nep-for New Economics Papers
on Forecasting
Issue of 2021‒06‒28
seven papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting US Inflation in Real Time By Chad Fulton; Kirstin Hubrich
  2. Inflation Dynamics and Forecast: Frequency Matters By Manuel M. F. Martins; Fabio Verona
  3. Panel Forecasts of Country-Level Covid-19 Infectionsliu By Liu, Laura; Moon, Hyungsik Roger; Schorfheide, Frank
  4. Next-Day Bitcoin Price Forecast Based on Artificial intelligence Methods By Liping Yang
  5. Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims By Daniel Aaronson; Scott A. Brave; R. Andrew Butters; Daniel Sacks; Boyoung Seo
  6. Forecasting VaR and ES using a joint quantile regression and implications in portfolio allocation By Luca Merlo; Lea Petrella; Valentina Raponi
  7. Probabilistic Forecasting of Imbalance Prices in the Belgian Context By Jonathan Dumas; Ioannis Boukas; Miguel Manuel de Villena; S\'ebastien Mathieu; Bertrand Corn\'elusse

  1. By: Chad Fulton; Kirstin Hubrich
    Abstract: We perform a real-time forecasting exercise for US inflation, investigating whether and how additional information--additional macroeconomic variables, expert judgment, or forecast combination--can improve forecast accuracy and robustness. In our analysis we consider the pre-pandemic period including the Global Financial Crisis and the following expansion--the longest on record--featuring unemployment that fell to a rate not seen for nearly sixty years. Distinguishing features of our study include the use of published Federal Reserve Board staff forecasts contained in Tealbooks and a focus on forecasting performance before, during, and after the Global Financial Crisis, with relevance also for the current crisis and beyond. We find that while simple models remain hard to beat, the additional information that we consider can improve forecasts, especially in the post-crisis period. Our results show that (1) forecast combination approaches improve forecast accuracy over simpler models and robustify against bad forecasts, a particularly relevant feature in the current environment; (2) aggregating forecasts of inflation components can improve performance compared to forecasting the aggregate directly; (3) judgmental forecasts, which likely incorporate larger and more timely datasets, provide improved forecasts at short horizons.
    Keywords: Inflation; Survey forecasts; Forecast combination
    JEL: C53 E37 E30
    Date: 2021–03–04
  2. By: Manuel M. F. Martins (Faculty of Economics, University of Porto and CEF.UP); Fabio Verona (Bank of Finland - Monetary Policy and Research Department and University of Porto - CEF.UP)
    Abstract: Policymakers and researchers see inflation characterized by cyclical fluctuations driven by changes in resource utilization and temporary shocks, around a trend influenced by inflation expectations. We study the in-sample inflation dynamics and forecast inflation out-of-sample by analyzing a New Keynesian Phillips Curve (NKPC) in the frequency domain. In-sample, while inflation expectations dominate medium-to-long-run cycles, energy prices dominate short cycles and business-to-medium cycles once expectations became anchored. While statistically significant, unemployment is not economically relevant for any cycle. Out-of-sample, forecasts from a low-frequency NKPC significantly outperform several benchmark models. The long-run component of unemployment is key for such remarkable forecasting performance.
    Keywords: Inflation dynamics; Inflation forecast; New Keynesian Phillips Curve; Frequency domain; Wavelets
    JEL: C53 E31 E37
    Date: 2021–06
  3. By: Liu, Laura; Moon, Hyungsik Roger; Schorfheide, Frank
    Abstract: We use dynamic panel data models to generate density forecasts for daily Covid-19 infections for a panel of countries/regions. At the core of our model is a specification that assumes that the growth rate of active infections can be represented by autoregressive fluctuations around a downward sloping deterministic trend function with a break. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of heterogeneous coefficients and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. According to our model, there is a lot of uncertainty about the evolution of infection rates, due to parameter uncertainty and the realization of future shocks. We find that over a one-week horizon the empirical coverage frequency of our interval forecasts is close to the nominal credible level. Weekly forecasts from our model are published at ecast/.
    Keywords: Bayesian inference; COVID-19; Density forecasts; interval forecasts; panel data models; random effects; SIR model
    JEL: C11 C23 C53
    Date: 2020–05
  4. By: Liping Yang
    Abstract: In recent years, Bitcoin price prediction has attracted the interest of researchers and investors. However, the accuracy of previous studies is not well enough. Machine learning and deep learning methods have been proved to have strong prediction ability in this area. This paper proposed a method combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning method called long short-term memory (LSTM) to research the problem of next-day Bitcoin price forecast.
    Date: 2021–06
  5. By: Daniel Aaronson; Scott A. Brave; R. Andrew Butters; Daniel Sacks; Boyoung Seo
    Abstract: We leverage an event-study research design focused on the seven costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic “unemployment,” we show that out-of-sample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the Covid-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty surrounding these estimates and demonstrate their use within a broader forecasting framework for US economic activity.
    Keywords: unemployment insurance; Google Trends; hurricanes; search; unemployment; Covid-19 1 The
    JEL: C53 H12 J65
    Date: 2020–04–08
  6. By: Luca Merlo; Lea Petrella; Valentina Raponi
    Abstract: In this paper we propose a multivariate quantile regression framework to forecast Value at Risk (VaR) and Expected Shortfall (ES) of multiple financial assets simultaneously, extending Taylor (2019). We generalize the Multivariate Asymmetric Laplace (MAL) joint quantile regression of Petrella and Raponi (2019) to a time-varying setting, which allows us to specify a dynamic process for the evolution of both VaR and ES of each asset. The proposed methodology accounts for the dependence structure among asset returns. By exploiting the properties of the MAL distribution, we then propose a new portfolio optimization method that minimizes the portfolio risk and controls for well-known characteristics of financial data. We evaluate the advantages of the proposed approach on both simulated and real data, using weekly returns on three major stock market indices. We show that our method outperforms other existing models and provides more accurate risk measure forecasts compared to univariate ones.
    Date: 2021–06
  7. By: Jonathan Dumas; Ioannis Boukas; Miguel Manuel de Villena; S\'ebastien Mathieu; Bertrand Corn\'elusse
    Abstract: Forecasting imbalance prices is essential for strategic participation in the short-term energy markets. A novel two-step probabilistic approach is proposed, with a particular focus on the Belgian case. The first step consists of computing the net regulation volume state transition probabilities. It is modeled as a matrix computed using historical data. This matrix is then used to infer the imbalance prices since the net regulation volume can be related to the level of reserves activated and the corresponding marginal prices for each activation level are published by the Belgian Transmission System Operator one day before electricity delivery. This approach is compared to a deterministic model, a multi-layer perceptron, and a widely used probabilistic technique, Gaussian Processes.
    Date: 2021–06

This nep-for issue is ©2021 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.