Added: Mandee Stampley - Date: 11.05.2022 06:02 - Views: 15634 - Clicks: 2068
Author s : Caroline JardetBaptiste Meunier. Keywords: Nowcastingmixed-frequency datahigh-frequency dataworld GDPlarge factor models. The Covid crisis has shown how high-frequency data can help tracking economic activity.
Going forward, we investigate in a recent paper available here whether it can improve nowcasting performances for world GDP growth. To this end, we select a large dataset of monthly and 39 weekly series. Nowcasting models that include weekly data ificantly outperforms others, both in- and out-of-sample. The sudden shock of the Covid crisis has put new emphasis on high-frequency data and a of weekly, daily, or even hourly data have been extensively used to assess in real-time the impact of the Great Lockdown e.
Chetty et al. We exploit monthly and weekly indicators to nowcast quarterly and annual GDP growth. The comparative advantage of weekly data is represented in Figure 1 : the red square figures a given date — around May 10th in this example — and available data at this date appear in red. Official quarterly growth rates for Q1 are not yet available they are published on average 45 days after quarter endmonthly indicators are available only until month 3 they are generally published around 20 days after month end but weekly data are available up to the preceding week.
Timeliness is the main reason why we consider incorporate such high-frequency data it in a nowcasting model. Figure 1. We therefore rely on using comparable statistics for a of countries.
The idea is to build a large cross-national dataset from which we can extract the information into a few factors by a principal component analysis PCA. Once extracted, this common trend can be taken as global variable. Following Bai and Ng — who showed that forecasting performances were improved when selecting fewer but more informative predictors — we select a dataset of monthly variables and 39 weekly series out of monthly and weekly potential regressors.
Our selection method is based on the correlation between these variables and our target variable global GDP growth in line with Bair et al. US jobless claims, stock market indexes ; and ii rely on a large dataset aggregating multiple cross-national variables.
Our approach is close to Ferrara and Marsilli which, to some extent, we extend to high-frequency data. To test whether high-frequency data enhance nowcasting performances, we compare root mean squared errors RMSE Best gdp episodes different models predicting quarterly world GDP growth. Major can be found in Table 1. On the entire sample, high-frequency data improve nowcasting performance as the accuracy — both in-sample and out-of-sample — is ificantly greater when a model includes weekly data. These are also confirmed more formally by Diebold and Mariano tests checking for ificant difference in predictive accuracies.
Table 1. Performances RMSE across models and months of the quarter. Grey cells indicate best performance for a given mont. Figure 2. Real-time nowcast and forecasts for the annual growth rate of world GDP. Bai J. Bair E. Carvalho V. Chetty R. Diebold F. Ferrara L. Marcellino M. She holds a PhD in economics from the University Paris 1. On top of high-frequency data, his research also covers cross-border banking flows.
They address topical issues and propose solutions to current economic and financial challenges. The views expressed are those of the author s and not necessarily those of the institution s the author s. Llewellyn, Donato Masciandaro, Natacha Valla.
Non-crisis episodes.Best gdp episodes
email: [email protected] - phone:(231) 934-5410 x 4741
What is your favorite GirlsDoPorn Episodes?