This article provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant ...examples include predicting the financial crisis of 2007–08, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth, and inflation. In the context of unstable environments, I discuss how to assess models’ forecasting ability; how to robustify models’ estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models’ parameters are neither necessary nor sufficient to generate time variation in models’ forecasting performance: thus, one should not test for breaks in models’ parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models’ forecasting performance are more appropriate than traditional, average measures.
Abstract
With the development of PV on a large scale, developable rooftop distributed PV becomes a high-quality resource to achieve the double carbon goal. Compared with single-node and ...low-permeability PV access, large-scale photovoltaic access makes the power grid present high-power electronic characteristics, which has a more complex impact on the harmonic characteristics of the distribution grid. To comprehensively reveal the influence of large-scale distributed photovoltaic access on the harmonicity of the distribution network, this paper first conducts a theoretical analysis of the harmonic generation mechanism. On this basis, build a simulation model based on Matlab/Simulink, considering the measurement indexes of low harmonics and supraharmonics, and the influence of grid-connected location, grid-connected capacity, background harmonics, access mode, and other factors on the harmonic characteristics of the distribution network is simulated and analysed. According to the simulation results, large-scale distributed PV access significantly increases the content of low and supraharmonics in the distribution network, which has a great impact on the harmonic characteristics of the distribution network.
•Under future climates elevated CO2 and increased heat stress will impact on wheat yields and quality.•Many crop models are restricted to simulating the quality variables of average grain size and ...grain-N content.•Crop models generally donot predict more detailed whole-grain physical characteristics or protein composition.•New state variables are needed in our crop models to accommodate a broader range of grain quality parameters.•Such models provide a tool for developing adaptation strategies to limit impacts of climate change to global grain quality.
Maintaining grain quality of wheat under climate change is critical for human nutrition, end-use functional properties, as well as commodity value. This paper reviews the current knowledge of high temperature and elevated atmospheric CO2 on whole-grain and functional properties of wheat. It also considers the utility of contemporary crop models for investigating the impacts of climate change on wheat quality; and discusses opportunities for advancing model capability. Under elevated CO2 wheat yield can increase by up to 36%, but universally grain protein concentration decreases and a shift in composition translates to reduced functional properties. High temperature during the post-anthesis period of crops can cause a step change reduction in grain-set, grain size and milling yield. Numerous crop models including APSIM-Nwheat, CropSyst, Sirius, GLAM-HTS account for high CO2 effects through modification of RUE, TE or critical leaf-N concentration and high temperature by accelerated leaf senescence, grain number, potential grain weight or HI modifications. For grain quality, however, crop models are typically restricted to predicting average grain size and grain-N content (concentration), although the SiriusQuality model accounts for the major storage proteins, gliadin and glutenin. For protein composition, high temperature stress reduces the glutenin/gliadin ratio and limits the synthesis of the larger SDS-insoluble glutenin polymers which causes wheat dough to have weaker viscoelasticity properties. This link provides an opportunity to model high temperature effects on grain functional properties. Further development and testing, utilizing grain quality data from global FACE programmes will be particularly valuable for validating and enhancing the performance of such models. For whole-grain characteristics, a single-spike model approach, which accounts for intra-spike variation in assimilate deposition may provide an opportunity to predict grain size distribution and associated screenings percentage and milling yield. Taken together expanding the predictive capability of our crop models to grain quality is an important step in providing a powerful tool for developing adaptation strategies for combating the impacts of climate change to global crop production and grain quality.
We provide a comprehensive assessment of the predictive power of combinations of dynamic stochastic general equilibrium (DSGE) models for GDP growth, inflation, and the interest rate in the euro ...area. We employ a battery of static and dynamic pooling weights based on Bayesian model averaging principles, prediction pools, and dynamic factor representations, and entertain six different DSGE specifications and five prediction weighting schemes. Our results indicate that exploiting mixtures of DSGE models produces competitive forecasts compared to individual specifications for both point and density forecasts over the last three decades. Although these combinations do not tend to systematically achieve superior forecast performance, we find improvements for particular periods of time and variables when using prediction pooling, dynamic model averaging, and combinations of forecasts based on Bayesian predictive synthesis.
Low‐fidelity computational models have been widely used for the computation of complex engineered systems to greatly save the computational time while sacrificing the computational precision. Model ...calibration can be implemented for low‐fidelity simulation models to remedy the errors of simulation results. This article proposes a modular Bayesian updating framework that considers epistemic uncertainty to achieve model calibration of low‐fidelity simulation models. The proposed framework mainly contains two steps: (1) A model calibration framework based on Bayesian theory is proposed that can simultaneously quantify the model form uncertainty, model parameter uncertainty, and the experimental measurement uncertainty, (2) the proposed method updates a low‐fidelity surrogate model via a Metropolis–Hastings (M–H) algorithm by using high‐fidelity experimental data. The proposed method greatly improves the prediction accuracy of the simulation model and enhances the computational efficiency. A mathematical example is used to testify the accuracy of the developed method, while the aerodynamics simulation of the NACA0012 airfoil is leveraged to demonstrate the engineering application. The results show that the posterior prediction mean is in better agreement with the experimental mean than the prior prediction mean, suggesting that the modular Bayesian updating method improves the prediction accuracy of low‐fidelity simulation models.
Abstract
The method of building a simulation model of the production and transportation problem is described. In the FlexSIM environment, a simulation model of a part of the production process was ...built and the impact of changing input parameters on output parameters and the behavior of the technological system as a whole was investigated and analyzed.
Abstract
In this paper, firstly, the generation of characteristic harmonics and non-characteristic harmonics, the content of each harmonic and its amplitude varying with the frequency of HVDC ...converter are analyzed theoretically, then, the harm of harmonics to HVDC transmission lines is introduced, and some restraining measures are put forward, such as increasing converter pulsation number, installing and improving AC filter to suppress the generation of specific frequency harmonics and reduce their amplitude, so as to increase the stability of the system. Secondly, through simulation based on PSCAD/EMTDC, it is verified that 6K±1 harmonics (where K is odd) can be eliminated by increasing the converter from 6 to 12 pulsations, so as to reduce the harmonic content of transmission lines. Finally, a simulation model is built to verify that the harmonic amplitude of 12K±1 (K is a positive integer) is greatly reduced when the hybrid tuning filter is installed at 12 pulsations.
•Time-varying volatility is key to estimating term premia and expected short rates.•Global macro factors influence eurozone term premia and short rate expectations.•Convexity effects have significant ...impact on long-term yields in turbulent times.•Connectedness in eurozone term premia and expected short rates varies over time.
Identifying the components of yields is a challenging task for monetary authorities. We use a term structure model with stochastic volatility and eurozone global macro factors to estimate time-varying term premia and short rate expectations for ten countries in the euro area. Unlike previous studies, we explicitly disentangle from these components the convexity effects that have substantial impact on long-term yields in turbulent times. The empirical evidence shows that term premia are significantly positively related to yield volatility across all countries, while term premia and expected short rates react in opposite directions to shocks in eurozone inflation and GDP growth expectations. A connectedness analysis based on variance decomposition suggests that there exist significant cross-country interconnections for the yield components, with the size of the links varying substantially over time and across countries.
Macroeconomic forecasting in times of crises Guerróon‐Quintana, Pablo; Zhong, Molin
Journal of applied econometrics (Chichester, England),
April/May 2023, Volume:
38, Issue:
3
Journal Article
Peer reviewed
Open access
Summary
We propose a parsimonious semiparametric method for macroeconomic forecasting. Based on ideas of clustering and similarity, we partition the series into blocks, search for the closest blocks ...to the latest block of observations, and forecast with the matched blocks. In a real‐time forecasting exercise, we show that our approach does especially well for labor market and other key macro variables. Our method outperforms parametric linear, nonlinear, time‐varying, and combination forecasts for the period 1999–2015 and particularly in the Great Recession. When adding financial spreads, our method delivers further improvements for labor market variables and capacity utilization.
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