Nowcasting in a pandemic using non-parametric mixed frequency VARs

Huber, Florian; Koop, Gary; Onorante, Luca; Pfarrhofer, Michael; Schreiner, Josef

doi:10.1016/j.jeconom.2020.11.006

Cited by 56 publications

(43 citation statements)

References 51 publications

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“…The goal of this paper is to go one step further in terms of model sophistication, by considering a variety of machine learning (ML) methods and assessing whether and to what extent they can improve the forecasts, both in general and specifically during the Covid-19 crisis, focussing on the UK economy that at the same time was also experiencing substantial Brexit-related uncertainty. A related paper, but with a focus on the largest euro area countries, is Huber et al (2020) who introduce Bayesian Additive Regression Tree-VARs (BART-VARs) for Covid. They develop a nonlinear mixed-frequency VAR framework by incorporating regression trees, and exploiting their ability to model outliers and to disentangle the signal from noise.…”

Section: Introductionmentioning

confidence: 99%

Can Machine Learning Catch the Covid-19 Recession?

Coulombe

Marcellino

Stevanović

2021

NIER

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Based on evidence gathered from a newly built large macroeconomic dataset (MD) for the UK, labelled UK-MD and comparable to similar datasets for the United States and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components). This and other crucial aspects of ML-based forecasting in unprecedented times are studied in an extensive pseudo-out-of-sample exercise.

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Section: Introductionmentioning

confidence: 99%

Can Machine Learning Catch the Covid-19 Recession?

Coulombe

Marcellino

Stevanović

2021

NIER

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“…Several papers discuss methods for estimating VAR models in light of huge-variance shocks such as during the pandemic (Huber et al, 2021;Lenza & Primiceri, 2020;Schorfheide & Song, 2020). Our approach is closest to Lenza and Primiceri (2020), who find that overall macroeconomic dynamics and cross-variable relationships during the pandemic months are consistent with those of the pre-pandemic period.…”

Section: This Is Achieved By Definingmentioning

confidence: 90%

Measuring the effectiveness of US monetary policy during the COVID‐19 recession

Feldkircher

Huber

Pfarrhofer

2021

Scottish J Political Eco

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The COVID‐19 recession that started in March 2020 led to an unprecedented decline in economic activity across the globe. To fight this recession, policy makers in central banks engaged in expansionary monetary policy. This paper asks whether the measures adopted by the US Federal Reserve (Fed) have been effective in boosting real activity and calming financial markets. To measure these effects at high frequencies, we propose a novel mixed frequency vector autoregressive (MF‐VAR) model. This model allows us to combine weekly and monthly information within a unified framework. Our model combines a set of macroeconomic aggregates such as industrial production, unemployment rates, and inflation with high‐frequency information from financial markets such as stock prices, interest rate spreads, and weekly information on the Fed's balance sheet size. The latter set of high‐frequency time series is used to dynamically interpolate the monthly time series to obtain weekly macroeconomic measures. We use this setup to simulate counterfactuals in absence of monetary stimulus. The results show that the monetary expansion caused higher output growth and stock market returns, more favorable long‐term financing conditions and a depreciation of the US dollar compared with a no‐policy benchmark scenario.

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“…In both cases, o j,t allows each model to pick up on temporary increases in volatility that would be ill-represented by the more persistent variations modeled via the conventional SV processes for log λ t . 22 Here in Section 2, SV-t sees outliers as being more moderately sized but occurring also more regularly than SVO, which tends to see outlier states to be larger than 5 (when they occur).…”

Section: Outlier Estimates In 2020 and Beforementioning

confidence: 94%

Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

et al. 2021

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Incoming data in 2020 posed sizable challenges for the use of VARs in economic analysis: Enormous movements in a number of series have had strong effects on parameters and forecasts constructed with standard VAR methods. We propose the use of VAR models with time-varying volatility that include a treatment of the COVID extremes as outlier observations. Typical VARs with time-varying volatility assume changes in uncertainty to be highly persistent. Instead, we adopt an outlier-adjusted stochastic volatility (SV) model for VAR residuals that combines transitory and persistent changes in volatility. In addition, we consider the treatment of outliers as missing data. Evaluating forecast performance over the last few decades in quasi-real time, we find that the outlier-augmented SV scheme does at least as well as a conventional SV model, while both outperform standard homoskedastic VARs. Point forecasts made in 2020 from heteroskedastic VARs are much less sensitive to outliers in the data, and the outlier-adjusted SV model generates more reasonable gauges of forecast uncertainty than a standard SV model. At least pre-COVID, a close alternative to the outlier-adjusted model is an SV model with t-distributed shocks. Treating outliers as missing data also generates better-behaved forecasts than the conventional SV model. However, since uncertainty about the incidence of outliers is ignored in that approach, it leads to strikingly tight predictive densities.

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Nowcasting in a pandemic using non-parametric mixed frequency VARs

Cited by 56 publications

References 51 publications

Can Machine Learning Catch the Covid-19 Recession?

Can Machine Learning Catch the Covid-19 Recession?

Measuring the effectiveness of US monetary policy during the COVID‐19 recession

Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

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