Real-time Forecasts of State and Local Government Budgets with an Application to the COVID-19 Pandemic

IF 1.8 3区 经济学 Q2 BUSINESS, FINANCE
Eric Ghysels, Fotis Grigoris, Nazire Özkan
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引用次数: 1

Abstract

Using a sample of the 48 contiguous US states, we consider the problem of forecasting state governments’ revenues and expenditures in real time using models that feature mixed-frequency data. We find that mixed-data sampling (MIDAS) regressions that predict low-frequency fiscal outcomes using high-frequency macroeconomic and financial market data outperform traditional fiscal forecasting models in both a relative and an absolute sense. We also consider an application of forecasting fiscal outcomes in the face of the economic uncertainty induced by the coronavirus pandemic. Overall, we show that MIDAS regressions provide a simple tool for predicting fiscal outcomes in real time.
州和地方政府预算实时预测及其在COVID-19大流行中的应用
使用美国48个相邻州的样本,我们考虑了使用具有混合频率数据的模型实时预测州政府收入和支出的问题。我们发现,使用高频宏观经济和金融市场数据预测低频财政结果的混合数据抽样(MIDAS)回归在相对和绝对意义上都优于传统的财政预测模型。我们还考虑了在冠状病毒大流行引发的经济不确定性面前预测财政结果的应用。总体而言,我们表明MIDAS回归为实时预测财政结果提供了一个简单的工具。
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来源期刊
CiteScore
3.40
自引率
11.80%
发文量
38
期刊介绍: The goal of the National Tax Journal (NTJ) is to encourage and disseminate high quality original research on governmental tax and expenditure policies. Articles published in the regular March, June and September issues of the journal, as well as articles accepted for publication in special issues of the journal, are subject to professional peer review and include economic, theoretical, and empirical analyses of tax and expenditure issues with an emphasis on policy implications. The NTJ has been published quarterly since 1948 under the auspices of the National Tax Association (NTA). Most issues include an NTJ Forum, which consists of invited papers by leading scholars that examine in depth a single current tax or expenditure policy issue. The December issue is devoted to publishing papers presented at the NTA’s annual Spring Symposium; the articles in the December issue generally are not subject to peer review.
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