William greene stern school of business new york university
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William Greene Stern School of Business New York University. Efficiency Measurement. Lab Session 4. Panel Data. Group Size Variables for Unbalanced Panels. Creating a Group Size Variable. Requires an ID variable (such as FARM) (1) Set the full sample exactly as desired

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Efficiency Measurement

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William greene stern school of business new york university

William Greene

Stern School of Business

New York University

Efficiency Measurement


Lab session 4

Lab Session 4

Panel Data


Group size variables for unbalanced panels

Group Size Variables for Unbalanced Panels


Creating a group size variable

Creating a Group Size Variable

  • Requires an ID variable (such as FARM)

  • (1) Set the full sample exactly as desired

  • (2) SETPANEL ; Group = the id variable ; Pds = the name you want limdep to use for the periods variable $

    SETPANEL ; Group = farm ; pds = ti $


Application to spanish dairy farms

Application to Spanish Dairy Farms

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


Exploring a panel data set dairy

Exploring a Panel Data Set: Dairy

REGRESS ; Lhs = YIT

; RHS = COBBDGLS

; PANEL $

REGRESS ; Lhs = YIT

; RHS = COBBDGLS

; PANEL ; Het = Group $


Initiating a panel data model

Initiating a Panel Data Model


Nonlinear panel data models

Nonlinear Panel Data Models

MODEL NAME ; Lhs = …

; RHS = …

; Panel

; … any other model parts … $

ALL PANEL DATA MODEL COMMANDS ARE THE SAME


Panel data frontier model commands

Panel Data Frontier Model Commands

FRONTIER ; LHS = … [ ; COST ]

; RHS = …

[; EFF = …]

; Panel

; ... the rest of the model

; any other options $


Pitt and lee random effects

Pitt and Lee Random Effects

FRONTIER ; LHS = … [ ; COST ]

; RHS = …

[; EFF = …]

; Panel

; any other options $

This is the default panel model.


Pitt and lee model

Pitt and Lee Model


Pitt and lee random effects with heteroscedasticity and time invariant inefficiency

Pitt and Lee Random Effects with Heteroscedasticity and Time Invariant Inefficiency

FRONTIER ; LHS = … [ ; COST ]

; RHS = …

[; EFF = …]

; Panel

; HET ; HFU = …

; HFV = … $


Pitt and lee random effects with heteroscedasticity and truncation time invariant inefficiency

Pitt and Lee Random Effectswith Heteroscedasticity and Truncation Time Invariant Inefficiency

FRONTIER ; LHS = … [ ; COST ]

; RHS = …

[; EFF = …]

; Panel

; HET ; HFU = …

; HFV = …

; MODEL = T ; RH2 = One,… $


Pitt and lee random effects with heteroscedasticity time invariant inefficiency

Pitt and Lee Random Effectswith HeteroscedasticityTime Invariant Inefficiency

FRONTIER ; LHS = … [ ; COST ]

; RHS = …

[; EFF = …]

; Panel

; HET ; HFU = …

; HFV = … $


Schmidt and sickles fixed effects

Schmidt and Sickles Fixed Effects

REGRESS ; LHS = … ; RHS = …

; PANEL

; PAR ; FIXED $

CREATE ; AI = ALPHAFE ( id ) $

CALC ; MAXAI = Max(AI) $

CREATE ; UI = MAXAI – AI $

(Use Minimum and AI – MINAI for cost)


True random effects time varying inefficiency

True Random EffectsTime Varying Inefficiency

FRONTIER ; LHS = … [ ; COST ] ; RHS = … $

FRONTIER ; LHS = … [ ; COST ] ; RHS = …

; Panel ; Halton (a good idea) ; PTS = number for the simulations

; RPM ; FCN = ONE (n)

; EFF = … $

Note, first and second FRONTIER commands are identical. This sets up the starting values.


True fixed effects time varying inefficiency

True Fixed EffectsTime Varying Inefficiency

FRONTIER ; LHS = … [ ; COST ] ; RHS = … $

FRONTIER ; LHS = … [ ; COST ] ; RHS = …

; Panel

; FEM

; EFF = … $

Note, first and second FRONTIER commands are identical. This sets up the starting values.


Battese and coelli time varying inefficiency

Battese and CoelliTime Varying Inefficiency

FRONTIER ; LHS = … [ ; COST ] ; RHS = …

; Panel

; MODEL = BC

; EFF = … $

This is the default specification, u(i,t) = exp[h(t-T)] |U(i)|

To use the extended specification, u(i,t)=exp[d’z(i)] |U(i)|

; Het

; HFU = variables


Other models

Other Models

There are many other panel models with time varying and time invariant inefficiency, heteroscedasticity, heterogeneity, etc.

Latent class,

Random parameters

Sample selection,

And so on….


Lab session 41

Lab Session 4

Model Building


Modeling assignment

Modeling Assignment


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