nep-cmp New Economics Papers
on Computational Economics
Issue of 2015‒03‒13
seven papers chosen by
Stan Miles
Thompson Rivers University

  1. 2011 Social Accounting Matrix for Senegal: By Fofana, Ismaël; Diallo, Mamadou Yaya; Sarr, Ousseynou; Diouf, Abdou
  2. “Regional Forecasting with Support Vector Regressions: The Case of Spain” By Oscar Claveria; Enric Monte; Salvador Torra
  3. “Multiple-input multiple-output vs. single-input single-output neural network forecasting” By Oscar Claveria; Enric Monte; Salvador Torra
  4. “Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques” By Oscar Claveria; Enric Monte; Salvador Torra
  5. The Macroeconomic Impact of Migration: A Simulation-Based Approach By Shyam Gouri Suresh
  6. Multi-Agent Systems as a Tool for Analyzing Path-Dependent Macrodynamics By Mark Setterfield; Shyam Gouri Suresh
  7. “Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis” By Oscar Claveria; Enric Monte; Salvador Torra

  1. By: Fofana, Ismaël; Diallo, Mamadou Yaya; Sarr, Ousseynou; Diouf, Abdou
    Abstract: This document describes the construction of a social accounting matrix (SAM) for Senegal. The SAM is based upon 2011 national accounts statistics and provides a consistent framework for the assessment of growth and employment policies.
    Keywords: National accounting, Finance, Indicators, Macroeconomics, Social Accounting Matrix (SAM), Computable General Equilibrium (CGE) model,
    URL: http://d.repec.org/n?u=RePEc:fpr:ifprid:1417&r=cmp
  2. By: Oscar Claveria (Faculty of Economics, University of Barcelona); Enric Monte (Polytechnic University of Catalunya); Salvador Torra (Faculty of Economics, University of Barcelona)
    Abstract: This study attempts to assess the forecasting accuracy of Support Vector Regression (SVR) with regard to other Artificial Intelligence techniques based on statistical learning. We use two different neural networks and three SVR models that differ by the type of kernel used. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian kernel shows the best forecasting performance. The best predictions are obtained for longer forecast horizons, which suggest the suitability of machine learning techniques for medium and long term forecasting.
    Keywords: Forecasting, support vector regressions, artificial neural networks, tourism demand, Spain JEL classification: C02, C22, C45, C63, E27, R11
    Date: 2015–01
    URL: http://d.repec.org/n?u=RePEc:ira:wpaper:201507&r=cmp
  3. By: Oscar Claveria (Faculty of Economics, University of Barcelona); Enric Monte (Polytechnic University of Catalunya); Salvador Torra (Faculty of Economics, University of Barcelona)
    Abstract: This study attempts to improve the forecasting accuracy of tourism demand by using the existing common trends in tourist arrivals form all visitor markets to a specific destination in a multiple-input multiple-output (MIMO) structure. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the MIMO approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals. When comparing the forecast accuracy of the different models, we find that radial basis function networks outperform multilayer-perceptron and Elman networks.
    Keywords: tourism demand, forecasting, multivariate, multiple-output, artificial neural networks JEL classification: C22, C45, C63, L83, R11
    Date: 2015–01
    URL: http://d.repec.org/n?u=RePEc:ira:wpaper:201502&r=cmp
  4. By: Oscar Claveria (Faculty of Economics, University of Barcelona); Enric Monte (Polytechnic University of Catalunya); Salvador Torra (Faculty of Economics, University of Barcelona)
    Abstract: This study aims to analyze the effects of data pre-processing on the performance of forecasting based on neural network models. We use three different Artificial Neural Networks techniques to forecast tourist demand: a multi-layer perceptron, a radial basis function and an Elman neural network. The structure of the networks is based on a multiple-input multiple-output setting (i.e. all countries are forecasted simultaneously). We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.
    Keywords: artificial neural networks, forecasting, multiple-input multiple-output (MIMO), seasonality, detrending, tourism demand, multilayer perceptron, radial basis function, Elman JEL classification: L83, C53, C45, R11
    Date: 2015–01
    URL: http://d.repec.org/n?u=RePEc:ira:wpaper:201503&r=cmp
  5. By: Shyam Gouri Suresh
    Abstract: This paper studies the impact of migration on welfare in a general equilibrium model using a simulation-based approach. Agents make decisions regarding the acquisition of skill and migration on the basis of their idiosyncratic characteristics and different migration regimes. Migration in turn has an impact on skilled and unskilled wages in migrant sending and receiving economies. The model also incorporates the effect of skill levels and migrations on subsequent technological changes in both economies. The macroeconomic implications of the model are studied under a variety of different parameterizations and the paper analyzes the non-equilibrium dynamics of various immigration policies in terms of various conceptions of welfare. Results indicate that the optimal policy depends greatly on the type of welfare function employed. Notably, under a variety of different parameterizations, if recipient country GDP per capita is used to describe welfare, more open migration policies are optimal whereas if minimizing inequality among recipient country citizens is the primary objective, closed border policies are optimal. For other definitions of welfare, optimal policies are more sensitive to changes in parameters and assumptions. The paper examines the importance of various assumptions and finds that endogenous skill-based technology change, technology transfer, and remittances are among some of the critical assumptions.
    Keywords: Migration, Simulation-Based Model, Welfare Functions, Inequality, General Equilibrium
    JEL: F22 C63 D6
    URL: http://d.repec.org/n?u=RePEc:dav:wpaper:15-01&r=cmp
  6. By: Mark Setterfield; Shyam Gouri Suresh
    Abstract: This paper discusses the concept of path dependence in macrodynamics, and identifies practical difficulties associated with building path-dependent macrodynamic models of the sort that Keynesians and Schumpeterians regard as necessary for the successful study of long-term growth and development. It is suggested that multi-agent systems (MAS) analysis can help address these difficulties, and therefore provides a useful tool for advancing path-dependent macrodynamic analysis. An illustrative example is provided in the form of a MAS model of path-dependent aggregate fluctuations.
    Keywords: Multi-agent systems, agent based models, path dependence, macrodynamics
    JEL: B41 C63 E12 E32 E37 O41
    URL: http://d.repec.org/n?u=RePEc:dav:wpaper:14-11&r=cmp
  7. By: Oscar Claveria (Faculty of Economics, University of Barcelona); Enric Monte (Polytechnic University of Catalunya); Salvador Torra (Faculty of Economics, University of Barcelona)
    Abstract: By means of Self-Organizing Maps we cluster fourteen European countries according to the most suitable way to model their agents’ expectations. Using the financial crisis of 2008 as a benchmark, we distinguish between those countries that show a progressive anticipation of the crisis and those where sudden changes in expectations occur. By mapping the trajectory of economic experts’ expectations prior to the recession we find that when there are brisk changes in expectations before impending shocks, Artificial Neural Networks are more suitable than time series models for modelling expectations. Conversely, in countries where expectations show a smooth transition towards recession, ARIMA models show the best forecasting performance. This result demonstrates the usefulness of clustering techniques for selecting the most appropriate method to model and forecast expectations according to their behaviour.
    Keywords: Business surveys; Self-Organizing Maps; Clustering; Forecasting; Neural networks; Time series models; Nonlinear models JEL classification:C02; C22; C45; C63; E27
    Date: 2015–03
    URL: http://d.repec.org/n?u=RePEc:ira:wpaper:201511&r=cmp

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