nep-cmp New Economics Papers
on Computational Economics
Issue of 2011‒06‒18
twelve papers chosen by
Stan Miles
Thompson Rivers University

  2. A NOVEL ARTIFICIAL NEURAL NETWORK MODEL FOR EXCHANGE RATE FORECASTING By Prof. Dr. Abu Hassan Shaari Md Nor; Behrooz Gharleghi; Prof. Dr. Khairuddin Omar; Dr. Tamat Sarmidi
  3. Forecasting Photovoltaic Deployment with Neural Networks By Crescenzio Gallo; Michelangelo De Bonis
  4. Pricing of average strike Asian call option using numerical PDE methods By Abhishek Kumar; Ashwin Waikos; Siddhartha P. Chakrabarty
  5. Volatility of world rice prices, import tariffs and poverty in Indonesia: a CGE-microsimulation analysis By Teguh, Dartanto
  6. Under what conditions does a carbon tax on fossil fuels stimulate biofuels ? By Timilsina, Govinda R.; Csordas, Stefan; Mevel, Simon
  7. The Effect of Climate Change on Land Use and Wetlands Conservation in Western Canada: An Application of Positive Mathematical Programming By Patrick Withey; G. Cornelis van Kooten
  8. Efficient and accurate log-L\'evy approximations to L\'evy driven LIBOR models By Antonis Papapantoleon; John Schoenmakers; David Skovmand
  9. Impact assessment of interregional government transfers in Brazil: an input-output approach By Luque, Carlos A.; Haddad, Eduardo A.; Lima, Gilberto T.; Sakurai, Sergio N.; Costa, Silvio M.
  10. Precautionary Savings and Wealth Inequality: a Global Sensitivity Analysis By Marco Cozzi
  11. A comparative analysis of alternative univariate time series models in forecasting Turkish inflation By Catik, A. Nazif; Karaçuka, Mehmet
  12. Climate Policy Design with Correlated Uncertainties in Offset Supply and Abatement Cost By Fell, Harrison; Burtraw, Dallas; Morgenstern, Richard; Palmer, Karen

  1. By: Saratha Sathasivam (School of Mathematical Sciences, Universiti Sains Malaysia)
    Abstract: Neural Networks represent a meaningfully different approach to using computers in the workplace. A neural network is used to learn patterns and relationships in data. The data may be the results of a market research effort, or the results of a production process given varying operational conditions. Regardless of the specifics involved, applying a neural network is a substantial departure from traditional approaches. In this paper we will look into how neural networks is used in data mining. The ultimate goal of data mining is prediction - and predictive data mining is the most common type of data mining and one that has the most direct business applications. Therefore, we will consider how this technique can be used to classify the performance status of a departmental store in monitoring their products
    Keywords: Neural networks, data mining, prediction
    JEL: M0
    Date: 2011–03
  2. By: Prof. Dr. Abu Hassan Shaari Md Nor; Behrooz Gharleghi (Faculty of Economic and Management, Universiti Kebangsaan Malaysia); Prof. Dr. Khairuddin Omar (Faculty of Science Information and Technology, Universiti Kebangsaan Malaysia); Dr. Tamat Sarmidi (Faculty of Economic and Management, Universiti Kebangsaan Malaysia)
    Abstract: Financial systems, such as the exchange rate market, are generally complex systems with limited information about the underlying mechanisms governing the data. Such systems are often characterised as ‘‘black boxes” and hence the internal mechanisms and relations among the elements of the system can be ignored while concentrating on the study of the relationship between the system’s input(s)/output(s). Financial systems are also generally assumed to be nonlinear systems, which increase the level of difficulty in accurately predicting their behaviour. Despite these difficulties, the financial implications of accurate prediction of the financial markets’ movements have encouraged researchers and practitioners to employ a variety of modelling methods
    Keywords: ANNs, ARIMA, hybrid models
    JEL: C22 C45 C53
    Date: 2011–03
  3. By: Crescenzio Gallo; Michelangelo De Bonis
    Abstract: The photovoltaic (PV) industry in Italy has already crossed the threshold of 1 GW of installed capacity. Currently there are approximately 70,000 certified facilities in operation for a power generation of 1,300 GWh/year. With these figures, Italy has become the second country in Europe for PV installed power after Germany. The energy produced would be sufficient to meet the power needs of approximately 1,200,000 people. This leads to some questions: Will this technology continue to grow exponentially even after the recent reduction in rates by the Energy Bill? Will the number of installed PV facilities still grow even with less public support and (probably) a reduction in the technology purchase price? The purpose of this paper is therefore to develop a conceptual model to make a prediction of the PV installed power in Italy through the use of “supervised” artificial neural networks. This model is also applied to the analysis of the spread of this technology in some other European countries.
    Keywords: photovoltaic, forecasting, neural networks.
    Date: 2011–03
  4. By: Abhishek Kumar; Ashwin Waikos; Siddhartha P. Chakrabarty
    Abstract: In this paper, a standard PDE for the pricing of arithmetic average strike Asian call option is presented. A Crank-Nicolson Implicit Method and a Higher Order Compact finite difference scheme for this pricing problem is derived. Both these schemes were implemented for various values of risk free rate and volatility. The option prices for the same set of values of risk free rate and volatility was also computed using Monte Carlo simulation. The comparative results of the two numerical PDE methods shows close match with the Monte Carlo results, with the Higher Order Compact scheme exhibiting a better match. To the best of our knowledge, this is the first work to use the numerical PDE approach for pricing Asian call options with average strike.
    Date: 2011–06
  5. By: Teguh, Dartanto
    Abstract: This study aims at measuring the impact of world price volatility and import tariffs on rice on poverty in Indonesia. Applying a Computable General Equilibrium-Microsimulation approach and the endogenous poverty line, this study found that the volatility of world rice prices during 2007 to 2010 had a large effect on the poverty incidence in Indonesia. The simulation result showed that a 60 per cent increase in world rice price raises the head count index by 0.81 per cent which is equivalent to an increase in the number of poor by 1,687,270. However, both the 40 per cent decrease in the effective import tariffs on rice enacted by regulation No.93/PMK.011/2007 and the zero import tariffs implemented by regulation No. 241/PMK.011/2010 in response to high world rice prices could not perfectly absorb the negative impact of increasing world rice prices on poverty. The 40 per cent decrease in the effective import tariffs on rice reduced the head count index by 0.08 per cent equal to 161,546 people while the zero import tariffs on rice reduced the head count index by 0.19 per cent equal to 390,160 people. These policies might not be enough to absorb the negative impact of an increase in world rice prices from 2007-2010, because, during this period, the world rice prices increased on average by almost 71 per cent, which have impoverished approximately two million people. Moreover, protection in the agricultural sector, such as raising import tariffs, intended to help agricultural producers will have the reverse effect of raising the head count index.
    Keywords: Rice Policy; Import Tariffs; Poverty; CGE; Microsimulation.
    JEL: D12 D58 I32 Q18
    Date: 2010–12
  6. By: Timilsina, Govinda R.; Csordas, Stefan; Mevel, Simon
    Abstract: A carbon tax is an efficient economic instrument to reduce emissions of carbon dioxide released from fossil fuel burning. Its impacts on production of renewable energy depend on how it is designed -- particularly in the context of the penetration of biofuels into the energy supply mix for road transportation. Using a multi-sector, multi-country computable general equilibrium model, this study shows first that a carbon tax with the entire tax revenue recycled to households through a lump-sum transfer does not stimulate biofuel production significantly, even at relatively high tax rates. This reflects the high cost of carbon dioxide abatement through biofuels substitution, relative to other energy substitution alternatives; in addition, the carbon tax will have negative economy-wide consequences that reduce total demand for all fuels. A combined carbon tax and biofuel subsidy policy, where part of the carbon tax revenue is used to finance a biofuel subsidy, would significantly stimulate market penetration of biofuels. Although the carbon tax and biofuel subsidy policy would cause higher loss in global economic output compared with the carbon tax with lump sum revenue redistribution, the incremental output loss is relatively small.
    Keywords: Climate Change Mitigation and Green House Gases,Transport Economics Policy&Planning,Taxation&Subsidies,Environment and Energy Efficiency,Energy and Environment
    Date: 2011–06–01
  7. By: Patrick Withey; G. Cornelis van Kooten
    Abstract: This study examines the impact of climate change on land use in the Prairie Pothole Region of Western Canada, with particular emphasis on how climate change will impact wetlands. A multi-region Positive Mathematical Programming model calibrates land use in the area to observed acreage in 2006. Policy simulations for both climate effects as well as the effects of biofuel policies determine how climate change will affect land use and wetlands. Given that the model calibrates to observed acreage, the policies provide a realistic view of how land use might change from current levels, given the effects of climate change. Results indicate that climate change could decrease wetlands in this area by as much as 50 percent. The effect will be very different depending on whether or not the social benefits of wetlands are considered, and the effects of climate change on wetlands are heterogeneous across the Prairie Provinces.
    Keywords: Positive mathematical programming; wetlands conservation; land use change; climate change; biofuels; Prairie pothole region
    JEL: C02 C63 Q15 Q54 Q57 Q24 Q25
    Date: 2011–04
  8. By: Antonis Papapantoleon; John Schoenmakers; David Skovmand
    Abstract: The LIBOR market model is very popular for pricing interest rate derivatives, but is known to have several pitfalls. In addition, if the model is driven by a jump process, then the complexity of the drift term is growing exponentially fast (as a function of the tenor length). In this work, we consider a L\'evy-driven LIBOR model and aim at developing accurate and efficient log-L\'evy approximations for the dynamics of the rates. The approximations are based on truncation of the drift term and Picard approximation of suitable processes. Numerical experiments for FRAs, caps and swaptions show that the approximations perform very well. In addition, we also consider the log-\lev approximation of annuities, which offers good approximations for high volatility regimes.
    Date: 2011–06
  9. By: Luque, Carlos A.; Haddad, Eduardo A.; Lima, Gilberto T.; Sakurai, Sergio N.; Costa, Silvio M.
    Abstract: Redistributive policies carried out by the central government through interregional government transfers is a relevant feature of the Brazilian federal fiscal system. Regional shares of the central government revenues in the poorer regions have been recurrently smaller than the shares of central government expenditures in those regions. Appeal to core-periphery outcomes could be made, as São Paulo, the wealthiest state in the country, concentrated, in 2005, over 40% of total Federal tax revenue, receiving less than 35% of Federal expenditures. These figures suggest a redistribution of public funds from the spatial economic core of the economy to the peripheral areas. This paper investigates the role interregional transfers play in the redistribution of activities in the country, using an interregional input-output approach. Counterfactual simulations allow us to estimate some costs and benefits, for the core and periphery respectively, from such fiscal mechanisms.
    Keywords: Interregional government transfers; input-output analysis; impact analysis; Brazilian economy
    JEL: H77 H5 R15
    Date: 2011–05
  10. By: Marco Cozzi (Queen’s University)
    Abstract: This paper applies Canova JAE 1994 methodology to perform a thorough sensitivity analysis for the Aiyagari QJE 1994 economy. This is a calibrated GE model with incomplete markets and uninsurable income risk, designed to quantify the size of precautionary savings and the degree of wealth inequality. The results of this global robustness analysis are broadly consistent with Aiyagari’s findings. Even when considering priors for the parameters uncertainty which are highly dispersed, the size of the precautionary savings is modest: at most, they account for an 11% increase in the saving rate. However, the results show that the parameter representing the exogenous borrowing limit seems to lead to relatively large changes in measures of wealth inequality. The Gini index increases by 15 points when considering values of the borrowing limits that lead to empirically plausible shares of households with a negative net worth. The parameters that quantitatively have the largest effects on determining the wealth Gini index are the capital share, the borrowing limit, and the depreciation rate. The parameters affecting most significantly precautionary savings are the risk aversion and the standard deviation of the income shocks.
    Keywords: Precautionary Savings, Calibration, Heterogeneous Agents, Incomplete Markets, Computable General Equilibrium, Monte Carlo
    JEL: E21 D52 D58
    Date: 2011–06
  11. By: Catik, A. Nazif; Karaçuka, Mehmet
    Abstract: This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflation in Turkey has time varying pattern and conventional models are not able to track underlying trend of inflation in the long run. --
    Keywords: Inflation forecasting,Neural networks,Unobserved components model
    JEL: C45 C53 E31 E37
    Date: 2011
  12. By: Fell, Harrison (Resources for the Future); Burtraw, Dallas (Resources for the Future); Morgenstern, Richard (Resources for the Future); Palmer, Karen (Resources for the Future)
    Abstract: Current and proposed greenhouse gas cap-and-trade systems allow regulated entities to offset abatement requirements by paying unregulated entities to abate. These offsets from unregulated entities are believed to contain system costs and stabilize allowance prices. However, the supply of offsets is highly uncertain and may be correlated with other sources of uncertainty in emissions trading systems. This paper presents a model that incorporates both uncertainties in the supply of offsets and in abatement costs. We numerically solve a dynamic stochastic model, with parameters relevant to the U.S. climate debate, under a variety of parameter settings, including a system that includes allowance price controls, risk aversion, and competitive offset purchasing. We find that as uncertainty in offsets and uncertainty in abatement costs become more negatively correlated, expected abatement plus offset purchase costs increase, as does the variability in allowance prices and emissions from the regulated sector. These results are amplified with risk sensitivity, larger annual offset limits, and competitive offset purchasing. Imposing an allowance price collar substantially mitigates cost increases as well as the variability in prices, while roughly maintaining expected environmental outcomes. In contrast with previous literature we find a collar may also mitigate emissions variability.
    Keywords: climate change, offsets, cap-and-trade, price collars, stochastic dynamic programming
    JEL: Q54 Q58 C61
    Date: 2011–06–09

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