PUBLICATIONS

How Do Firms' Financial Conditions Influence the Transmission of Monetary Policy? A Non-parametric Local Projection Approach, Journal of Econometrics

[Paper] [Slides] [Codes coming soon!]

SNDE Young Scholars Award for the best paper presented by a graduate student

Abstract

How do monetary policy shocks affect firm investment? This paper provides new evidence on US non-financial firms and a novel non-parametric framework based on random forests. The key advantage of the methodology is that it does not impose any assumptions on how the effect of shocks varies across firms thereby allowing for general forms of heterogeneity in the transmission of shocks. My estimates suggest that there exists a threshold in the level of firm risk above which monetary policy is much less effective. Additionally, there is no evidence that the effect of policy varies with firm risk for the 75% of firms in the sample with higher risk. The proposed methodology is a generalization of local projections and nests many common local projection specifications, including linear and nonlinear.

Predicting Inflation with Recurrent Neural Networks, International Journal of Forecasting

[Paper] [Slides]

Older versions: [ArXiv]  [Warwick WP] 

Media coverage: Francis X. Diebold's blog No Hesitations

Abstract

This paper applies a recurrent neural network, long short-term memory (LSTM), to forecast inflation. This is an appealing model for time series, as it processes each time step sequentially and explicitly learns dynamic dependencies. The paper also explores the dimension reduction capability of the model to uncover economically meaningful factors that can explain the inflation process. Results from an exercise with US data indicate that the estimated neural networks present competitive, but not outstanding, performance against common benchmarks (including other machine learning models). LSTM in particular is found to perform well at long horizons and during periods of heightened macroeconomic uncertainty. Interestingly, LSTM-implied factors are highly correlated with business cycle indicators, informing on the usefulness of such signals as inflation predictors. The paper also sheds light on the impact of network initialization and architecture on forecasting performance. 

WORK IN PROGRESS

Inflation Prediction with Sparse Price Data: Evidence from the UK

Nonparametric Impulse Responses and Heterogeneous Shocks, joined with Andreas Joseph and George Kapetanios