William greene stern school of business new york university
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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

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Frontier Models and Efficiency Measurement Lab Session 1

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William greene stern school of business new york university

William Greene

Stern School of Business

New York University

Frontier Models and Efficiency MeasurementLab Session 1

0Introduction

1Efficiency Measurement

2Frontier Functions

3Stochastic Frontiers

4Production and Cost

5Heterogeneity

6Model Extensions

7Panel Data

8Applications


Executing the lab scripts

Executing the Lab Scripts


William greene stern school of business new york university1

William Greene

Stern School of Business

New York University

Frontier Models and Efficiency MeasurementLab Session 1: Operating NLOGIT

0Introduction

1Efficiency Measurement

2Frontier Functions

3Stochastic Frontiers

4Production and Cost

5Heterogeneity

6Model Extensions

7Panel Data

8Applications


Lab session 1

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

Desktop


Entering data for analysis

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

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

Data on Spanish Dairy Farms

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


Frontier models and efficiency measurement lab session 1

Locate file Dairy.lpj


Project window

Project Window

Project window displays the data set currently being analyzed:

Variables

Matrices

Other program related results


Instructing limdep to do something

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

Use File:New/OK for an Editing Window


Text editing window

Text Editing Window

Commands will be entered in this window and submitted from here


Typing commands in the editor

Typing Commands in the Editor

Spacing and capitalization never matter. Just type instructions so they are easily readable and contain the right information.


Frontier models and efficiency measurement lab session 1

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

“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

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

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.


Frontier models and efficiency measurement lab session 1

Standard Three Window Operation

Commands typed in editing window

Project window shows variables in the data set

Results appear in output window


Command structure

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

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 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 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

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

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

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

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

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


Frontier models and efficiency measurement lab session 1

Results on the Screen in the Output Window


Frontier models and efficiency measurement lab session 1

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


Frontier models and efficiency measurement lab session 1

Scalar results from a regression can also be used in later computations


Regression analysis testing cobb douglas vs translog

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

Exiting the Program


Save your work when you exit

Save Your Work When You Exit


Lab exercises with dairy farm data

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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