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Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs

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

arXiv 28 Aug 2020 · Econometrics · publishedJournal of Econometrics (2020) · 81 citations (OpenAlex)

arXiv:2008.12706 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their flexibility and ability to model outliers. In an application involving four major euro area countries, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR.

Citation extraction

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appendix boundary found by appendix_command · 89% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Chipman HA, George EI, and McCulloch RE (2010) BART: Bayesian additive regression trees0.87482100%
2Crawford L, Wood K, Zhou X, and Mukherjee S (2018) Bayesian Approximate Kernel Regression With Variable Selection0.73732100%
3Crawford L, Flaxman S, Runcie D, and West M (2019) Variable prioritization in nonlinear black box methods: A genetic association case study0.73732100%
4Schorfheide F, and Song D (2015) Real-time forecasting with a mixed-frequency VAR0.73732100%
5Carriero A, Clark TE, and Marcellino M (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors0.5112250%
6Chipman HA, George EI, and McCulloch RE (1998) Bayesian CART Model Search0.51121100%
7Huber F, and Rossini L (2020) Inference in Bayesian additive vector autoregressive tree models0.51121100%
8Lenza M, and Primiceri G (2020) How to estimate a VAR after March 20200.51121100%
9Mariano R, and Murasawa Y (2003) A new coincident index of business cycles based on monthly and quarterly series0.51121100%
10Schorfheide F, and Song D (2020) Real-time forecasting with a (standard) mixed-frequency VAR during a pandemic0.51121100%

Showing the top 10 of 26 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Let the Tree Decide: FABART A Non-Parametric Factor Model1.00053
2Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model0.73732
3Bayesian Forecasting in Economics and Finance: A Modern Review0.64441
4Forecasting Thai inflation from univariate Bayesian regression0.64422
5Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions0.51121
6Bayesian Nonlinear Regression using Sums of Simple Functions0.51121
7Macroeconomic Forecasting with Large Language Models0.51121
8Forecasting in small open emerging economies: Evidence from Thailand0.51121
9Inference in Bayesian Additive Vector Autoregressive Tree Models0.40511
10Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model0.40511