nep-for New Economics Papers
on Forecasting
Issue of 2022‒03‒28
four papers chosen by
Rob J Hyndman
Monash University

  1. A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction By Verena Monschang; Bernd Wilfling
  2. Forecasting Market Diffusion of Innovative Battery-Electric and Conventional Vehicles in Germany under Model Uncertainty By Andreas Marcus Gohs
  3. Ensemble and Multimodal Approach for Forecasting Cryptocurrency Price By Zeyd Boukhers; Azeddine Bouabdallah; Matthias Lohr; Jan J\"urjens
  4. Optimal Forecast under Structural Breaks By Tae-Hwy Lee; Shahnaz Parsaeian; Aman Ullah

  1. By: Verena Monschang; Bernd Wilfling
    Abstract: We investigate mean-squared-forecast-error (MSE) accuracy improvements for linear-convex combination forecasts, whose components are pretreated by a procedure called 'Vector Autoregressive Forecast Error Modeling' (VAFEM). Assuming that the fore-cast-error series of the individual forecasts are governed by a stable VAR process under classic conditions, we obtain the following results: (i) VAFEM treatment bias-corrects all individual and linear-convex combination forecasts. (ii) Any VAFEM-treated combination has smaller theoretical MSE than its untreated analogue, if the VAR parameters are known. (iii) In empirical applications, VAFEM gains depend on (1) in-sample sizes, (2) out-of-sample forecast horizons, (3) the biasedness of the untreated forecast combination. We demonstrate the VAFEM capacity for realized-volatility forecasting, using S&P 500 data.
    Keywords: Combination forecasts, mean-squared-error loss, VAR forecast-error molding, multivariate least squares estimation
    JEL: C10 C32 C51 C53
    Date: 2022–03
  2. By: Andreas Marcus Gohs (University of Kassel)
    Abstract: In this research paper accuracies (percentage errors, MAPE) of different procedures (growth, ARIMA(X), exponential smoothing and deterministic trend models) in forecasting new passenger car registrations in Germany are presented. It is found that the Logistic Growth Model provides rather accurate predictions of the number of new registrations (total number, which still refers to predominantly conventional gasoline and diesel vehicles) for the forecast period of the study. However, the Bass diffusion model is recommended for predicting the new registration numbers of the innovative battery-electric technology. Furthermore, it is exemplarified that the Bass coefficient of imitation q, in contrast to the coefficient of innovation p, is robust to a variation of the assumed market potential M. Therefore, q should also contribute to a stable short-term forecast (given a variation of M), provided that a period in the early phase of the product life cycle is considered. The study also shows that with the bulk of the procedures, percentage forecast errors are obtained which lie in a narrow margin for the established product passenger car, but not for the innovative battery-electric propulsion technology. So while the careful selection of the forecasting model seems rather negligible for the established product, it is essential for the innovative product. In addition, new registration figures in the German federal states were forecasted, which in turn were used to calculate pooled forecasts for Germany. In general, no increase in forecast accuracy was achieved by means of pooling compared with direct forecasting (i.e. from the national time series).
    Keywords: Growth Curves, Bass Diffusion Model, Pooled Forecasting, Model Uncertainty, Electric Vehicles
    JEL: C22 C53 O33
    Date: 2022
  3. By: Zeyd Boukhers; Azeddine Bouabdallah; Matthias Lohr; Jan J\"urjens
    Abstract: Since the birth of Bitcoin in 2009, cryptocurrencies have emerged to become a global phenomenon and an important decentralized financial asset. Due to this decentralization, the value of these digital currencies against fiat currencies is highly volatile over time. Therefore, forecasting the crypto-fiat currency exchange rate is an extremely challenging task. For reliable forecasting, this paper proposes a multimodal AdaBoost-LSTM ensemble approach that employs all modalities which derive price fluctuation such as social media sentiments, search volumes, blockchain information, and trading data. To better support investment decision making, the approach forecasts also the fluctuation distribution. The conducted extensive experiments demonstrated the effectiveness of relying on multimodalities instead of only trading data. Further experiments demonstrate the outperformance of the proposed approach compared to existing tools and methods with a 19.29% improvement.
    Date: 2022–02
  4. By: Tae-Hwy Lee (Department of Economics, University of California Riverside); Shahnaz Parsaeian (University of Kansas); Aman Ullah (University of California Riverside)
    Abstract: This paper develops an optimal combined estimator to forecast out-of-sample under structural breaks. When it comes to forecasting, using only the post-break observations after the most recent break point may not be optimal. In this paper we propose a new estimation method that exploits the pre-break information. In particular, we show how to combine the estimator using the full-sample (i.e., both the pre-break and post-break data) and the estimator using only the post-break sample. The full-sample estimator is inconsistent when there is a break while it is efficient. The post-break estimator is consistent but inefficient. Hence, depending on the severity of the breaks, the full-sample estimator and the post-break estimator can be combined to balance the consistency and efficiency. We derive the Stein-like combined estimator of the full-sample and the post-break estimators, to balance the bias-variance trade-off. The combination weight depends on the break severity, which we measure by the Wu-Hausman statistic. We examine the properties of the proposed method, analytically in theory, numerically in simulation, and also empirically in forecasting real output growth across nine industrial economies.
    Keywords: Forecasting, Structural breaks, Stein-like combined estimator, Output growth
    JEL: C13 C32 C53
    Date: 2022–02

This nep-for issue is ©2022 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.