arXiv subjects

Reason Lesego Machete

Publications and source records attributed to Reason Lesego Machete.

3 recordsLinked to original sources

arXiv2020

Towards a Sustainable Agricultural Credit Guarantee Scheme

Since 1986, Government of Botswana has been running an Agricultural Credit Guarantee Scheme for dry-land arable farming. The scheme purports to assist dry-land crop farmers who have taken loans with participating banks or lending institutions to help them meet their debt obligations in case of crop failure due to drought, floods, frost or hailstorm. Nonetheless, to date, the scheme has focused solely on drought. The scheme has placed an unsustainable financial burden on Government because it is not based on sound actuarial principles. This paper argues that the level of Government subsidies should take into account the gains made by farmers during non-drought years. It is an attempt to circumvent the challenges of correlated climate risks and recommends a quasi self-financing mechanism, assuming that the major driver of crop yield failure is drought. Moreover, it provides a novel subsidy and premium rate setting method.

q-fin.GN↗

arXiv2012

Contrasting Probabilistic Scoring Rules

There are several scoring rules that one can choose from in order to score probabilistic forecasting models or estimate model parameters. Whilst it is generally agreed that proper scoring rules are preferable, there is no clear criterion for preferring one proper scoring rule above another. This manuscript compares and contrasts some commonly used proper scoring rules and provides guidance on scoring rule selection. In particular, it is shown that the logarithmic scoring rule prefers erring with more uncertainty, the spherical scoring rule prefers erring with lower uncertainty, whereas the other scoring rules are indifferent to either option.

math.ST↗

arXiv2012

Early Warning with Calibrated and Sharper Probabilistic Forecasts

Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In order to enhance the quality of the density forecasts, one can mix them with the unconditional density. This paper examines the value of combining conditional density forecasts with the unconditional density. The findings have positive implications for issuing early warnings in different disciplines including economics and meteorology, but UK inflation forecasts are considered as an example.

nlin.CD↗