This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases factor methods have been ...traditionally used, but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic dataset containing 168 variables. We find that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Typically, we find that the simple Minnesota prior forecasts well in medium and large VARs, which makes this prior attractive relative to computationally more demanding alternatives. Our empirical results show the importance of using forecast metrics based on the entire predictive density, instead of relying solely on those based on point forecasts.
This work marks a thorough analysis of a confidential real‐time dataset consisting of the Eurosystem/ECB staff macroeconomic projections since their existence. By applying techniques widely employed ...in the literature of forecast evaluation, we examine their statistical properties with a special emphasis on optimality and rationality. Long‐term GDP projections are biased (tendency to overpredict), do not fully account for available information, and are outperformed by private sector expectations. Inflation projections are optimal and rational on a full‐sample analysis; however, subsample analysis reveals two distinct periods with a persistent and significant bias. Before the financial crisis inflation was persistently underpredicted, while in post‐2013, the bias reverses into overprediction.
Abstract
With the rapid growth of automobile ownership, energy shortage and exhaust pollution have become the two major problems restricting the development of automobiles. Electric vehicles with ...zero fuel consumption and pollution-free advantages are the future direction of development. But battery technology is not mature at this stage, and the driving distance of electric vehicles is short. Extended range electric vehicle is a hybrid electric vehicle equipped with a range extender based on a pure electric vehicle. When the power is sufficient, the whole vehicle runs with pure electric power. When the power is insufficient, the range extender works to drive the vehicle. As a kind of electric vehicle that can extend driving range, the extended-range electric vehicle has great research significance at present. In this paper, based on MATLAB/Simulink platform, the simulation model of the vehicle power transmission system is built by using the experimental modeling method. According to the model, the dynamic performance simulation of the extended-range electric vehicle is carried out, and the rationality of parameter matching of the vehicle components is verified.
Nitrate and sulfate account for a significant fraction of PM₂.₅ mass and are generally secondary in nature. Contributions to these two inorganic aerosol components from major sources need to be ...identified for policy makers to develop cost effective regional emission control strategies. In this work, a source-oriented version of the Community Multiscale Air Quality (CMAQ) model that directly tracks the contributions from multiple emission sources to secondary PM₂.₅ is developed to determine the regional contributions of power, industry, transportation and residential sectors as well as biogenic sources to nitrate and sulfate concentrations in China in January and August 2009. The source-oriented CMAQ model is capable of reproducing most of the available PM₁₀ and PM₂.₅ mass, and PM₂.₅ nitrate and sulfate observations. Model prediction suggests that monthly average PM₂.₅ inorganic components (nitrate + sulfate + ammonium ion) can be as high as 60 μg m⁻³ in January and 45 μg m⁻³ in August, accounting for 20–40% and 50–60% of total PM₂.₅ mass. The model simulations also indicate significant spatial and temporal variation of the nitrate and sulfate concentrations as well as source contributions in the country. In January, nitrate is high over Central and East China with a maximum of 30 μg m⁻³ in the Sichuan Basin. In August, nitrate is lower and the maximum concentration of 16 μg m⁻³ occurs in North China. In January, highest sulfate occurs in the Sichuan Basin with a maximum concentration of 18 μg m⁻³ while in August high sulfate concentration occurs in North and East China with a similar maximum concentration. Power sector is the dominating source of nitrate and sulfate in both January and August. Transportation sector is an important source of nitrate (20–30%) in both months. Industry sector contributes to both nitrate and sulfate concentrations by approximately 20–30%. Residential sector contributes to approximately 10–20% of nitrate and sulfate in January but its contribution is low in August.
•AquaData and AquaGIS were developed to run with the FAO-AquaCrop simulation model.•Both tools allow automated management of model inputs and outputs for multiple runs.•AquaGIS integrates a GIS tool ...with AquaCrop outputs for spatial analyses.•The new tools will facilitate dissemination of simulation results among stakeholders.
The crop simulation model AquaCrop, recently developed by FAO can be used for a wide range of purposes. However, in its present form, its use over large areas or for applications that require a large number of simulations runs (e.g., long-term analysis), is not practical without developing software to facilitate such applications. Two tools for managing the inputs and outputs of AquaCrop, named AquaData and AquaGIS, have been developed for this purpose and are presented here. Both software utilities have been programmed in Delphi v. 5 and in addition, AquaGIS requires the Geographic Information System (GIS) programming tool MapObjects. These utilities allow the efficient management of input and output files, along with a GIS module to develop spatial analysis and effect spatial visualization of the results, facilitating knowledge dissemination. A sample of application of the utilities is given here, as an AquaCrop simulation analysis of impact of climate change on wheat yield in Southern Spain, which requires extensive input data preparation and output processing. The use of AquaCrop without the two utilities would have required approximately 1000h of work, while the utilization of AquaData and AquaGIS reduced that time by more than 99%. Furthermore, the use of GIS, made it possible to perform a spatial analysis of the results, thus providing a new option to extend the use of the AquaCrop model to scales requiring spatial and temporal analyses.
Energy storage components improve the energy efficiency of systems by reducing the mismatch between supply and demand. For this purpose, phase-change materials are particularly attractive since they ...provide a high-energy storage density at a constant temperature which corresponds to the phase transition temperature of the material. Nevertheless, the incorporation of phase-change materials (PCMs) in a particular application calls for an analysis that will enable the researcher to optimize performances of systems. Due to the non-linear nature of the problem, numerical analysis is generally required to obtain appropriate solutions for the thermal behavior of systems. Therefore, a large amount of research has been carried out on PCMs behavior predictions. The review will present models based on the first law and on the second law of thermodynamics. It shows selected results for several configurations, from numerous authors so as to enable one to start his/her research with an exhaustive overview of the subject. This overview stresses the need to match experimental investigations with recent numerical analyses since in recent years, models mostly rely on other models in their validation stages.
We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters ...exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. gross domestic product (GDP) growth and inflation suggests that models that allow for parameter instability generate more accurate density forecasts than constant-parameter models although they fail to produce better point forecasts. Model combinations deliver similar gains in predictive performance although they fail to improve on the predictive accuracy of the single best model, which is a specification that allows for time-varying parameters and stochastic volatility. Supplementary materials for this article are available online.
We show that high-dimensional models produce, on average, smaller forecasting errors for macroeconomic variables when we consider a large set of predictors. Our results showed that a good selection ...of the adaptive LASSO hyperparameters also reduces forecast errors.
•Comparison between several high-dimensional econometric models.•Application to forecast different macroeconomic variables using large datasets.•High-dimensional models produce smaller forecast errors on average.•LASSO type models are the best amongst the high-dimensional models.
We employ datasets for seven developed economies and consider four classes of multivariate forecasting models in order to extend and enhance the empirical evidence in the macroeconomic forecasting ...literature. The evaluation considers forecasting horizons of between one quarter and two years ahead. We find that the structural model, a medium-sized DSGE model, provides accurate long-horizon US and UK inflation forecasts. We strike a balance between being comprehensive and producing clear messages by applying meta-analysis regressions to 2,976 relative accuracy comparisons that vary with the forecasting horizon, country, model class and specification, number of predictors, and evaluation period. For point and density forecasting of GDP growth and inflation, we find that models with large numbers of predictors do not outperform models with 13–14 hand-picked predictors. Factor-augmented models and equal-weighted combinations of single-predictor mixed-data sampling regressions are a better choice for dealing with large numbers of predictors than Bayesian VARs.
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