Abstract
In order to describe the working conditions of the inverter inductance in actual work more accurately, this paper proposes a simulation model of parallel connection of energy storage ...converters based on nonlinear inductance modeling. Firstly, the mathematical model of nonlinear inductance based on tan
−1
function fitting is introduced, and the equivalent fixed-value inductance model of nonlinear inductance is obtained. Then the suppression method of “PI+R” low-frequency zero sequence circulation is proposed. The simulation results show that the method can suppress low-frequency zero sequence circulation. Finally, the model is established by PLECS, and the method proposed in this paper is verified.
A similarity‐based approach for macroeconomic forecasting Dendramis, Y.; Kapetanios, G.; Marcellino, M.
Journal of the Royal Statistical Society. Series A, Statistics in society,
June 2020, 2020-06-01, 20200601, Volume:
183, Issue:
3
Journal Article
Peer reviewed
Summary
In the aftermath of the recent financial crisis there has been considerable focus on methods for predicting macroeconomic variables when their behaviour is subject to abrupt changes, ...associated for example with crisis periods. We propose similarity‐based approaches as a way to handle parameter instability and apply them to macroeconomic forecasting. The rationale is that clusters of past data that match the current economic conditions can be more informative for forecasting than the entire past behaviour of the variable of interest. We apply our methods to predict both simulated data in a set of Monte Carlo experiments, and a broad set of key US macroeconomic indicators. The forecast evaluation exercises indicate that similarity‐based approaches perform well, in general, in comparison with other common time‐varying forecasting methods, and particularly well during crisis episodes.
Summary
Macroeconomists using large datasets often face the choice of working with either a large vector autoregression (VAR) or a factor model. In this paper, we develop a conjugate Bayesian VAR ...with a subspace shrinkage prior that combines the two. This prior shrinks towards the subspace which is defined by a factor model. Our approach allows for estimating the strength of the shrinkage and the number of factors. After establishing the theoretical properties of our prior, we show that it successfully detects the number of factors in simulations and that it leads to forecast improvements using US macroeconomic data.
This article presents a model that simulates the dynamics of water demand, water supply and the instability of water allocation schemes at the national river basin scale during water scarcity. The ...Zhanghe River Basin in China is used as a case study to demonstrate the model. The optimum solution, minimizing water allocation instability, allocated most of the river's water to the downstream sub-basin, with most of the water assigned for downstream use allocated to Anyang city. The results show that the socioeconomic-environmental dynamics of the stakeholders in a water-sharing problem should be taken into account when allocating water.
Abstract
This article analyzes how economics can frame political debates using economic policy devices. It examines the FIFI macroeconomic model’s introduction into the French planning processes of ...the mid-1960s and argues that economists perform two operations, selection and qualification, which play a key role in structuring political debates on the French economy’s future. Building on archives and in-depth interviews, I show how the FIFI model was a central component of the Sixth Plan (1971–1975): it was designed to produce simulations of state intervention in the French economy and organize planning commissions debates. Studying the struggles and controversies surrounding this model and the economic policies promoted by it, the article ultimately shows how certain political options are made publicly available while others are discarded.
We forecast a single time series using many predictor variables with a new estimator called the three-pass regression filter (3PRF). It is calculated in closed form and conveniently represented as a ...set of ordinary least squares regressions. 3PRF forecasts are consistent for the infeasible best forecast when both the time dimension and cross section dimension become large. This requires specifying only the number of relevant factors driving the forecast target, regardless of the total number of common factors driving the cross section of predictors. The 3PRF is a constrained least squares estimator and reduces to partial least squares as a special case. Simulation evidence confirms the 3PRF’s forecasting performance relative to alternatives. We explore two empirical applications: Forecasting macroeconomic aggregates with a large panel of economic indices, and forecasting stock market returns with price–dividend ratios of stock portfolios.
We apply a novel methodology for estimating time-varying weights in linear prediction pools, which we call Dynamic Pools, and use it to investigate the relative forecasting performance of DSGE models ...with and without financial frictions for output growth and inflation from 1992 to 2011. We find strong evidence of time variation in the pool’s weights, reflecting the fact that the DSGE model with financial frictions produces superior forecasts in periods of financial distress but does not perform as well in tranquil periods. The dynamic pool’s weights react in a timely fashion to changes in the environment, leading to real-time forecast improvements relative to other methods of density forecast combination, such as equal-weights combination, Bayesian model averaging, optimal static pools, and dynamic model averaging. We show how a policymaker dealing with model uncertainty could have used a dynamic pool to perform a counterfactual exercise (responding to the gap in labor market conditions) in the immediate aftermath of the Lehman crisis.
We consider several economic uncertainty indicators for the US and UK before and during the COVID-19 pandemic: implied stock market volatility, newspaper-based policy uncertainty, Twitter chatter ...about economic uncertainty, subjective uncertainty about business growth, forecaster disagreement about future GDP growth, and a model-based measure of macro uncertainty. Four results emerge. First, all indicators show huge uncertainty jumps in reaction to the pandemic and its economic fallout. Indeed, most indicators reach their highest values on record. Second, peak amplitudes differ greatly – from a 35% rise for the model-based measure of US economic uncertainty (relative to January 2020) to a 20-fold rise in forecaster disagreement about UK growth. Third, time paths also differ: Implied volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting differences between Wall Street and Main Street uncertainty measures. Fourth, in Cholesky-identified VAR models fit to monthly U.S. data, a COVID-size uncertainty shock foreshadows peak drops in industrial production of 12–19%.
We develop tests for the null hypothesis that forecasts become uninformative beyond some maximum forecast horizon h∗. The forecast may result from a survey of forecasters or from an estimated ...parametric model. The first class of tests compares the mean-squared prediction error of the forecast to the variance of the evaluation sample, whereas the second class of tests compares it with the mean-squared prediction error of the recursive mean. We show that the forecast comparison may easily be performed by adopting the encompassing principle, which results in simple regression tests with standard asymptotic inference. Our tests are applied to forecasts of macroeconomic key variables from the survey of Consensus Economics. The results suggest that these forecasts are barely informative beyond two to four quarters ahead.
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