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AbstractAs an analytical methodology, the Policy Analysis Matrix (PAM) can be used to provide indicators of the incentives faced by producers in a given policy and institutional environment. However, the methodology suffers from the limitation that the analyst is unable to attach any level of confidence to the results, other than that generated through sensitivity analysis. In this paper the technique of bootstrapping is introduced as a general method for assessing the degree of uncertainty surrounding indicators derived from PAMs. The example of wheat production in Slovakia is used to demonstrate the usefulness of the additional information generated by this approach. Copyright © 2002 John Wiley & Sons, Ltd.

AbstractIn this paper, we econometrically examine the performance of salience theory (ST) for explaining observed behavior outside of a fully defined state contingent setting. Using a well‐known dataset, we find that only a minority of people act consistently in the way proposed by ST when confronted with lottery choices for which only marginal probabilities are presented. By estimating the implied dependence structure of payoffs consistent with ST, only a minority of people infer independent payoffs when attaching probabilities to states, a finding at odds with ST. Instead, a majority treat lotteries as having positively correlated payoffs which raise questions about the independence assumption in ST. Finally, we also find that ST explains choice behavior less consistently than expected utility. Thus, ST should not be assumed to be superior to the most prominent models within the literature when employed outside of particular contexts.

Bayesian analysis is given of an instrumental variable model that allows for t‐distributed errors in both the structural equation and the instrument equation. Specifically, the approach for dealing with t‐distributed errors is extended to the Bayesian instrumental variable estimator by modelling the variance for each error using a Gamma distributed hierarchical prior. The computation is carried out by using a Markov chain Monte Carlo sampling algorithm for the heteroskedastic case. An example using data illustrates the approach and shows that ignoring the presence of error terms with heavy tails in the instrument equation when it exists may lead to biased estimates.

The paper assesses the extent to which sampling variation affects findings about Malmquist productivity change derived using Data Envelopment Analysis (DEA), in the first stage calculating productivity indices and in the second stage investigating the farm-specific change in productivity. Confidence intervals for Malmquist indices are constructed using Simar and Wilson's (1999) bootstrapping procedure. The main contribution of the paper is to account in the second stage for the information provided by the first-stage bootstrap. The DEA standard errors of the Malmquist indices given by bootstrapping are employed in an innovative heteroscedastic panel regression, using a maximum likelihood procedure. The application is to a sample of 250 Polish farms over the period 1996-2000.
The confidence intervals' results suggest that the second half of 1990s for Polish farms was characterised not so much by productivity regress but rather by stagnation. As for the determinants of farm productivity change, we find that the integration of the DEA standard errors in the second-stage regression is significant in explaining a proportion of the variance in the error term. Although our heteroscedastic regression results differ with those from the standard OLS, in terms of significance and sign, they are consistent with theory and previous research.

This paper employs bootstrapping to correct for bias and to construct confidence intervals for Malmquist TFP indices derived with DEA. It uses these results to investigate the productivity change in Polish agriculture during a crucial period of the country's transition to a market economy, 1996-2000, when Poland was preparing for accession to the European Union. The bias corrected estimates show regress in productivity at an annual rate of 4 percent. The confidence intervals suggest that between two-thirds and four-fifths of the sample farms (250) in different years might have experienced no change in productivity. The cluster analysis based on confidence bounds reveals three paths of productivity change. Farms which recorded an increase in productivity at least in the last year of the analysed period, are larger, more capital intensive, run by younger farmers, and more integrated in factor and product markets. However, they account for only 19 percent of the sample farms. The most important for Poland now is to unlock the forces that can drive ahead structural reform and thus productivity growth.

JEL: D24 ; Q12 ; C6 ; This paper employs bootstrapping to correct for bias and to construct confidence intervals for Malmquist TFP indices derived with DEA. It uses these results to investigate the productivity change in Polish agriculture during a crucial period of the country's transition to a market economy, 1996-2000, when Poland was preparing for accession to the European Union.The bias corrected estimates show regress in productivity at an annual rate of 4 percent. The confidence intervals suggest that between two-thirds and four-fifths of the sample farms (250) in different years might have experienced no change in productivity. The cluster analysis based on confidence bounds reveals three paths of productivity change. Farms which recorded an increase in productivity at least in the last year of the analysed period, are larger, more capital intensive, run by younger farmers, and more integrated in factor and product markets. However, they account for only 19 percent of the sample farms. The most important for Poland now is to unlock the forces that can drive ahead structural reform and thus productivity growth.

In this paper we estimate and examine technical efficiency for a cross-section of Australian dairy farms using various frontier methodologies; Bayesian and Classical stochastic frontiers, and Data Envelopment Analysis. Our results indicate technical inefficiency is present in the sample data. We also identify statistical differences between the point estimates of technical efficiency generated by the various methodologies. However, the rank of farm level technical efficiency is statistically invariant to the estimation technique employed. Finally, when we compare confidence/credible intervals of technical efficiency we find significant overlap for many of the farms' intervals for all frontier methods employed. Our results indicate that the choice of estimation methodology may matter, but the explanatory power of all frontier methods is significantly weaker when we examine interval estimate of technical efficiency.