A Stochastic Frontier Analysis of Output Level and Growth in Poland and Western Economies

This paper uses Bayesian stochastic frontier methods to measure the productivity gap between Poland and Western countries that existed before the beginning of the main Polish economic reform. Using data for 20 Western economies, Poland and Yugoslavia (1980–1990) we estimate a translog stochastic frontier and make inference about individual efficiencies. Following the methodology proposed in our earlier work, we also decompose output growth into technical, efficiency and input changes and examine patterns of growth in the period under consideration. This revised version was published online in July 2006 with corrections to the Cover Date.     

A Bayesian note on competing correlation structures in the dynamic linear regression model

A Bayesian posterior odds approach is used to distinguish between different error correlation structures in dynamic linear regression models. Recent classical results are provided with a Bayesian interpretation, and a small empirical example illustrates the approach.     

The Price-Wage Mechanism in Poland: An Endogenous Switching Model

This paper proposes a two-equation price-wage model that enables to test whether the inflationary pressure on wage rate is only present when the rate of inflation is greater than some threshold value. Since the likelihood function for this model is very nonstandard, we develop a small-sample Bayesian approach to estimate its parameters. Our empirical results for Poland, 1962–1993, give support to the hypothesis of the price- wage spiral with a positive threshold value of inflation. This revised version was published online in July 2006 with corrections to the Cover Date.     

The Components of Output Growth: A Stochastic Frontier Analysis

This paper uses Bayesian stochastic frontier methods to decompose output change into technical, efficiency and input changes. In the context of macroeconomic growth exercises, which typically involve small and noisy data sets, we argue that stochastic frontier methods are useful since they incorporate measurement error and assume a (flexible) parametric form for the production relationship. These properties enable us to calculate measures of uncertainty associated with the decomposition and minimize the risk of overfitting the noise in the data. Tools for Bayesian inference in such models are developed. An empirical investigation using data from 17 OECD countries for 10 years illustrates the practicality and usefulness of our approach.     

Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models

In this paper we describe the use of modern numerical integration methods for making posterior inferences in composed error stochastic frontier models for panel data or individual cross- sections. Two Monte Carlo methods have been used in practical applications. We survey these two methods in some detail and argue that Gibbs sampling methods can greatly reduce the computational difficulties involved in analyzing such models.