A THRESHOLD COINTEGRATION ANALYSIS OF ASYMMETRIC ADJUSTMENTS IN THE GHANAIAN MAIZE MARKETS

This paper analyzes the long-run equilibrium relationship between retail and wholesale Ghanaian maize prices with cointegration test assuming asymmetric adjustment. Using the Enders-Siklos asymmetric cointegration tests, it is found that the retail and wholesale prices are cointegrated with threshold adjustment. Furthermore, the adjustment process is asymmetric when the retail and wholesale prices adjust to achieve the long-term equilibrium. Finally, there is faster convergence for negative deviations from long-term equilibrium than for positive deviations. These results imply that price increases tend to persist whereas decreases tend to revert quickly towards equilibrium

Farmers Perception and Adaptation to Climate Change: An Estimation of Willingness to Pay

This paper assesses farmers’ perception and adaptation to climate change to enhance policy towards tackling the challenges climate change poses to the farmers in Ghana. With regards to farmers’ perception and methods of adaptation, majority of the farmers perceived increase in temperature and decrease in rainfall pattern. Farmers’ level of adaptation was found to be relatively high with majority of the farmers using changing planting dates, different crop varieties, soil conservation and water harvesting as the major adaptation measures to climate change impacts. However, access to water, high cost of adaptation, lack of information, lack of knowledge on adaptation, insecure property rights, insufficient access to inputs and lack of credits were identified as the major barriers to adaptation. The probit regression estimation results indicated that the probability of willingness to pay for climate change mitigation policies increases with age, years of education and ownership of farm land.Perception, adaptation, climate change, willingnessto pay, probit regression, Agricultural and Food Policy, Farm Management, Production Economics, Productivity Analysis, GA, IN,

RANK-BASED ESTIMATION FOR COBB-DOUGLAS MODELLING IN THE PRESENCE OF OUTLIERS

Ordinary least square (OLS) has been widely used in estimating the Cobb-Douglas production function when analysing the empirical linkage between inputs and outputs. However, the estimates based on OLS technique may be biased by the presence of outliers. Rank-based regression estimation is resistant to outliers and may result in unbiased estimates. The objective of this study is therefore to investigate by use of Monte Carlo methods, the performance of the Rank-based regression and OLS methods in estimating the Cobb-Douglas regression model using data with and without outliers. Monte Carlo simulation results indicate that the estimates of the coefficients of the Cobb-Douglas regression model derived from the Least Squares and the Rank-based estimation methods are accurate and equivalent or close to their true values for normal data regardless of variability in sample size. For data with outliers, Least Squares method is affected by outliers and yields inaccurate estimates of the coefficients of the Cobb-Douglas model across various sample sizes. Rank-based estimation remains robust to outliers in large samples and provides estimates of the coefficients of the Cobb-Douglas Regression model that are accurate and nearly equivalent to their true values. The evidence from Monte Carlo experimentation suggests that the proposed Rank-based estimation is likely to do no worse than the OLS with normal dataset and promise to do better when the dataset has outliers within the Cobb-Douglas production function modelling context. The presence of outliers can bias the results of the OLS estimation of the Cobb-Douglas model and it is recommended that the use of Rank-based regression can be an appropriate method to avoid such biased estimates

Residual likelihood approach for asymmetric price relationship selection

This study considers the problem of asymmetric price transmission model selection and investigates the performance of the recently developed model selection criteria (RIC) against commonly used alternatives (AIC and BIC) in terms of their ability to recover the true asymmetric data generating process. Asymmetric price transmission models are estimated and compared using the selection criteria. Monte Carlo simulation results indicate that the performance of the model selection methods depends on the sample size, the level of asymmetry and the amount of noise in the model used in the application. In larger samples, RIC is comparable to BIC and outperforms AIC. At higher noise levels, RIC is comparable to AIC and outperforms BIC. Additionally, at strong levels of asymmetry, RIC outperforms both AIC and BIC. These results suggest that RIC which has both BIC’s useful property of consistency and efficient property of AIC is a very reliable and useful criterion in asymmetric price transmission model selection

On the Comparison of Akaike Information Criterion and Consistent Akaike Information Criterion in Selection of an Asymmetric Price Relationship: Bootstrap Simulation Results

Akaike’s Information Criteria provide a basis for choosing between competing approaches to testing for price asymmetry. However, very little research has been undertaken to understand its performance in the price transmission modelling context. In addressing this issue, this paper introduces and applies parametric bootstrap techniques to evaluate the ability of Akaike Information Criteria (AIC) and Consistent Akaike Information Criteria (CAIC) in distinguishing between competing asymmetric price transmission models under various error and sample size conditions. Bootstrap simulation results suggest that the performance of the model selection methods depends on sample size and stochastic variance. The Bootstrap simulations further indicate that CAIC is consistent and performs better than the AIC in large bootstrap samples. The ability of the model selection methods to identify the true asymmetric price relationship decreases with increase in stochastic variance. The research findings demonstrate the usefulness of Bootstrap algorithms in price transmission model comparison and selection

Farmers Perception and Adaptation to Climate Change: An Estimation of Willingness to Pay

This paper assesses farmers’ perception and adaptation to climate change to enhance policy towards tackling the challenges climate change poses to the farmers in Ghana. With regards to farmers’ perception and methods of adaptation, majority of the farmers perceived increase in temperature and decrease in rainfall pattern. Farmers’ level of adaptation was found to be relatively high with majority of the farmers using changing planting dates, different crop varieties, soil conservation and water harvesting as the major adaptation measures to climate change impacts. However, access to water, high cost of adaptation, lack of information, lack of knowledge on adaptation, insecure property rights, insufficient access to inputs and lack of credits were identified as the major barriers to adaptation. The probit regression estimation results indicated that the probability of willingness to pay for climate change mitigation policies increases with age, years of education and ownership of farm land

Alternative Approaches to Technical Efficiency Estimation in the Stochastic Frontier Model

Estimating the stochastic frontier model and calculating technical efficiency of decision making units are of great importance in applied production economic works. This paper estimates technical efficiency from the stochastic frontier model using Jondrow, and Battese and Coelli approaches. Simulated data is employed to compare the alternative methods. Empirical results show a strong correlation between the alternative methods regardless of the differences in the actual values of the efficiency estimates. Mean technical efficiency is sensitive to the choice of estimation method. Analysis of variance and Tukey’s test suggest difference in means between the efficiency scores from different methods. Battese and Coelli’s approach produces more homogenous estimates of technical efficiency when compared with the Jondrow’s mean or mode approach. Our results suggest that differences in conclusion are possible when the alternative methods of measuring technical efficiency are applied

A Stochastic Production Investigation of Fish Farms in Ghana

This paper considers the stochastic production frontier approach to analyse the technical efficiency and its determinants of fish farms in Ghana using a cross-section data of 150 farms. It considers the explicit effects of family and hired labour on production by setting the log-value of the zero-observation of these two sources of labour to zero with dummy variables. Results demonstrate that expected elasticities of mean output with respect to all input variables are positive and significant. Findings also show that family and hired labour used for fish farming in Ghana may be equally productive. Fish farms in Ghana are revealed to be characterised by technology with increasing return to scale. The combined effects of operational and farm specific factors are found to influence efficiency. The study further reveals that inclusion of interaction between some exogenous variables in the inefficiency model is significant in explaining the variation in efficiency. Results also suggest that small pond operators are more efficient than farms with large ponds. Mean technical efficiency is estimated to be 78 percent