nep-for New Economics Papers
on Forecasting
Issue of 2008‒02‒16
eight papers chosen by
Rob J Hyndman
Monash University

  1. A Note on Long Horizon Forecasts of Nonlinear Models of Real Exchange Rates: Comments on Rapach and Wohar (2006) By Daniel Buncic
  2. Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change By Anindya Banerjee; Massimiliano Marcellino; Igor Masten
  3. CAN EXCHANGE RATES FORECAST COMMODITY PRICES? By Chen, Yu-chin; Rogoff, Kenneth; Rossi, Barbara
  4. Modeling Tick-by-Tick Realized Correlations By Fulvio Corsi; Francesco Audrino
  5. Effect of noise filtering on predictions : on the routes of chaos. By Dominique Guegan
  6. Electricity Demand for Sri Lanka: A Time Series Analysis By Himanshu A. Amarawickrama; Lester C. Hunt
  7. US Inflation Dynamics 1981-2007: 13,193 Quarterly Observations By Gregor W. Smith
  8. The Use of Pseudo Panel Data for Forecasting Car Ownership By Huang, Biao

  1. By: Daniel Buncic (School of Economics, The University of New South Wales)
    Abstract: We show that long horizon forecasts from the nonlinear models that are considered in the study by Rapach and Wohar (2006) cannot generate any forecast gains over a simple AR(1) specification. This is contrary to the findings reported in Rapach and Wohar (2006). Moreover, we illustrate graphically that the nonlinearity in the forecasts from the ESTAR model is the strongest when forecasting one step-ahead and that it diminishes as the forecast horizon increases. There exists, therefore, no potential whatsoever for the considered nonlinear models to outperform linear ones when forecasting far ahead. We also illustrate graphically why one step-ahead forecasts from the nonlinear ESTAR model fail to yield superior predictions to a simple AR(1).
    Keywords: PPP; regime modelling; nonlinear real exchange rate models; ESTAR; forecast evaluation
    JEL: C22 C52 C53 F31 F47
    Date: 2008–02
  2. By: Anindya Banerjee; Massimiliano Marcellino; Igor Masten
    Abstract: We conduct a detailed simulation study of the forecasting performance of diffusion index-based methods in short samples with structural change. We consider several data generation processes, to mimic different types of structural change, and compare the relative forecasting performance of factor models and more traditional time series methods. We find that changes in the loading structure of the factors into the variables of interest are extremely important in determining the performance of factor models. We complement the analysis with an empirical evaluation of forecasts for the key macroeconomic variables of the Euro area and Slovenia, for which relatively short samples are officially available and structural changes are likely. The results are coherent with the findings of the simulation exercise, and confirm the relatively good performance of factor-based forecasts also in short samples with structural change.
    Keywords: Factor models, forecasts, time series models, structural change, short samples, parameter uncertainty
    JEL: C53 C32 E37
    Date: 2008
  3. By: Chen, Yu-chin; Rogoff, Kenneth; Rossi, Barbara
    Abstract: This paper studies the dynamic relationship between exchange rate fluctuations and world commodity price movements. Taking into account parameter instability, we demonstrate surprisingly robust evidence that exchange rates predict world commodity price movements, both in-sample and out-of-sample. Because commodity prices are exogenous to the exchange rates we consider, we are able to overcome the identification problems that plague the existing empirical exchange rate literature. Because our finding that exchange rates predict future commodity prices can be given a true causal interpretation, it provides the most concrete support yet for the importance of selected macroeconomic fundamentals in determining exchange rates. As an important by-product of our analysis, we show that exchange rate-based forecasts may be a viable alternative for predicting future commodity price movements.
    Keywords: Exchange rates, forecasting, commodity prices, random walk
    JEL: C52 C53 F31 F47
    Date: 2008
  4. By: Fulvio Corsi; Francesco Audrino
    Abstract: We propose a tree-structured heterogeneous autoregressive (tree-HAR) process as a simple and parsimonious model for the estimation and prediction of tick-by-tick realized correlations. The model can account for different time and other relevant predictors' dependent regime shifts in the conditional mean dynamics of the realized correlation series. Testing the model on S&P 500 and 30-year treasury bond futures realized correlations, we provide empirical evidence that the tree-HAR model reaches a good compromise between simplicity and flexibility, and yields accurate single- and multi-step out-of-sample forecasts. Such forecasts are also better then those obtained from other standard approaches.
    Keywords: High frequency data, Realized correlation, Stock-bond correlation, Tree-structured models, HAR, Regimes
    JEL: C13 C22 C51 C53
    Date: 2008–01
  5. By: Dominique Guegan (Centre d'Economie de la Sorbonne et Paris School of Economics)
    Abstract: The detection of chaotic behaviors in commodities, stock markets and weather data is usually complicated by large noise perturbation inherent to the underlying system. It is well known, that predictions, from pure deterministic chaotic systems can be accurate mainly in the short term. Thus, it will be important to be able to reconstruct in a robust way the attractor in which evolves the data, if this attractor exists. In chaotic theory, the deconvolution methods have been largely studied and there exist different approaches which are competitive and complementary. In this work, we apply two methods : the singular value method and the wavelet approach. This last one has not been investigated a lot of filtering chaotic systems. Using very large Monte Carlo simulations, we show the ability of this last deconvolution method. Then, we use the de-noised data set to do forecast, and we discuss deeply the possibility to do long term forecasts with chaotic systems.
    Keywords: Deconvolution, chaos, SVD, state space method, Wavelets method.
    JEL: C02 C32 C45 C53
    Date: 2008–01
  6. By: Himanshu A. Amarawickrama (Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey & Infrastructure Advisory, Ernst and Young LLP, London); Lester C. Hunt (Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey)
    Abstract: This study estimates electricity demand functions for Sri Lanka using six econometric techniques. It shows that the preferred specifications differ somewhat and there is a wide range in the long-run price and income elasticities with the estimated long-run income elasticity ranging from 1.0 to 2.0 and the long run price elasticity from 0 to –0.06. There is also a wide range of estimates of the speed with which consumers would adjust to any disequilibrium, although the estimated impact income elasticities tended to be more in agreement ranging from 1.8 to 2.0. Furthermore, the estimated effect of the underlying energy demand trend varies between the different techniques; ranging from being positive to zero to predominantly negative. Despite these differences the forecasts generated from the six models up until 2025 do not differ significantly. Thus on one hand it is encouraging that the Sri Lanka electricity authorities can have some faith in econometrically estimated models used for forecasting. However, by the end of the forecast period in 2025 there is a variation of around 452MW in the base forecast peak demand; which, in relative terms for a small electricity generation system like Sri Lanka’s, represents a considerable difference.
    Keywords: Developing Countries, Electricity Demand Estimation, Sri Lanka
    JEL: Q48 Q41
    Date: 2007–10
  7. By: Gregor W. Smith (Queen's University)
    Abstract: The new Keynesian Phillips curve (NKPC) restricts multivariate forecasts. I estimate and test it entirely within a panel of professional forecasts, thus using the time-series, cross-forecaster, and cross-horizon dimensions of the panel. Estimation uses 13,193 observations on quarterly US inflation forecasts since 1981. The main finding is a significantly larger weight on expected future inflation than on past inflation, a finding which also is estimated with much more precision than in the standard approach. Inflation dynamics also are stable over time, with no decline in inflation inertia from the 1980s to the 2000s. But, as in historical data, identifying the output gap is difficult.
    Keywords: forecast survey, new Keynesian Phillips curve
    JEL: E31 E37 C23
    Date: 2008–02
  8. By: Huang, Biao
    Abstract: While car ownership forecasting has always been a lively area of research, traditionally it was dominated by static models. To utilize the rich and readily available repeated cross sectional data sources and avoid the need for scarce and expensive panel data, this study adopts pseudo panel methods. A pseudo panel dataset is constructed using the Family Expenditure Survey between 1982 and 2000 and a range of econometric models are estimated. The methodological issues associated with the properties of various pseudo panel estimators are also discussed. For linear pseudo panel models, the methodological issues include: the relationship between the pseudo panel estimator and instrumental variable estimator based on individual survey data; the problem of measurement errors (and when they can be ignored) and the consistent estimation of dynamic pseudo panel parameters under different asymptotics. Static and dynamic models of car ownership are estimated and a systematic specification search is carried out to determine the model with best fit. The robustness of the estimator is investigated using parametric bootstrap techniques. As an individual household’s car ownership choice is discrete, limiting the model to linear form is obvious insufficient. This study attempts to combine the pseudo panel approach with discrete choice model, which has the distinctive advantages of allowing both dynamics and saturation but without the need for expensive genuine panel data. This does not seem to have been done before. Under the framework of random utility model (RUM), it is shown that the utility function of the pseudo panel model is a direct transformation from that of cross-sectional model and both share similar probability model albeit with different scale. This study also explores the various forms of true state dependence in the dynamic models and tackles the difficult econometric issues caused by the inclusion of lagged dependent variables. The pseudo panel random utility model is then applied to car ownership modeling, which is subsequently extended to take saturation into account. The model with the best fit has a Dogit structure, which is consistent with the RUM theory and is able to estimate the level of saturation and test its statistical significance. Both linear and discrete choice models are applied to generate forecasts of car ownership in Great Britain to year 2021. While the forecasts based on discrete choice models closely match the observed car stock between 2001 and 2006, those based on linear models appear to be too high. Furthermore, the results from nonlinear models are comparable to the findings in other authoritative studies, while the long term forecasts from linear models are significantly higher. These results highlight the importance of saturation, and hence the choice of model functional form, in car ownership forecasts. In conclusion, we make some comments about the usefulness of pseudo panel models.
    Keywords: pseudo panel; discrete choice model; dynamics; saturation; car ownership
    JEL: C53 C23 C35
    Date: 2007–06

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