Recent Developments on Factor Models and its Applications in Econometric Learning

Fan, Jianqing; Li, Kunpeng; Liao, Yuan

doi:10.48550/arxiv.2009.10103

Cited by 2 publications

(2 citation statements)

References 98 publications

(135 reference statements)

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“…Previous methods rely on statistical learning, including shrinkage methods like Lasso (Tibshirani, 1996), Bayesian methods (Sims and Zha, 1998;Doan, Litterman, and Sims, 1984) and machine learning methods like factor models (Fan, Li, and Liao, 2020b) and autoencoder (Goodfellow, Bengio, and Courville, 2016).…”

Section: Introductionmentioning

confidence: 99%

The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data

Yang¹,

Pang²,

Huang³

et al. 2020

Preprint

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The current knowledge system of macroeconomics is built on interactions among a small number of variables, since traditional macroeconomic models can mostly handle a handful of inputs. Recent work using big data suggests that a much larger number of variables are active in driving the dynamics of the aggregate economy. In this paper, we introduce a knowledge graph (KG) that consists of not only linkages between traditional economic variables but also new alternative big data variables. We extract these new variables and the linkages by applying advanced natural language processing (NLP) tools on the massive textual data of academic literature and research reports. As one example of the potential applications, we use it as the prior knowledge to select variables for economic forecasting models in macroeconomics. Compared to statistical variable selection methods, KG-based methods achieve significantly higher forecasting accuracy, especially for long run forecasts.

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