The study applies the single-stage modelling stochastic frontier approach to investigate the performance of maize farmers in the Ejura-Sekyedumase District of Ghana. It estimates the level of technical efficiency and its determinants for 306 maize farmers. Findings indicated that land, labour and fertilizer influenced output positively whilst agrochemicals and seeds affected output negatively. A wide variation in output was also found among producers of maize. The study further revealed that age, sex and off-farm work activities were significant determinants of technical inefficiencies in production. Results from the maximum likelihood estimate of the frontier model showed that averagely, farmers were 67% technically efficient, implying that 33% of maize yield was not realized. The return to scale which measures the productivity level of farmers was 1.22, suggesting that the farmers are operating at an increasing returns to scale.

This paper introduces Bayesian analysis and demonstrates its application to parameter estimation of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm. The Bayesian logistic regression estimation is compared with the classical logistic regression. Both the classical logistic regression and the Bayesian logistic regression suggest that higher per capita income is associated with free trade of countries. The results also show a reduction of standard errors associated with the coefficients obtained from the Bayesian analysis, thus bringing greater stability to the coefficients. It is concluded that Bayesian Markov Chain Monte Carlo algorithm offers an alternative framework for estimating the logistic regression model.

Ascertaining the attitude of farmers toward risk is an important first step in understanding their behaviour and coping strategies they normally adopt to mitigate the effects of risk they constantly face within the environment they operate. This study aims at examining risk attitudes of farmers using the Equally Likely Certainty Equivalent with a Purely Hypothetical Risky prospect (ELCEPH), and analyzing coping strategies use by food crop farmers to deal with risk situations at Agona Duakwa in East District in the Central Region of Ghana. Simple random sampling technique was used to select 40 farmers from a sample frame of 100 farmers which was obtained through snow balling. Data was obtained through structured interview schedule with the selected farmers. Descriptive statistics (frequencies and percentages, means and standard deviations) were further used to analyse the data with the help of Statistical Product and Service Solution (SPSS) computer software. The study revealed that majority of food crop farmers are risk-averse. The regression result shows that access to microcredit, income status, age, education and household size are significant determinants of farmer risk attitude. Most food crop farmers use enterprise diversification, geographical diversification, and labour supply for non-farm wage to manage risk of loss in yield; however, the food crop farmers understudied neglect the use of crop insurance and some human and marketing risks coping strategies to deal with risk in their farming business.

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Alternative formulations of the Bayesian Information Criteria provide a basis for choosing between competing methods for detecting price asymmetry. However, very little is understood about their performance in the asymmetric price transmission modelling framework. In addressing this issue, this paper introduces and applies parametric bootstrap techniques to evaluate the ability of Bayesian Information Criteria (BIC) and Draper's Information Criteria (DIC) in discriminating between alternative asymmetric price transmission models under various error and sample size conditions. The results of the bootstrap simulations indicate that model selection performance depends on bootstrap sample size and the amount of noise in the data generating process. The Bayesian criterion clearly identifies the true asymmetric model out of different competing models in the presence of bootstrap samples. Draper's Information Criteria (DIC; Draper, 1995) outperforms BIC at either larger bootstrap sample size or lower noise level.