We provide estimation methods for panel nonseparable models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data.
Economists are often interested in estimating averages with respect to distributions of unobservables. Examples are moments of individual fixed-effects, average partial effects in discrete choice models, and counterfactual simulations in structural models.
Many empirical questions concern target parameters selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data.
We study consumer spending dynamics during the first infection wave of the COVID-19 pandemic using household scanner data covering fast-moving consumer goods in the United Kingdom.
This paper provides estimation and inference methods for a structural function, such as Conditional Average Treatment Effect (CATE), based on modern machine learning (ML) tools.
We examine a kernel regression estimator for time series that takes account of the error correlation structure as proposed by Xiao et al. (2003, Journal of the American Statistical Association 98, 980–992). We show that this method continues to improve estimation in the case where the regressor is a unit root or a near unit root process.
This paper studies the estimation problem of the covariance matrices of asset returns in the presence of microstructure noise and asynchronicity between the observations across different assets.
We propose several multivariate variance ratio statistics for “testing” the weak form Efficient Market Hypothesis and for measuring the direction and magnitude of departures from this hypothesis.
The exponential GARCH (EGARCH) model introduced by Nelson (1991) is a popular model for discrete time volatility since it allows for asymmetric effects and naturally ensures positivity even when including exogenous variables.
We propose an alternative Ratio Statistic for measuring predictability of stock prices. Our statistic is based on actual returns rather than logarithmic returns and is therefore better suited to capturing price predictability.
