Nowcasting GDP with a pool of factor models and a fast estimation algorithm

Eraslan, Sercan; Schröder, Maximilian

doi:10.1016/j.ijforecast.2022.07.009

Cited by 3 publications

(1 citation statement)

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“…The approach adopted in this paper is characterised by time-varying parameters, as customary in the related literature (Martínez-Martín & Rusticelli, 2020). Unlike studies focused on quarterly outcomes (Eraslan & Schröder, 2022), this paper's focus on monthly target variables implies that a mixed-frequency approach is not necessary.…”

Section: Numbermentioning

confidence: 99%

Nowcasting services trade for the G7 economies

Jaax,

Mourougane,

Gonzales

2023

World Economy

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The growing importance of services trade in the global economy contrasts with the scarcity of timely data on this part of international trade. This paper develops models to nowcast aggregate services imports and exports, as measured by monthly services trade data, for the G7 countries. The methodology relies on machine‐learning techniques and dynamic factor models and combines traditional high‐frequency data with the use of Google Trends search data. The estimated services trade nowcasting models display a higher out‐of‐sample predictive power than a simple benchmark model. However, there does not seem to be one approach that outperforms other model specifications. Rather, a weighted average of the best models, combining machine‐learning with dynamic factor models, seems to be a promising avenue. The best models improve one step ahead predictive performance relative to a simple benchmark by 30%–35% on average across the G7 countries and across trade flows. Models capture approximately 67% of the fall in services exports following the COVID‐19 shock and 60% of the fall in imports on average across G7 economies.

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Section: Numbermentioning

confidence: 99%

Nowcasting services trade for the G7 economies

Jaax,

Mourougane,

Gonzales

2023

World Economy

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Financial Condition Indices in an Incomplete Data Environment

Herculano,

Jacob

2023

Studies in Nonlinear Dynamics &Amp; Econometrics

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We construct a Financial Conditions Index (FCI) for the United States using a dataset that features many missing observations. The novel combination of probabilistic principal component techniques and a Bayesian factor-augmented VAR model resolves the challenges posed by data points being unavailable within a high-frequency dataset. Even with up to 62 % of the data missing, the new approach yields a less noisy FCI that tracks the movement of 22 underlying financial variables more accurately both in-sample and out-of-sample.

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