86,907 research outputs found
Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox
No full textThis paper presents the MATLAB package DeCo (density combination) which is based on the paper by Billio, Casarin, Ravazzolo, and van Dijk (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights are time-varying and may depend on past predictive forecasting performances and other learning mechanisms. The core algorithm is the function DeCo which applies banks of parallel Sequential Monte Carlo algorithms to filter the time-varying combination weights. The DeCo procedure has been implemented both for standard CPU computing
and for graphical process unit (GPU) parallel computing. For the GPU implementation we use the MATLAB parallel computing toolbox and show how to use general purposes GPU computing almost effortless. This GPU implementation comes with a speed up of the execution time up to seventy times compared to a standard CPU MATLAB mplementation on a multicore CPU. We show the use of the package and the computational gain of the GPU version, through some simulation experiments and empirical applications
Density calibration with consistent scoring functions
No full textThis contribution studies a calibration approach for predictive densities based on generalized scoring rules. We consider a set of simulated experiments in order to study the effectiveness of the metho
A discussion on: On a Class of Objective Priors from Scoring Rules by F. Leisen, C. Villa and S. G. Walker
No full textObjective prior distributions represent an important tool that allows one to have the advantages of using a Bayesian framework even when information about the parameters of a model is not available. The usual objective approaches work off the chosen statistical model and in the majority of cases the resulting prior is improper, which can pose limitations to a practical implementation, even when the complexity of the model is moderate. In this paper we propose to take a novel look at the construction of objective prior distributions, where the connection with a chosen sampling distribution model is removed. We explore the notion of defining objective prior distributions which allow one to have some degree of flexibility, in particular in exhibiting some desirable features, such as being proper, or log-concave, convex etc. The basic tool we use are proper scoring rules and the main result is a class of objective prior distributions that can be employed in scenarios where the usual model based priors fail, such as mixture models and model selection via Bayes factors. In addition, we show that the proposed class of priors is the result of minimising the information it contains, providing solid interpretation to the method
Data for: Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach
No full textThe dataset is an update version of the global VAR Mohaddes and Raissi (2018). The original data is available at https://www.repository.cam.ac.uk/handle/1810/280234 Series are updated to 2018Q4 using sources described in the paper
Density Forecasting
No full textThis paper reviews different methods to construct density forecasts and to aggregate forecasts from many sources. Density evaluation tools to measure the accuracy of density forecasts are reviewed and calibration methods for improving the accuracy of forecasts are presented. The manuscript provides some numerical simulation tools to approximate predictive densities with a focus on parallel computing on graphical process units. Some simple examples are proposed to illustrate the methods
A multivariate dependence analysis for electricity prices, demand and renewable energy sources
No full textThis paper examines the dependence between electricity prices, demand, and renewable energy sources by means of a multivariate copula model while studying Germany, the widest studied market in Europe. The inter-dependencies are investigated in-depth and monitored over time, with particular emphasis on the tail behavior. To this end, suitable tail dependence measures are introduced to take into account a multivariate extreme scenario appropriately identified through the Kendall's distribution function. The empirical evidence demonstrates a strong association between electricity prices, renewable energy sources, and demand within a day and over the studied years. Hence, this analysis provides guidance for further and different incentives for promoting green energy generation while considering the time-varying dependencies of the involved variables
Large Time-Varying Volatility Models for Hourly Electricity Prices
No full textWe study the importance of time-varying volatility in modelling hourly electricity prices when fundamental drivers are included in the estimation. This allows us to contribute to the literature of large Bayesian VARs by using well-known time series models in a large dimension for the matrix of coefficients. Based on novel Bayesian techniques, we exploit the importance of both Gaussian and non-Gaussian error terms in stochastic volatility. We find that using regressors as fuel prices, forecasted demand and forecasted renewable energy is essential to properly capture the volatility of these prices. Moreover, we show that the time-varying volatility models outperform the constant volatility models in both the in-sample model-fit and the out-of-sample forecasting performance
Is the price cap for gas useful? Evidence from European countries
No full textSince Russia’s invasion of Ukraine, many countries have pledged to end or restrict their oil and gas imports to curtail Moscow’s revenues and hinder its war effort. Thus, the European ministers agreed to trigger a cap on the gas price. To detect the importance of the price cap for gas, we provide a mixture representation for the gas price to detect the presence of outliers made by a truncated normal distribution and a uniform one. We focus our analysis on a unique dataset of different commodity prices for Germany and Italy, which are major Russian gas importers by exploiting the response of the different commodities to a gas shock through a Bayesian vector autoregressive (VAR) model. As a result, including a lower gas price cap smooths the impact of a gas shock on electricity prices, while not considering a price cap will increase exponentially this impact. Regarding the other commodities, gas shocks matter in the short and long run when a price cap is not considered
Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions
No full textThis article proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It generalizes the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable’s range. The (weighted) ACPS extends the symmetric (weighted) CRPS by allowing for asymmetries in the preferences underlying the scoring rule. A test is used to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision-maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (U.S. employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period
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