowerPoint Presentation

Frontier Models and Efficiency Measurement Lab Session 1

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Frontier Models and Efficiency Measurement Lab Session 1

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Stern School of Business

New York University

Frontier Models and Efficiency MeasurementLab Session 10Introduction

1Efficiency Measurement

2Frontier Functions

3Stochastic Frontiers

4Production and Cost

5Heterogeneity

6Model Extensions

7Panel Data

8Applications

Stern School of Business

New York University

Frontier Models and Efficiency MeasurementLab Session 1: Operating NLOGIT0Introduction

1Efficiency Measurement

2Frontier Functions

3Stochastic Frontiers

4Production and Cost

5Heterogeneity

6Model Extensions

7Panel Data

8Applications

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

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

N = 247 farms, T = 6 years (1993-1998)

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

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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

- 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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

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? for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

- 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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

Commands typed in editing window

Project window shows variables in the data set

Results appear in output window

Command Structure for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

- 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: for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

- 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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

- 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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

- 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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

- 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 for you. Submit the existing commands, modify them then submit, or type new commands in the same window.

Matrices B and VARB. Double click names to open windows. Use B and VARB in other MATRIX computations and commands.

Regression Analysis: Testing Cobb-Douglas vs. Translog computations

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 computations

Save Your Work When You Exit computations

Lab Exercises with Dairy Farm Data computations

- 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