nep-for New Economics Papers
on Forecasting
Issue of 2007‒11‒17
four papers chosen by
Rob J Hyndman
Monash University

  1. Bayesian Methods of Forecasting Inventory Investment in South Africa By Rangan Gupta
  2. Forecasting the South African Economy with Gibbs Sampled BVECMs By Samuel Zita; Rangan Gupta
  3. Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges By Ketter, W.; Collins, J.; Gini, M.; Gupta, A.; Schrater, P.
  4. The Role of No-Arbitrage on Forecasting: Lessons from a Parametric Term Structure Model By Caio Almeida; José Vicente

  1. By: Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: This paper develops a Bayesian Vector Error Correction Model (BVECM) for forecasting inventory investment in South Africa. The model is estimated using quarterly data on actual sales, production, unfilled orders, price levels and interest rates, for the period of 1978 to 2000. The out-of-sample-forecast accuracy obtained from the BVECM, over the forecasting horizon of 2001:1 to 2003:4, is compared with those generated from the Classical variant of the VAR and the VECM, the Bayesian VAR, and the ECM of inventory investment developed by Smith et al. (2006) for the South African economy. The BVECM with the most tight prior outperforms all the other models, except for a relatively tight BVAR. This BVAR model also correctly predicts the direction of change of inventory investment over the period of 2004:1 to 2006:3.
    Keywords: VECM and BVECM, VAR and BVAR Model, Forecast Accuracy, BVECM Forecasts, VECM Forecasts, BVAR Forecasts, ECM Forecasts, VAR Forecasts
    JEL: E17 E27 E37 E47
    Date: 2007–02
  2. By: Samuel Zita (Department of Economics, University of Pretoria); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: The paper uses Gibbs sampling technique to estimate a heteroscedastic Bayesian Vector Error Correction Model (BVECM) of the South African economy for the period 1970:1-2000:4, and then forecast GDP, consumption, investment, short and long term interest rates, and the CPI over the period of 2001:1 to 2005:4. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out-of-sample-forecast accuracy resulting from the Gibbs sampled BVECM is compared with those generated from a Classical VECM and a homoscedastic BVECM. The homoscedastic BVECM is found to produce the most accurate out of sample forecasts.
    Keywords: Forecast Accuracy, Metical-Rand Exchange Rate, Random Walk, Sticky-Price Model, VAR Forecasts, VECM Forecasts
    JEL: B23 C22 F31 E17 E27 E37 E47
    Date: 2007–02
  3. By: Ketter, W.; Collins, J.; Gini, M.; Gupta, A.; Schrater, P. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
    Abstract: We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.
    Keywords: Trading agents;machine learning;market forecasting;dynamic pricing;
    Date: 2007–10–19
  4. By: Caio Almeida; José Vicente
    Date: 2007–10

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