Dissecting Characteristics Nonparametrically

Abstract

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Economic Predictions with Big Data: The Illusion of Sparsity

“…In our macroeconomic applications, we investigate the predictability of economic activity in the US (Stock and Watson, 2002a), and the determinants of economic growth in a cross-section of countries (Barro andLee, 1994, Belloni et al, 2011). In finance, we study the predictability of the US equity premium (Welch and Goyal, 2008), and the factors that explain the cross-sectional variation of US stock returns (Freyberger et al, 2017). Finally, in our microeconomic analyses, we investigate the factors behind the decline in the crime rate in a cross-section of US states (Donohue andLevitt, 2001, Belloni et al, 2014), and the determinants of rulings in the matter of government takings of private property in US judicial circuits Yeh, 2012, Belloni et al, 2012).…”

Sparse Signals in the Cross‐Section of Returns

“…() explain why norm‐constrained estimators will perform better out‐of‐sample in the presence of estimation error. Both Freyberger, Neuhierl, and Weber () and Feng, Giglio, and Xiu () use the LASSO to identify characteristics related to the cross‐section of expected returns at the monthly horizon. Bryzgalova () develops a new model‐specification test by imposing an ℓ 1 ‐penalty function.…”

Asset Pricing Anomalies and the Low-Risk Puzzle

“…A burgeoning literature in the intersection of asset pricing and econometrics aims at reducing the set of cross-sectional anomalies, or "risk factors." This literature employs machine-learning techniques to evaluate the explanatory power of new factors in addition to existing ones (Feng, Giglio and Xiu, 2017;Freyberger, Neuhierl and Weber, 2017;Kozak, Nagel and Santosh, 2017). My paper adds to this literature by taking an empirically-motivated approach, and shedding light on a viable dimension along which the space of cross-sectional anomalies could be reduced.…”