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The volume examines the state-of-the-art of productivity and
efficiency analysis. It brings together a selection of the best
papers from the 10th North American Productivity Workshop. By
analyzing world-wide perspectives on challenges that local
economies and institutions may face when changes in productivity
are observed, readers can quickly assess the impact of productivity
measurement, productivity growth, dynamics of productivity change,
measures of labor productivity, measures of technical efficiency in
different sectors, frontier analysis, measures of performance,
industry instability and spillover effects. The contributions in
this volume focus on the theory and application of economics,
econometrics, statistics, management science and operational
research related to problems in the areas of productivity and
efficiency measurement. Popular techniques and methodologies
including stochastic frontier analysis and data envelopment
analysis are represented. Chapters also cover broader issues
related to measuring, understanding, incentivizing and improving
the productivity and performance of firms, public services, and
industries.
The volume examines the state-of-the-art of productivity and
efficiency analysis. It brings together a selection of the best
papers from the 10th North American Productivity Workshop. By
analyzing world-wide perspectives on challenges that local
economies and institutions may face when changes in productivity
are observed, readers can quickly assess the impact of productivity
measurement, productivity growth, dynamics of productivity change,
measures of labor productivity, measures of technical efficiency in
different sectors, frontier analysis, measures of performance,
industry instability and spillover effects. The contributions in
this volume focus on the theory and application of economics,
econometrics, statistics, management science and operational
research related to problems in the areas of productivity and
efficiency measurement. Popular techniques and methodologies
including stochastic frontier analysis and data envelopment
analysis are represented. Chapters also cover broader issues
related to measuring, understanding, incentivizing and improving
the productivity and performance of firms, public services, and
industries.
Quantile Methods for Stochastic Frontier Analysis seeks to merge
two seemingly disparate econometric fields, quantile estimation and
stochastic frontier analysis (SFA). Why might these two fields be
viewed as disparate? Quantiles exist on a continuum of the
distribution; the frontier is a fixed object of it. As will be
seen, these two approaches can, when used properly, be merged to
provide a unified approach to studying a stochastic boundary.
Sections 1 to 5 present the current state of affairs. Section 1
details the very close link between the regression function and the
conditional quantile function, in order to show that the quantile
relation is not some disconnected statistical aspect that lives
independently of our regression specification. This section also
shows what the quantile approach and the Q-estimator actually do,
and we contrast this with what SFA models want to do, using also a
simulated example. Section 2 presents the main characteristics and
properties of the linear Q-estimator when the error term is
independent of the regressors, as a necessary preparation to move
to Section 3, where the authors show how some of these properties
are fundamentally incompatible with the goals and purposes of SFA.
Section 4 discusses recent advances that properly construct the
deterministic frontier. Section 5 moves away from quantile
regression and presents likelihood-based approaches that use
density functions that include as one of their parameters the
probability of the zero-quantile of their distributions. Sections 6
to 9 present a new estimator, but also metrics and insights that
allow to fruitfully use the quantile approach in SFA. Section 6
shows how one can use the Qestimator together with additional
assumptions in order to provide conceptually valid and useful
estimation and inference results in SFMs. Section 7 presents
quantile-dependent measures of efficiency both at the sample level,
and at the individual level, but also how the conditional quantiles
of the distribution of inefficiency can be used to offer a picture
of how individual efficiency scores are distributed around a chosen
quantile of the efficiency distribution. Section 8 proves a
fundamental result: that positive and high values of the composite
error term of production SFA models, are expected to co-exist with
low inefficiency, in a concrete probabilistic sense. Section 9
examines the case of dependence between the error term and the
regressors or other covariates. Section 10 provides an empirical
illustration that showcases the approach of the four previous
Sections, and functions as a guide for detailed applied studies.
Section 11 includes a list of the various open issues as well as
ideas and directions for future research, while Section 12 offers a
short summary and conclusions.
The majority of empirical research in economics ignores the
potential benefits of nonparametric methods, while the majority of
advances in nonparametric theory ignores the problems faced in
applied econometrics. This book helps bridge this gap between
applied economists and theoretical nonparametric econometricians.
It discusses in depth, and in terms that someone with only one year
of graduate econometrics can understand, basic to advanced
nonparametric methods. The analysis starts with density estimation
and motivates the procedures through methods that should be
familiar to the reader. It then moves on to kernel regression,
estimation with discrete data, and advanced methods such as
estimation with panel data and instrumental variables models. The
book pays close attention to the issues that arise with
programming, computing speed, and application. In each chapter, the
methods discussed are applied to actual data, paying attention to
presentation of results and potential pitfalls.
Efficiency Analysis details the important econometric area of
efficiency estimation, both past approaches as well as new
methodology. There are two main camps in efficiency analysis: that
which estimates maximal output and attributes all departures from
this as inefficiency, known as Data Envelopment Analysis (DEA), and
that which allows for both unobserved variation in output due to
shocks and Measurement error as well as inefficiency, known as
Stochastic Frontier Analysis (SFA). This volume focuses exclusively
on SFA. The econometric study of efficiency analysis typically
begins by constructing a convoluted error term that is composed on
noise, shocks, Measurement error, and a one-sided shock called
inefficiency. Early in the development of these methods, attention
focused on the proposal of distributional assumptions which yielded
a likelihood function whereby the parameters of the distributional
components of the convoluted error could be recovered. The field
evolved to the study of individual specific efficiency scores and
the extension of these methods to panel data. Recently, attention
has focused on relaxing the stringent distributional assumptions
that are commonly imposed, relaxing the functional form assumptions
commonly placed on the underlying technology, or some combination
of both. All told exciting and seminal breakthroughs have occurred
in this literature, and reviews of these methods are needed to
effectively detail the state of the art. The generality of SFA is
such that the study of efficiency has gone beyond simple
application of frontier methods to study firms and appears across a
diverse Set of applied milieus. This review should appeal to those
outside of the efficiency literature seeking to learn about new
methods which might assist them in uncovering phenomena in their
applied area of interest.
The majority of empirical research in economics ignores the
potential benefits of nonparametric methods, while the majority of
advances in nonparametric theory ignores the problems faced in
applied econometrics. This book helps bridge this gap between
applied economists and theoretical nonparametric econometricians.
It discusses in depth, and in terms that someone with only one year
of graduate econometrics can understand, basic to advanced
nonparametric methods. The analysis starts with density estimation
and motivates the procedures through methods that should be
familiar to the reader. It then moves on to kernel regression,
estimation with discrete data, and advanced methods such as
estimation with panel data and instrumental variables models. The
book pays close attention to the issues that arise with
programming, computing speed, and application. In each chapter, the
methods discussed are applied to actual data, paying attention to
presentation of results and potential pitfalls.
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