nep-for New Economics Papers
on Forecasting
Issue of 2024‒01‒01
five papers chosen by
Rob J Hyndman, Monash University

  1. Carpe Diem: Can daily oil prices improve model-based forecasts of the real price of crude oil? By Amor Aniss Benmoussa, Reinhard Ellwanger, Stephen Snudden
  2. Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data By Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo
  3. Predictive Density Combination Using a Tree-Based Synthesis Function By Tony Chernis; Niko Hauzenberger; Florian Huber; Gary Koop; James Mitchell
  4. ABC-based Forecasting in State Space Models By Chaya Weerasinghe; Ruben Loaiza-Maya; Gael M. Martin; David T. Frazier
  5. A Data-driven Deep Learning Approach for Bitcoin Price Forecasting By Parth Daxesh Modi; Kamyar Arshi; Pertami J. Kunz; Abdelhak M. Zoubir

  1. By: Amor Aniss Benmoussa, Reinhard Ellwanger, Stephen Snudden (Wilfrid Laurier University)
    Abstract: This paper proposes techniques to include information from the underlying nominal daily series in model-based forecasts of average real series. We apply these approaches to forecasts of the real price of crude oil. Models utilizing information from daily prices yield large forecast improvements and, in some cases, almost halve the forecast error compared to current specifications. We demonstrate for the first time that model-based forecasts of the real price of crude oil can outperform the traditional random walk forecast, which is the end-of-month no-change forecast, at short forecast horizons.
    Keywords: Forecasting and Prediction Methods, Temporal Aggregation, Oil Prices
    JEL: C18 C53 Q47
    Date: 2023–12
  2. By: Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo (Wilfrid Laurier University)
    Abstract: Economists often need to forecast temporally aggregated data, such as monthly or quarterly averages. However, when the underlying data is persistent, constructing forecasts with aggregated data is inefficient. We propose a new forecasting method, Period-End-Point Sampling (PEPS), which uses end-of-period data to create point-in-time forecasts for aggregated data. We show that PEPS forecasts rival the accuracy of bottom-up forecasts and substantially outperform forecasts constructed with averaged data. Importantly, the PEPS method allows models to maintain the lower frequency of the forecast target. Real-time forecast applications to monthly nominal 10-year bond yields and the real prices of gasoline and copper find that disaggregated forecasts can outperform the end-of-month no-change forecasts.
    Keywords: Forecasting and Prediction Methods, Interest Rates, Commodity Prices
    JEL: C1 C53 E47 F37 Q47
    Date: 2023–12
  3. By: Tony Chernis; Niko Hauzenberger; Florian Huber; Gary Koop; James Mitchell
    Abstract: Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a “synthesis” function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from the euro area’s Survey of Professional Forecasters. The second combines density forecasts of US inflation produced by many regression models involving different predictors. Both applications demonstrate the benefits – in terms of improved forecast accuracy and interpretability – of modeling the synthesis function nonparametrically.
    Keywords: Forecast density combination; Bayesian nonparametrics; Bayesian predictive synthesis
    JEL: C11 C32 C53
    Date: 2023–11–21
  4. By: Chaya Weerasinghe; Ruben Loaiza-Maya; Gael M. Martin; David T. Frazier
    Abstract: Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in some sense. In this paper we use ABC to produce probabilistic forecasts in state space models (SSMs). Whilst ABC-based forecasting in correctly-specified SSMs has been studied, the misspecified case has not been investigated, and it is that case which we emphasize. We invoke recent principles of ‘focused’ Bayesian prediction, whereby Bayesian updates are driven by a scoring rule that rewards predictive accuracy; the aim being to produce predictives that perform well in that rule, despite misspecification. Two methods are investigated for producing the focused predictions. In a simulation setting, `coherent' predictions are in evidence for both methods: the predictive constructed via the use of a particular scoring rule predicts best according to that rule. Importantly, both focused methods typically produce more accurate forecasts than an exact, but misspecified, predictive. An empirical application to a truly intractable SSM completes the paper.
    Keywords: Approximate Bayesian computation, auxiliary model, loss-based prediction, focused Bayesian prediction, proper scoring rules, stochastic volatility model
    JEL: C11 C53 C58
    Date: 2023
  5. By: Parth Daxesh Modi; Kamyar Arshi; Pertami J. Kunz; Abdelhak M. Zoubir
    Abstract: Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. Deep neural networks, on the other hand, has shown promising results recently; however, we require huge amount of high-quality data to leverage their power. There are some techniques such as augmentation that can help us with increasing the dataset size, but we cannot exploit them on historical bitcoin data. As a result, we propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models.
    Date: 2023–10

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