nep-for New Economics Papers
on Forecasting
Issue of 2009‒03‒28
eleven papers chosen by
Rob J Hyndman
Monash University

  1. Model selection for forecast combination By Franses, Ph.H.B.F.
  2. Forecast evaluation of small nested model sets. By Kirstin Hubrich; Kenneth D. West
  3. Expert opinion versus expertise in forecasting By Franses, Ph.H.B.F.; McAleer, M.; Legerstee, R.
  4. Does the ROMC have expertise, and can it forecast? By Franses, Ph.H.B.F.; McAleer, M.; Legerstee, R.
  5. Evaluation of the Diachronic Performance of the OECD Macroeconomic Forecasts for Greece By Dikaios Tserkezos
  6. Forecasting Random Walks under Drift Instability By M. Hashem Pesaran; Andreas Pick
  7. Inflation forecasting in the new EU member states. By Olga Arratibel; Christophe Kamps; Nadine Leiner-Killinger
  8. Optimal Prediction Pools. By John Geweke; Gianni Amisano
  9. Outliers and judgemental adjustment of time series forecasts. By Franses, Ph.H.B.F.
  10. Agricultural Decision Analysis: The Causal Challenge By Parton, Kevin A.
  11. Modelling sustainable international tourism demand to the Brazilian Amazon By Divino, J. A.; McAleer, M.

  1. By: Franses, Ph.H.B.F. (Erasmus Econometric Institute)
    Abstract: In this paper it is advocated to select a model only if it significantly contributes to the accuracy of a combined forecast. Using hold-out-data forecasts of individual models and of the combined forecast, a useful test for equal forecast accuracy can be designed. An illustration for real-time forecasts for GDP in the Netherlands shows its ease of use.
    Keywords: forecast combination;model selection
    Date: 2008–06–01
  2. By: Kirstin Hubrich (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.); Kenneth D. West (University of Wisconsin, Madison, Department of Economics,  1180 Observatory Drive,  Madison, WI 53706, USA.)
    Abstract: We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the benchmark to all the alternative models simultaneously rather than sequentially, and do not require reestimation of models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White’s (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate, our procedures by comparing forecasts of different models for U.S. inflation. JEL Classification: C32, C53, E37.
    Keywords: Out-of-sample, prediction, testing, multiple model comparisons, inflation forecasting.
    Date: 2009–03
  3. By: Franses, Ph.H.B.F.; McAleer, M.; Legerstee, R. (Erasmus Econometric Institute)
    Abstract: Expert opinion is an opinion given by an expert, and it can have significant value in forecasting key policy variables in economics and finance. Expert forecasts can either be expert opinions, or forecasts based on an econometric model. An expert forecast that is based on an econometric model is replicable, and can be defined as a replicable expert forecast (REF), whereas an expert opinion that is not based on an econometric model can be defined as a non-replicable expert forecast (Non-REF). Both replicable and non-replicable expert forecasts may be made available by an expert regarding a policy variable of interest. In this paper we develop a model to generate replicable expert forecasts, and compare REF with Non-REF. A method is presented to compare REF and Non-REF using efficient estimation methods, and a direct test of expertise on expert opinion is given. The latter serves the purpose of investigating whether expert adjustment improves the model-based forecasts. Illustrations for forecasting pharmaceutical SKUs, where the econometric model is of (variations of) the ARIMA type, show the relevance of the new methodology proposed in the paper. In particular, experts possess significant expertise, and expert forecasts are significant in explaining actual sales.
    Keywords: direct test;efficient estimation;expert opinion;replicable expert;forecasts;generated regressors;non-replicable expert forecast
    Date: 2008–11–24
  4. By: Franses, Ph.H.B.F.; McAleer, M.; Legerstee, R. (Erasmus Econometric Institute)
    Abstract: The primary purpose of the paper is to answer the following two questions regarding the performance of the influential Federal Open Market Committee (FOMC) of the Federal Reserve System, in comparison with the forecasts contained in the “Greenbooks†of the professional staff of the Board of Governors: Does the FOMC have expertise, and can it forecast better than the staff? The FOMC forecasts that are analyzed in practice are non-replicable forecasts. In order to evaluate such forecasts, this paper develops a model to generate replicable FOMC forecasts, and compares the staff forecasts, non-replicable FOMC forecasts, and replicable FOMC forecasts, considers optimal forecasts and efficient estimation methods, and presents a direct test of FOMC expertise on non-replicable FOMC forecasts. The empirical analysis of Romer and Romer (2008) is re-examined to evaluate whether their criticisms of the FOMC’s forecasting performance should be accepted unreservedly, or might be open to alternative interpretations.
    Keywords: direct test of FOMC expertise;FOMC forecast;forecast performance;replicable FOMC;staff forecast
    Date: 2008–12–01
  5. By: Dikaios Tserkezos (Department of Economics, University of Crete, Greece)
    Abstract: A variety of standard forecasting accuracy criteria and one suggestion are applied to evaluate the OECD's macroeconomic forecasts for Greece for the aggregate demand and output, the GDP implicit price deflator, the investment, the imports and the exports of goods and services. Every year and half-year the OECD provides projections for these variables published in the OECD Economic Outlook. Because these projections are used extensively by governmental and nongovernmental organizations, it is useful to examine their accuracy. Among some ¡traditional¢ forecasting performance criteria another forecasting criterion is suggested in order to take into account the diachronic adjustment process between the forecasts supplied by OECD and the actual data the last 27 years. According to our results, irrespective of how accurate are the OECD¢s forecasts, there is certainly much room for further improvement. As predictors of direction the OECD's six-month ahead forecasts should be considered valuable; this cannot be said for forecasts which look ahead a year and 18 months.
    Keywords: OECD Forecasts Accuracy, Greek Economy, Diachronic Adjustment Speed, Distributed Lags model, Monte Carlo Experiments.
    JEL: E17 E37 F17 F47
    Date: 2009–03–19
  6. By: M. Hashem Pesaran; Andreas Pick
    Abstract: This paper considers forecast averaging when the same model is used but estimation is carried out over different estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or more structural breaks. It is shown that compared to using forecasts based on a single estimation window, averaging over estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks. Similar results are also obtained when observations are exponentially down-weighted, although in this case the performance of forecasts based on exponential down-weighting critically depends on the choice of the weighting coefficient. The forecasting techniques are applied to 20 weekly series of stock market futures and it is found that average forecasting methods in general perform better than using forecasts based on a single estimation window. 
    Date: 2009–03
  7. By: Olga Arratibel (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.); Christophe Kamps (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.); Nadine Leiner-Killinger (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.)
    Abstract: To the best of our knowledge, our paper is the first systematic study of the predictive power of monetary aggregates for future inflation for the cross section of New EU Member States. This paper provides stylized facts on monetary versus non-monetary (economic and fiscal) determinants of inflation in these countries as well as formal econometric evidence on the forecast performance of a large set of monetary and non-monetary indicators. The forecast evaluation results suggest that, as has been found for other countries before, it is difficult to find models that significantly outperform a simple benchmark, especially at short forecast horizons. Nevertheless, monetary indicators are found to contain useful information for predicting inflation at longer (3-year) horizons. JEL Classification: C53, E31, E37, E51, E52, E62, P24
    Keywords: Inflation forecasting, leading indicators, monetary policy, information content of money, fiscal policy, New EU Member States
    Date: 2009–02
  8. By: John Geweke (Departments of Statistics and Economics, University of Iowa, Iowa City, IA, USA.); Gianni Amisano (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.)
    Abstract: A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave function of the weights and, in general, an optimal linear combination will include several models with positive weights despite the fact that exactly one model has limiting posterior probability one. The paper derives several interesting formal results: for example, a prediction model with positive weight in a pool may have zero weight if some other models are deleted from that pool. The results are illustrated using S&P 500 returns with prediction models from the ARCH, stochastic volatility and Markov mixture families. In this example models that are clearly inferior by the usual scoring criteria have positive weights in optimal linear pools, and these pools substantially outperform their best components. JEL Classification: C11, C53.
    Keywords: forecasting, GARCH, log scoring, Markov mixture, model combination, S&P 500 returns, stochastic volatility.
    Date: 2009–03
  9. By: Franses, Ph.H.B.F. (Erasmus Econometric Institute)
    Abstract: This paper links judgemental adjustment of model-based forecasts with the potential presence of exceptional observations in time series. Specific attention is given to current and future additive outliers, as these require most consideration. A brief illustration to a quarterly real GDP series demonstrates various issues. The main focus of the paper is on various testable propositions, which should facilitate the creation and the evaluation of judgemental adjustment of time series forecasts.
    Date: 2008–03–18
  10. By: Parton, Kevin A.
    Abstract: The paper sets out the agenda for reviewing models of decision making in the context of farmers’ use of seasonal climate forecasting. Such forecasts have been framed in terms of shifts in cumulative distribution functions of yields or gross margins. Typically they have been applied to choices about crop variety, crop type, time of planting or level of fertiliser application. Fundamental questions are: how do farmers conceptualise and make use of the information contained in seasonal climate forecasts? Do our models of decision making represent well the way in which these decisions are made?
    Keywords: decision making, farmers, seasonal climate forecasts, conceptualisation, review,
    Date: 2009
  11. By: Divino, J. A.; McAleer, M. (Erasmus Econometric Institute)
    Abstract: The Amazon rainforest is one of the world’s greatest natural wonders and holds great importance and significance for the world’s environmental balance. Around 60% of the Amazon rainforest is located in the Brazilian territory. The two biggest states of the Amazon region are Amazonas (the upper Amazon) and Pará (the lower Amazon), which together account for around 73% of the Brazilian Legal Amazon, and are the only states that are serviced by international airports in Brazil’s North region. The purpose of this paper is to model and forecast sustainable international tourism demand for the states of Amazonas, Pará, and the aggregate of the two states. Economic progress of the region has been achieved at a cost of destroying large areas of the Amazon rain forest. In this scenario, the tourism industry would seem to have the potential to contribute to sustainable economic development in the North region of Brazil. The paper presents unit root tests for monthly and annual data, estimates alternative time series models and conditional volatility models of the shocks to international tourist arrivals, and provides forecasts for 2006 and 2007.
    Keywords: Brazilian Amazon;international tourism demand;time series modelling;conditional volatility models;forecasting
    Date: 2008–11–10

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