Topics in Microeconometrics Professor William Greene Stern School of Business, New York University at Curtin Business School Curtin University Perth July.

Slides:



Advertisements
Similar presentations
[Part 4] 1/25 Stochastic FrontierModels Production and Cost Stochastic Frontier Models William Greene Stern School of Business New York University 0Introduction.
Advertisements

Part 12: Asymptotics for the Regression Model 12-1/39 Econometrics I Professor William Greene Stern School of Business Department of Economics.
Part 1: Simple Linear Model 1-1/301-1 Regression Models Professor William Greene Stern School of Business IOMS Department Department of Economics.
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.
- 1 - Benchmarking With An Application to Electricity Distribution GAP Workshop 14 December 2005, Berlin Astrid Cullmann, DIW Berlin E E².
The Simple Linear Regression Model: Specification and Estimation
Microeconometric Modeling
Data Envelopment Analysis MSc in Regulation and Competition Quantitative techniques in Practice John Cubbin, City University©
Maximum likelihood Conditional distribution and likelihood Maximum likelihood estimations Information in the data and likelihood Observed and Fisher’s.
Topic 7 Sampling And Sampling Distributions. The term Population represents everything we want to study, bearing in mind that the population is ever changing.
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.
Maximum likelihood (ML)
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-
The Paradigm of Econometrics Based on Greene’s Note 1.
Part 1: Introduction 1-1/22 Econometrics I Professor William Greene Stern School of Business Department of Economics.
Chapter 7 Technology and Production Copyright © 2014 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written.
CHAPTER 2: TWO VARIABLE REGRESSION ANALYSIS: SOME BASIC IDEAS
Part 17: Regression Residuals 17-1/38 Statistics and Data Analysis Professor William Greene Stern School of Business IOMS Department Department of Economics.
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.
LECTURE 2. GENERALIZED LINEAR ECONOMETRIC MODEL AND METHODS OF ITS CONSTRUCTION.
MULTIPLE TRIANGLE MODELLING ( or MPTF ) APPLICATIONS MULTIPLE LINES OF BUSINESS- DIVERSIFICATION? MULTIPLE SEGMENTS –MEDICAL VERSUS INDEMNITY –SAME LINE,
EFFICIENCY OF BIODYNAMIC FARMS Marie Pechrová Czech University of Life Sciences Prague, Faculty of Economics and Management September 17-18, 2013.
VI. Evaluate Model Fit Basic questions that modelers must address are: How well does the model fit the data? Do changes to a model, such as reparameterization,
Efficiency Measurement William Greene Stern School of Business New York University.
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.
1 Lecture 16: Point Estimation Concepts and Methods Devore, Ch
De la Economía Agraria a la Economía Rural y Agroalimentaria TECHNICAL EFFICIENCY AND PRODUCTIVITY ANALYSIS OF SPANISH CITRUS FARMS Fatima Lambarraa, Teresa.
Generalised method of moments approach to testing the CAPM Nimesh Mistry Filipp Levin.
Chapter 2 Ordinary Least Squares Copyright © 2011 Pearson Addison-Wesley. All rights reserved. Slides by Niels-Hugo Blunch Washington and Lee University.
1 Standard error Estimated standard error,s,. 2 Example 1 While measuring the thermal conductivity of Armco iron, using a temperature of 100F and a power.
Econometrics in Health Economics Discrete Choice Modeling and Frontier Modeling and Efficiency Estimation Professor William Greene Stern School of Business.
Psychology 202a Advanced Psychological Statistics October 22, 2015.
8-1 MGMG 522 : Session #8 Heteroskedasticity (Ch. 10)
Efficiency Measurement William Greene Stern School of Business New York University.
[Part 1] 1/18 Stochastic FrontierModels Efficiency Measurement Stochastic Frontier Models William Greene Stern School of Business New York University 0Introduction.
Linear Regression Linear Regression. Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Purpose Understand Linear Regression. Use R functions.
Lecture 3 (Chapter 4). Linear Models for Longitudinal Data Linear Regression Model (Review) Ordinary Least Squares (OLS) Maximum Likelihood Estimation.
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.”
R. Kass/W03 P416 Lecture 5 l Suppose we are trying to measure the true value of some quantity (x T ). u We make repeated measurements of this quantity.
1/61: Topic 1.2 – Extensions of the Linear Regression Model Microeconometric Modeling William Greene Stern School of Business New York University New York.
Benchmarking for Improved Water Utility Performance.
Lecturer: Ing. Martina Hanová, PhD. Business Modeling.
Chapter 4. The Normality Assumption: CLassical Normal Linear Regression Model (CNLRM)
Estimating standard error using bootstrap
Linear Regression with One Regression
The Maximum Likelihood Method
Microeconometric Modeling
6-1 Introduction To Empirical Models
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
Chengyaun yin School of Mathematics SHUFE
Simple Linear Regression
Stochastic Frontier Models
Econometrics I Professor William Greene Stern School of Business
Microeconometric Modeling
Microeconometric Modeling
Panel Stochastic Frontier Models with Endogeneity in Stata
Presentation transcript:

Topics in Microeconometrics Professor William Greene Stern School of Business, New York University at Curtin Business School Curtin University Perth July 22-24, 2013

1. Efficiency

Modeling Inefficiency

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).

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.

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)

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)

Production Functions

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:

Isoquants and Level Sets

The Distance Function

Inefficiency in Production

Production Function Model with Inefficiency

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.

Specification

Corrected Ordinary Least Squares

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 ].

COLS and MOLS

Principles The production function resembles a regression model (with a structural interpretation). We are modeling the disturbance process in more detail.

Frontier Functions

Deterministic Frontier: Programming Estimators

Estimating Inefficiency

Statistical Problems with Programming Estimators They do correspond to MLEs. The likelihood functions are “irregular” There are no known statistical properties – no estimable covariance matrix for estimates. They might be “robust,” like LAD. Noone knows for sure. Never demonstrated.

An Orthodox Frontier Model with a Statistical Basis

Extensions Cost frontiers, based on duality results: ln y = f(x) – u  ln C = g(y,w) + u’ u > 0. u’ > 0. Economies of scale and allocative inefficiency blur the relationship. Corrected and modified least squares estimators based on the deterministic frontiers are easily constructed.

Data Envelopment Analysis

Methodological Problems with DEA Measurement error Outliers Specification errors The overall problem with the deterministic frontier approach

DEA and SFA: Same Answer? Christensen and Greene data N=123 minus 6 tiny firms X = capital, labor, fuel Y = millions of KWH Cobb-Douglas Production Function vs. DEA (See Coelli and Perelman (1999).)

Comparing the Two Methods.

Total Factor Productivity