2014 Training Course - Productivity and Efficiency Analysis

Event Date: 
15 Dec, 2014 - 19 Dec, 2014 
Location: 
University of Pretoria - Pretoria, South Africa 


Overview

When we look at firms, we notice that the output of each can vary tremendously. If we assume that the firms are using the same technology, then what can account for such disparate outcomes. Productivity and efficiency analysis engages in exactly this exercise, discerning which firms are doing the best with the limited resources they have, and identifying which firms are lagging behind. Productivity and efficiency methods are widely used by regulators to monitor the behavior of firms in a given industry as well as to track performance over time of firms. Given the broad array of methods, coupled with myriad applications, accessing this literature can be daunting. This class will introduce students to the fundamentals of productivity and efficiency analysis. The class will also discuss in detail implementation of these models using the popular open source software R. The class will take place over five days to ensure maximum coverage of the core topics.

Applications for this course must be submitted by July 24, 2014.


Course Objectives

Participants will be introduced to a set of parametric, semi- and nonparametric econometric models focusing on the measurement of efficiency and productivity with real world applications that illustrate each of the models. The applications will include production, cost, and distance function estimation. Special emphasis will be given to modeling and estimating production/cost efficiency models. The open source statistical software R will be used for all empirical demonstrations. By the end of the class participants will be able to undertake a research project using either parametric
and/or semi- and non-parametric stochastic frontier approaches.

Course Outline

  1. Introduction to R using the basic linear regression model (all notes, data and examples will be provided)
  2. Notions of efficiency from a primal perspective will be introduced and the use of both the primal and distance function perspectives will be discussed. The emphasis will be on technical efficiency.
    1. Introduction
    2. Cross-Sectional Methods
      1. Distribution Free Methods
      2. Maximum Likelihood Methods
    3. Skewness
      1. Tests of Skewness
      2. The Wrong Skew Problem
    4. Estimating Firm-Specif ic Inefficiency
      1. Con fidence Intervals
      2. Tests of Correct Distributional Form
    5. Estimation/Inference of Cross-Sectional SF models in R
  3. Introduce panel data models and discuss various speci fications with or without technical change.
    1. Panel Data Methods
      1. Distribution Free Methods
      2. Maximum Likelihood Estimation
      3. Time Constant Variables
    2. Measurement of Technical Change
    3. Estimation/Inference of Panel Data SF models in R
  4. System estimation using cost function models.
    1. System Methods
    2. Cost System Issues
      1. Input/Output Oriented Ineffciency
      2. Fixed Inputs
      3. Greene Problem
    3. System Estimation/Inference of SF models in R
  5. Modeling determinants of inefficiency/Alternative Modellng Approaches/Introduce some alternative models.
    1. Determinants of Inefficiency
      1. The Scaling Property
      2. Mean versus Variance E ffects
    2. Alternative SF models (mixture models/Zero Inefficiency SF)
    3. Estimation in R
  6. Introduce semi- and non-parametric models to estimate.
    1. Semi- and non-parametric methods for estimating SF models
      1. Kernel Smoothing
      2. Semiparametric Production Frontier
      3. Deconvolved Technical Inefficiency
      4. Nonparametric Estimation of the Determinants of Inefficiency
    2. Estimation of Non/semiparametric SF models in R and STATA LAB session

PREREQUISITES

The course level is appropriate for participants with a background in economics, statistics, mathematics, and/or public policy. A strong background in quantitative analysis is required. Basic knowledge of the statistical software R is desirable. A general fluency in the statistical/econometric lingo at the (post-) doctoral level (hopefully in a non-statistics/econometric discipline) is required. More specifically, the Law of Large Numbers and the Central Limit Theorem should be understood.

software Requirements

This course will heavily leverage implementation in R, a powerful statistical software package that is freely available. R possesses the facilities to implement an impressive array of panel data estimators as well as to serve as an interface for data manipulation, making it an ideal choice when discussing the application of panel data estimators. Moreover, R's real strength is that users can readily and easily construct their own estimators and tests so that canned approaches do not need to be relied on, allowing users to stand on their legs when conducting empirical research.


ONLINE APPLICATION

In order to apply for this course, AGRODEP members must complete the following by July 24, 2014:

If you would like to practice using Stata before taking the proficiency test, please review the modules below. Information included covers Stata use for beginners, linear regressions, bivariate regressions, and panel data. You will need to know this information to successfully complete the test.

REAL WORLD PANEL DATA APPLICATIONS 

Once selected, course participants will have the opportunity to provide real data applications, one of which will be used as an example during the course.


Instructor

Christopher F. Parmeter is an Associate Professor in the Economics Department at the School of Business Administration at the University of Miami. His area of expertise is in applied econometrics with special interests in semi- and nonparametric methods, benefit transfers, meta-analysis and efficiency analysis. Dr. Parmeter has coauthored 30 peer reviewed scientific articles in leading econometric and applied economics journals.