FRED-QD: A Quarterly Database for Macroeconomic Research

McCracken, Michael; Ng, Serena

doi:10.3386/w26872

Cited by 37 publications

(16 citation statements)

References 33 publications

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“…As mentioned, we focus on the UK and, as another contribution of the paper, we construct a monthly large-scale macroeconomic database (MD), labelled UK-MD, comparable to those for the United States by McCracken and Ng (2016, 2020) and for Canada by Fortin-Gagnon et al (2018). 2 Specifically, the dataset contains 112 monthly macroeconomic and financial indicators divided into nine categories: labour, production, retail and services, consumer and retail price indices, producer price indices, international trade, money, credit and interest rate, stock market and finally sentiment and leading indicators.…”

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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“…They differ by the third element of

. We assign each forecaster a different macroeconomic variable from the FRED-QD database by [ 29 ]. A detailed description of the variable used in this exercise is in Table 1 .…”

Section: Empirical Illustrationmentioning

confidence: 99%

“… Note: All variables are obtained from the FRED-QD database [ 29 ]. Inflation rate is computed as a log difference of the GDP deflator (GDPCTPI).…”

Section: Figurementioning

confidence: 99%

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Probability Forecast Combination via Entropy Regularized Wasserstein Distance

Cumings-Menon

Shin

2020

Entropy

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We propose probability and density forecast combination methods that are defined using the entropy regularized Wasserstein distance. First, we provide a theoretical characterization of the combined density forecast based on the regularized Wasserstein distance under the assumption. More specifically, we show that the regularized Wasserstein barycenter between multivariate Gaussian input densities is multivariate Gaussian, and provide a simple way to compute mean and its variance–covariance matrix. Second, we show how this type of regularization can improve the predictive power of the resulting combined density. Third, we provide a method for choosing the tuning parameter that governs the strength of regularization. Lastly, we apply our proposed method to the U.S. inflation rate density forecasting, and illustrate how the entropy regularization can improve the quality of predictive density relative to its unregularized counterpart.

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“…19 Specifically, we select from FRED-QD 220 series that are available for the time period covered by our baseline VAR. For a detailed description of the series and transformation codes to obtain stationarity, the reader may visit the website of the Federal Reserve Bank of St. Louis (https://research.stlouisfed.org/ econ/mccracken/fred-databases) and consult McCracken and Ng (2020). First, we extract r common factors (denoted Â Ft) from these variables by means of principal component estimation, where 1 AE r AE 3.…”

Section: Theory-and Data-based Identificationmentioning

confidence: 99%

Proxy SVAR Identification of Monetary Policy Shocks – Monte Carlo Evidence and Insights for the US

2020

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In empirical macroeconomics, proxy structural vector autoregressive models (SVARs) have become a prominent path towards detecting monetary policy (MP) shocks. However, in practice, the merits of proxy SVARs depend on the relevance and exogeneity of the instrumental information employed. Our Monte Carlo analysis sheds light on the performance of proxy SVARs under realistic scenarios of low relative signal strength attached to MP shocks and alternative assumptions on instrument accuracy. In an empirical application with US data we argue in favor of the specific informational content of instruments based on the dynamic stochastic general equilibrium model of Smets and Wouters (2007). A joint assessment of the benchmark proxy SVAR and the outcomes of a structural covariance change model imply that from 1973 until 1979 monetary policy contributed on average between 2.2 and 2.4 units of inflation in the GDP deflator. For the so-called Volcker disinflation starting in 1979Q4, the benchmark structural model shows that the Fed's policy measures e ectively reduced the GDP deflator within three years (i.e. by-3.06 units until 1982Q3). While the empirical analysis largely conditions on a small-dimensional trinity SVAR, the benchmark proxy SVAR shocks remain remarkably robust within a six-dimensional factor-augmented model comprising rich information from Michael McCracken's database (FRED-QD).

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FRED-QD: A Quarterly Database for Macroeconomic Research

Cited by 37 publications

References 33 publications

Can Machine Learning Catch the Covid-19 Recession?

Can Machine Learning Catch the Covid-19 Recession?

Probability Forecast Combination via Entropy Regularized Wasserstein Distance

Proxy SVAR Identification of Monetary Policy Shocks – Monte Carlo Evidence and Insights for the US

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