Impulse response analysis in nonlinear multivariate models

Why do growth rates differ? This paper surveys the recent empirical literature on economic growth, starting with a discussion of stylized facts, data problems, and statistical methods. Six research questions are emphasized, drawing on growth and convergence research. In answering these questions, the paper argues that efficiency has grown at different rates across countries, casting doubt on neoclassical models in which technology is a public good. The latter half of the paper rounds up a variety of findings before providing answers to all six questions, including a short summary of how differences in growth rates arise.

The New Growth Evidence

http://people.stern.nyu.edu/wgreene/FrontierModeling/Reference-Papers/Greene-JE2005-Heterogeneity.pdf

Reconsidering heterogeneity in panel data estimators of the stochastic frontier model

The Econometrics of Panel DataSpringer Handbook of Science and Technology IndicatorsPanel Data EconometricsThe Econometrics of Panel DataA Practitioner's Guide to Stochastic Frontier Analysis Using StataBenchmarking for Performance EvaluationEssays on Microeconomics and Industrial OrganisationHealth System EfficiencyInternational Journal of Production EconomicsEconometric Analysis of Model Selection and Model TestingInternational Applications of Productivity and Efficiency AnalysisAdvanced Robust and Nonparametric Methods in Efficiency AnalysisEconometrics and the Philosophy of EconomicsThe Measurement of Productive EfficiencyMeasuring Efficiency in Health CareFinancial, Macro and Micro Econometrics Using REconometric Analysis of Cross Section and Panel DataApplied EconometricsProductivity and Efficiency AnalysisEconometric Model SelectionProductivity and Efficiency AnalysisStochastic Frontier AnalysisThe Oxford Handbook of Health EconomicsThe Measurement of Productive Efficiency and Productivity GrowthNew Directions in Productivity Measurement and Efficiency AnalysisA Primer on Efficiency Measurement for Utilities and Transport RegulatorsPanel Data EconometricsProduction and Efficiency Analysis with RApplications of Modern Production TheoryThe Measurement of Productive EfficiencyNonparametric Econometric Methods and ApplicationAn Introduction to Efficiency and Productivity AnalysisHealth, the Medical Profession, and RegulationThe Analysis of Household SurveysData Envelopment AnalysisProgramming Collective IntelligenceEfficiency AnalysisProductivity and Efficiency AnalysisMeasurement of Productivity and EfficiencyProduction Frontiers

The Econometric Approach to Efficiency Analysis

The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. It focuses on innovation in economic forecasting via industry applications. It presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications. It makes details about economic forecasting accessible to scholars in fields outside economics.

Handbook of Economic Forecasting

Stochastic frontier models: a bayesian perspective

https://pages.stern.nyu.edu/~wgreene/FrontierModeling/Reference-Papers/Koop-Osiewalski-Steel-JE1997-Hospital-FE.pdf

Bayesian efficiency analysis through individual effects: Hospital cost frontiers

A new class of continuous multivariate distributions on × ∈ ℜ n is proposed. We define these so-called υ-spherical distributions through properties of the density function in a location-scale context. We derive conditions for properness of υ-spherical distributions and discuss how to generate them in practice. The name “υ-spherical” is motivated by the fact that these distributions generalize the classes of spherical (when υ(·) is the l 2 norm) and l q -spherical (when υ(·) is the l q norm) distributions. Isodensity sets are still always situated around the location parameter μ, but exchangeability and axial symmetry are no longer imposed, as is illustrated in some examples. As an important special case, we define a class of distributions suggested by independent sampling from a generalization of exponential power distributions. This allows us to model skewness. Interestingly, all the robustness results found previously for spherical and l q -spherical models carry over directly to υ-spherical mo...

Modeling and Inference with υ-Spherical Distributions

On the use of panel data in stochastic frontier models with improper priors

In this paper we describe the use of Gibbs sampling methods for making posterior inferences.in stochastic frontier models with composed error. We show how Gibbs sampling methods can greatly reduce the computational difficulties involved in analyzing such models. Our findings are illustrated in an empirical example.

Jacek Osiewalski

Papers

Stochastic frontier models: a bayesian perspective

Bayesian efficiency analysis through individual effects: Hospital cost frontiers

Modeling and Inference with υ-Spherical Distributions

On the use of panel data in stochastic frontier models with improper priors

Posterior analysis of stochastic frontier models using Gibbs sampling