Working Paper Series

By Serena Ng. 2019

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Principal Components and Regularized Estimation of Factor Models (2017, methodology): “The result that (A,B) solves the problem (11) if and only if L = A B′ solves (10) was first noted in Rennie and Srebro (2005). Detailed proofs are given in Hastie et al.”
Inverse Moment Methods for Sufficient Forecasting Using High-Dimensional Predictors (2016, unknown): “As noted by McCracken and Ng (2016) 8 factors have good explanatory power in various”
Deep Learning in Asset Pricing (2019, methodology): “Messmer (2017) and Feng, He, and Polson (2018) follow a similar approach as Gu, Kelly, and Xiu (2020) to predict stock returns with neural networks. Bianchi, Büchner, and Tamoni (2019) provide a comparison of machine learning method for predicting bond returns in the spirit of Gu, Kelly, and Xiu (20”
Dynamic factor model with infinite‐dimensional factor space: Forecasting (2016, methodology): “Therefore, as in Stock and Watson (2012), we exclude from the evaluation the variables associated with a superior forecasting performance of AR.”
Large Order-Invariant Bayesian VARs with Stochastic Volatility (2021, methodology): “Next we provide a simple illustration of how the choice of ordering can influence empirical results in large VARs using a dataset of 20 popular U.S. monthly variables obtained from the FRED-MD database (McCracken and Ng 2016).”
Adaptive Shrinkage in Bayesian Vector Autoregressive Models (2019, methodology): “The data we use are a recent version of that constructed in McCracken and Ng (2016).”
Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments (2018, unknown): “With this motivation, column (3) adds lags of four factors computed from the FRED-MD data set (McCracken and Ng, 2016).”
Linear Hypothesis Testing in Dense High-Dimensional Linear Models (2016, methodology): “The macroeconomic variables we use are from the dataset constructed by McCracken and Ng (2015).”
Quantile Factor Models (2017, methodology): “We refer to McCracken and Ng (2016) for the details of a very similar dataset that contains monthly macroeconomic variables.”
Macroeconomic forecast accuracy in a data‐rich environment (2019, unknown): “The logarithm of INDPRO, EMP and the SP500 are treated as I(1) while the logarithm of the CPI is assumed to be I(2), as in Stock and Watson (2002b) and McCracken and Ng (2016).”