nep-for New Economics Papers
on Forecasting
Issue of 2022‒10‒03
three papers chosen by
Rob J Hyndman
Monash University

  1. Modeling Volatility and Dependence of European Carbon and Energy Prices By Jonathan Berrisch; Sven Pappert; Florian Ziel; Antonia Arsova
  2. Testing big data in a big crisis: Nowcasting under COVID-19 By Barbaglia, Luca; Frattarolo, Lorenzo; Onorante, Luca; Pericoli, Filippo Maria; Ratto, Marco; Tiozzo Pezzoli, Luca
  3. Semiparametric Partially Linear Varying Coefficient Modal Regression By Aman Ullah; Tao Wang; Weixin Yao

  1. By: Jonathan Berrisch; Sven Pappert; Florian Ziel; Antonia Arsova
    Abstract: We study the prices of European Emission Allowances (EUA), whereby we analyze their uncertainty and dependencies on related energy markets. We propose a probabilistic multivariate conditional time series model that exploits key characteristics of the data. The forecasting performance of the proposed model and various competing models is evaluated in an extensive rolling window forecasting study, covering almost two years out-of-sample. Thereby, we forecast 30-steps ahead. The accuracy of the multivariate probabilistic forecasts is assessed by the energy score. We discuss our findings focusing on volatility spillovers and time-varying correlations, also in view of the Russian invasion of Ukraine.
    Date: 2022–08
  2. By: Barbaglia, Luca (European Commission); Frattarolo, Lorenzo (European Commission); Onorante, Luca (European Commission); Pericoli, Filippo Maria (European Monitoring Centre for Drugs and Drug Addiction); Ratto, Marco (European Commission); Tiozzo Pezzoli, Luca (European Commission)
    Abstract: During the COVID-19 pandemic, economists have struggled to obtain reliable economic predictions, with standard models becoming outdated and their forecasting performance deteriorating rapidly. This paper presents two novelties that could be adopted by forecasting institutions in unconventional times. The first innovation is the construction of an extensive data set for macroeconomic forecasting in Europe. We collect more than a thousand time series from conventional and unconventional sources, complementing traditional macroeconomic variables with timely big data indicators and assessing their added value at nowcasting. The second novelty consists of a methodology to merge an enormous amount of non-encompassing data with a large battery of classical and more sophisticated forecasting methods in a seamlessly dynamic Bayesian framework. Specifically, we introduce an innovative “selection prior†that is used not as a way to influence model outcomes, but as a selecting device among competing models. By applying this methodology to the COVID-19 crisis, we show which variables are good predictors for nowcasting Gross Domestic Product and draw lessons for dealing with possible future crises
    Keywords: Bayesian Model Averaging, Big Data, COVID-19 Pandemic, Nowcasting
    JEL: C11 C30 E3 E37
    Date: 2022–08
  3. By: Aman Ullah (Department of Economics, University of California Riverside); Tao Wang (University of Victoria); Weixin Yao (University of California Riverside)
    Abstract: We in this paper propose a semiparametric partially linear varying coefficient (SPLVC) modal regression, in which the conditional mode function of the response variable given covariates admit a partially linear varying coefficient structure. In comparison to existing regressions, the newly developed SPLVC modal regression captures the most likely effect and provides superior prediction performance when the data distribution is skewed. The consistency and asymptotic properties of the resultant estimators for both parametric and nonparametric parts are rigorously established. We employ a kernel-based objective function to simplify the computation and a modified modal-expectation-maximization (MEM) algorithm to estimate the model numerically. Furthermore, taking the residual sums of modes as the loss function, we construct a goodness of fit testing statistic for hypotheses on the coefficient functions, whose limiting null distribution is shown to follow an asymptotically normal-distribution with a scale dependent on density functions. To achieve sparsity in the high-dimensional SPLVC modal regression, we develop a regularized estimation procedure by imposing a penalty on the coefficients in the parametric part to eliminate the irrelevant variables. Monte Carlo simulations and two real-data applications are conducted to examine the performance of the suggested estimation methods and hypothesis test. We also briefly explore the extension of the SPLVC modal regression to the case where some varying coefficient functions admit higher-order smoothness.
    Keywords: Goodness of fit test, MEM algorithm, Modal regression, Oracle property, Partially linear varying coefficient
    JEL: C01 C12 C14 C50
    Date: 2022–06

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