Cost-benefit analysis of alien vegetation clearing for water yield and tourism in a mountain catchment in the Western Cape of South Africa

Economic analysis is used to assess the costs and benefits of restoration following clearing of invasive alien trees in the floristically rich Fynbos mountainous area near Franschhoek, Western Cape of South Africa. The Groot Drakenstein, Franschhoek and Jonkershoek mountains receives more rainfall than the surrounding areas and is an important source of water for the city of Cape Town. The costs of alien invasive plant removal, gully-erosion repair and reseeding with indigenous plants are considered in a case-study cost-benefit analysis of restoration, in terms of the water and tourism benefits derived. Three different options of restoration (comprehensive, moderate, basic) were analysed under three different economic scenarios (optimistic, realistic, pessimistic) and the costs of which have been weighted up against the income derived from the supply of water and tourism. The results have shown that despite the high costs of restoration, the basic restoration option costs were out-weighed by the water and tourism benefits derived. This was also true of the moderate restoration option, when evaluated under the optimistic scenario and using an 8% discount rate, or a 3% discount rate under any scenario. However, this was not the case in the moderate restoration option when using an 8% discount rate in conjunction with the realistic and pessimistic scenarios. Neither was it the case when using a 12% discount rate, irrespective of the scenario. Under no scenario was the cost of a comprehensive restoration option outweighed by the benefits quantified, irrespective of the discount rate used. It was concluded that further restoration, in addition to the mere clearing of alien invasive plants, would be economically viable under certain assumptions and conditions.     

An empirical procedure to evaluate monetary management under exogenous changes in the money supply

This article proposes an empirical procedure to evaluate central banks’ monetary management in a presence of exogenous changes in the money supply. Monetary shocks deviate the market interest rate from the target, and the monetary authority decides its optimal intervention in the money market, bearing in mind the benefits and costs of re-establishing its target interest rate. According to monetary management theory, typically a central bank will allow for variation in the interest rate within a range around the target interest rate, thereby intervening in the money market when the interest rate trends toward a point outside that range. In this context, we develop an empirical strategy to analyse central bank’s reactions to exogenous money changes by making a statistical comparison of the actual and the estimated intraday shift in the money supply. We also employ our method to test the reactions of the Brazilian Central Bank to liquidity shifts caused by changes in the Treasury Single Account (TSA) balance. Using different metrics of analysis, the applications of our procedure confirms the predictions of the optimal monetary management theory.     

Feedforward versus recurrent neural networks for forecasting monthly japanese yen exchange rates

Neural networks are a relatively new computer artificial intelligence method which attempt to mimic the brain's problem solving process and can be used for predicting nonlinear economic time series. Neural networks are used to look for patterns in data, learn these patterns, and then classify new patterns and make forecasts. Feedforward neural networks pass the data forward from input to output, while recurrent networks have a feedback loop where data can be fed back into the input at some point before it is fed forward again for further processing and final output. Some have argued that since time series data may have autocorrelation or time dependence, the recurrent neural network models which take advantage of time dependence may be useful. Feedforward and recurrent neural networks are used for comparison in forecasting the Japanese yen/US dollar exchange rate. A traditional ARIMA model is used as a benchmark for comparison with the neural network models.Results for out of sample show that the feedforward model is relatively accurate in forecasting both price levels and price direction, despite being quite simple and easy to use. However, the recurrent network forecast performance was lower than that of the feedforward model. This may be because feed forward models must pass the data from back to forward as well as forward to back, and can sometimes become confused or unstable. Both the feedforward and recurrent models performed better than the ARIMA benchmark model.The author wish to thank the reviewers Drs. Kraft and Radford for their helpful comments.     

U.S. tourism in Mexico: An empirical analysis

This study estimated demand functions for tourism by U.S. residents in Mexico border areas, the Mexican interior, and overseas. There was no evidence that U.S. tourists substitute Mexico for more distant destinations as transportation costs rise. Border tourism is income elastic when the share of U.S. income of the border states is held fixed. Tourism in the Mexican interior is also income elastic. Tourism along the border is price elastic, while tourism in the interior is elastic with respect to U.S. and overseas prices but inelastic with respect to Mexican prices. The rise in the share of the Mexican interior in U.S. overseas tourism is not related to price factors. Recent devaluations of the Mexican peso are unlikely to provide benefits to the Mexican tourism industry.