[Part 1] 1/18 Stochastic FrontierModels Efficiency Measurement Stochastic Frontier Models William Greene Stern School of Business New York University 0Introduction.

Slides:



Advertisements
Similar presentations
Efficiency and Productivity Measurement: Bootstrapping DEA Scores
Advertisements

[Part 4] 1/25 Stochastic FrontierModels Production and Cost Stochastic Frontier Models William Greene Stern School of Business New York University 0Introduction.
Part 12: Asymptotics for the Regression Model 12-1/39 Econometrics I Professor William Greene Stern School of Business Department of Economics.
6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.
[Part 1] 1/15 Discrete Choice Modeling Econometric Methodology Discrete Choice Modeling William Greene Stern School of Business New York University 0Introduction.
R. Werner Solar Terrestrial Influences Institute - BAS Time Series Analysis by means of inference statistical methods.
A Short Introduction to Curve Fitting and Regression by Brad Morantz
[Part 3] 1/49 Stochastic FrontierModels Stochastic Frontier Model Stochastic Frontier Models William Greene Stern School of Business New York University.
Topics in Microeconometrics Professor William Greene Stern School of Business, New York University at Curtin Business School Curtin University Perth July.
- 1 - Benchmarking With An Application to Electricity Distribution GAP Workshop 14 December 2005, Berlin Astrid Cullmann, DIW Berlin E E².
Microeconometric Modeling
CHAPTER 3 ECONOMETRICS x x x x x Chapter 2: Estimating the parameters of a linear regression model. Y i = b 1 + b 2 X i + e i Using OLS Chapter 3: Testing.
Topic4 Ordinary Least Squares. Suppose that X is a non-random variable Y is a random variable that is affected by X in a linear fashion and by the random.
Part 21: Generalized Method of Moments 21-1/67 Econometrics I Professor William Greene Stern School of Business Department of Economics.
Chapter 6 The production, costs, and technology of health care 1.Production and the possibility for substitution 2.Economies of scale and scope 3.Technology-
Cross-sectional:Observations on individuals, households, enterprises, countries, etc at one moment in time (Chapters 1–10, Models A and B). 1 During this.
[Part 8] 1/27 Stochastic FrontierModels Applications Stochastic Frontier Models William Greene Stern School of Business New York University 0Introduction.
Chapter 7 Technology and Production Copyright © 2014 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written.
[Part 7] 1/68 Stochastic FrontierModels Panel Data Stochastic Frontier Models William Greene Stern School of Business New York University 0Introduction.
1 Regression Models with Binary Response Regression: “Regression is a process in which we estimate one variable on the basis of one or more other variables.”
CHAPTER 2: TWO VARIABLE REGRESSION ANALYSIS: SOME BASIC IDEAS
The Stochastic Nature of Production Lecture VII. Stochastic Production Functions  Just, Richard E. and Rulan D. Pope. “Stochastic Specification of Production.
Chapter 6 Production. The Production Function A production function tells us the maximum output a firm can produce (in a given period) given available.
Lecture 12 Statistical Inference (Estimation) Point and Interval estimation By Aziza Munir.
Efficiency of Public Spending in Developing Countries: A Stochastic Frontier Approach William Greene Stern School of Business World Bank, May 23, 2005.
Efficiency Measurement William Greene Stern School of Business New York University.
Economic.
Microeconometric Modeling William Greene Stern School of Business New York University.
Regression Maarten Buis Outline Recap Estimation Goodness of Fit Goodness of Fit versus Effect Size transformation of variables and effect.
EFFICIENCY OF BIODYNAMIC FARMS Marie Pechrová Czech University of Life Sciences Prague, Faculty of Economics and Management September 17-18, 2013.
 A probability function is a function which assigns probabilities to the values of a random variable.  Individual probability values may be denoted by.
Efficiency Measurement William Greene Stern School of Business New York University.
Ordinary Least Squares Estimation: A Primer Projectseminar Migration and the Labour Market, Meeting May 24, 2012 The linear regression model 1. A brief.
Part 2: Model and Inference 2-1/49 Regression Models Professor William Greene Stern School of Business IOMS Department Department of Economics.
Efficiency Measurement William Greene Stern School of Business New York University.
Using Productivity Modeling to Assess Regional Advantage ST&E Policy Lab Research Methods Seminar April 2, 2009 Joshua Drucker University of Illinois at.
De la Economía Agraria a la Economía Rural y Agroalimentaria TECHNICAL EFFICIENCY AND PRODUCTIVITY ANALYSIS OF SPANISH CITRUS FARMS Fatima Lambarraa, Teresa.
1 G Lect 2w Review of expectations Conditional distributions Regression line Marginal and conditional distributions G Multiple Regression.
Chapter 2 Ordinary Least Squares Copyright © 2011 Pearson Addison-Wesley. All rights reserved. Slides by Niels-Hugo Blunch Washington and Lee University.
Frontier Models and Efficiency Measurement Lab Session 2: Stochastic Frontier William Greene Stern School of Business New York University 0Introduction.
Unit 1 Review Standards 1-8. Standard 1: Describe subsets of real numbers.
Econometrics in Health Economics Discrete Choice Modeling and Frontier Modeling and Efficiency Estimation Professor William Greene Stern School of Business.
Efficiency Measurement William Greene Stern School of Business New York University.
Efficiency Measurement William Greene Stern School of Business New York University.
Part 4A: GMM-MDE[ 1/33] Econometric Analysis of Panel Data William Greene Department of Economics Stern School of Business.
Stochastic Frontier Models
Stochastic Error Functions I: Another Composed Error Lecture X.
Measuring Technical Efficiency Lecture XIV. Basic Concepts of Production Efficiency Lovell, C. A. Knox. “Production Frontiers and Productive Efficiency.”
1/61: Topic 1.2 – Extensions of the Linear Regression Model Microeconometric Modeling William Greene Stern School of Business New York University New York.
Efficiency Measurement William Greene Stern School of Business New York University.
Chapter 4. The Normality Assumption: CLassical Normal Linear Regression Model (CNLRM)
Dynamic Models, Autocorrelation and Forecasting
Efficiency Measurement
Microeconometric Modeling
Introduction to Instrumentation Engineering
The Regression Model Suppose we wish to estimate the parameters of the following relationship: A common method is to choose parameters to minimise the.
Efficiency Measurement
Stochastic Frontier Models
Efficiency Measurement
Stochastic Frontier Models
Microeconometric Modeling
Life cycle patterns, farm performance and structural change: an empirical research Steven Van Passel I’m working for the policy research centre for sustainable.
The Simple Linear Regression Model: Specification and Estimation
Econometrics I Professor William Greene Stern School of Business
Stochastic Frontier Models
William Greene Stern School of Business New York University
Simple Linear Regression
Econometrics I Professor William Greene Stern School of Business
Microeconometric Modeling
Panel Stochastic Frontier Models with Endogeneity in Stata
Presentation transcript:

[Part 1] 1/18 Stochastic FrontierModels Efficiency Measurement Stochastic Frontier Models William Greene Stern School of Business New York University 0Introduction 1Efficiency Measurement 2Frontier Functions 3Stochastic Frontiers 4Production and Cost 5Heterogeneity 6Model Extensions 7Panel Data 8Applications

[Part 1] 2/18 Stochastic FrontierModels Efficiency Measurement The Production Function “A single output technology is commonly described by means of a production function f(z) that gives the maximum amount q of output that can be produced using input amounts (z 1,…,z L-1 ) > 0. “Microeconomic Theory,” Mas-Colell, Whinston, Green: Oxford, 1995, p See also Samuelson (1938) and Shephard (1953).

[Part 1] 3/18 Stochastic FrontierModels Efficiency Measurement Thoughts on Inefficiency Failure to achieve the theoretical maximum  Hicks (ca. 1935) on the benefits of monopoly  Leibenstein (ca. 1966): X inefficiency  Debreu, Farrell (1950s) on management inefficiency All related to firm behavior in the absence of market restraint – the exercise of market power.

[Part 1] 4/18 Stochastic FrontierModels Efficiency Measurement A History of Empirical Investigation  Cobb-Douglas (1927)  Arrow, Chenery, Minhas, Solow (1963)  Joel Dean (1940s, 1950s)  Johnston (1950s)  Nerlove (1960)  Berndt, Christensen, Jorgenson, Lau (1972)  Aigner, Lovell, Schmidt (1977)

[Part 1] 5/18 Stochastic FrontierModels Efficiency Measurement Inefficiency in the “Real” World Measurement of inefficiency in “markets” – heterogeneous production outcomes:  Aigner and Chu (1968)  Timmer (1971)  Aigner, Lovell, Schmidt (1977)  Meeusen, van den Broeck (1977)

[Part 1] 6/18 Stochastic FrontierModels Efficiency Measurement Production Functions

[Part 1] 7/18 Stochastic FrontierModels Efficiency Measurement Defining the Production Set Level set: The Production function is defined by the isoquant The efficient subset is defined in terms of the level sets:

[Part 1] 8/18 Stochastic FrontierModels Efficiency Measurement Isoquants and Level Sets

[Part 1] 9/18 Stochastic FrontierModels Efficiency Measurement The Distance Function

[Part 1] 10/18 Stochastic FrontierModels Efficiency Measurement Inefficiency in Production

[Part 1] 11/18 Stochastic FrontierModels Efficiency Measurement Production Function Model with Inefficiency

[Part 1] 12/18 Stochastic FrontierModels Efficiency Measurement Cost Inefficiency y* = f(x)  C* = g(y*,w) (Samuelson – Shephard duality results) Cost inefficiency: If y < f(x), then C must be greater than g(y,w). Implies the idea of a cost frontier. lnC = lng(y,w) + u, u > 0.

[Part 1] 13/18 Stochastic FrontierModels Efficiency Measurement Specifications

[Part 1] 14/18 Stochastic FrontierModels Efficiency Measurement Corrected Ordinary Least Squares

[Part 1] 15/18 Stochastic FrontierModels Efficiency Measurement COLS Cost Frontier

[Part 1] 16/18 Stochastic FrontierModels Efficiency Measurement Modified OLS An alternative approach that requires a parametric model of the distribution of u i is modified OLS (MOLS). The OLS residuals, save for the constant displacement, are pointwise consistent estimates of their population counterparts, - u i. Suppose that u i has an exponential distribution with mean λ. Then, the variance of u i is λ 2, so the standard deviation of the OLS residuals is a consistent estimator of E[u i ] = λ. Since this is a one parameter distribution, the entire model for u i can be characterized by this parameter and functions of it. The estimated frontier function can now be displaced upward by this estimate of E[u i ].

[Part 1] 17/18 Stochastic FrontierModels Efficiency Measurement COLS and MOLS

[Part 1] 18/18 Stochastic FrontierModels Efficiency Measurement Principles  The production function model resembles a regression model (with a structural interpretation).  We are modeling the disturbance process in more detail.