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 nonparametric stochastic frontier approaches.
Course Outline
 Introduction to R using the basic linear regression model (all notes, data and examples will be provided)

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.
 Introduction

CrossSectional Methods
 Distribution Free Methods
 Maximum Likelihood Methods

Skewness
 Tests of Skewness
 The Wrong Skew Problem

Estimating FirmSpecific Inefficiency
 Confidence Intervals
 Tests of Correct Distributional Form
 Estimation/Inference of CrossSectional SF models in R

Introduce panel data models and discuss various specifications with or without technical change.

Panel Data Methods
 Distribution Free Methods
 Maximum Likelihood Estimation
 Time Constant Variables
 Measurement of Technical Change
 Estimation/Inference of Panel Data SF models in R

Panel Data Methods

System estimation using cost function models.
 System Methods

Cost System Issues
 Input/Output Oriented Ineffciency
 Fixed Inputs
 Greene Problem
 System Estimation/Inference of SF models in R

Modeling determinants of inefficiency/Alternative Modellng Approaches/Introduce some alternative models.

Determinants of Inefficiency
 The Scaling Property
 Mean versus Variance Effects
 Alternative SF models (mixture models/Zero Inefficiency SF)
 Estimation in R

Determinants of Inefficiency

Introduce semi and nonparametric models to estimate.

Semi and nonparametric methods for estimating SF models
 Kernel Smoothing
 Semiparametric Production Frontier
 Deconvolved Technical Inefficiency
 Nonparametric Estimation of the Determinants of Inefficiency
 Estimation of Non/semiparametric SF models in R and STATA LAB session

Semi and nonparametric methods for estimating SF models
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 nonstatistics/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.
 Training Module 1: Introduction to Stata
 Training Module 2: Basic Data Management, Graphs, and LogFiles
 Training Module 3: Linear Regressions
 Training Module 4: Bivariate Regressions
 Training Module 5: Panel Data Regressions
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, metaanalysis and efficiency analysis. Dr. Parmeter has coauthored 30 peer reviewed scientific articles in leading econometric and applied economics journals.