Search Results
Working Paper
Delphic and Odyssean Monetary Policy Shocks: Evidence from the Euro Area
What drives the strong reaction of financial markets to central bank communication on the days of policy decisions? We highlight the role of two factors that we identify from high-frequency monetary surprises: news on future macroeconomic conditions (Delphic shocks) and news on future monetary policy shocks (Odyssean shocks). These two shocks move the yield curve in the same direction but have opposite effects on financial conditions and macroeconomic expectations. A drop in future interest rates that is associated with a negative Delphic (Odyssean) shock is perceived as being contractionary ...
Working Paper
A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area
This paper examines point and density forecasts of real GDP growth, inflation and unemployment from the European Central Bank?s Survey of Professional Forecasters. We present individual uncertainty measures and introduce individual point- and density-based measures of disagreement. The data indicate substantial heterogeneity and persistence in respondents? uncertainty and disagreement, with uncertainty associated with prominent respondent effects and disagreement associated with prominent time effects. We also examine the co-movement between uncertainty and disagreement and find an ...
Working Paper
A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area
This paper examines point and density forecasts of real GDP growth, inflation and unemployment from the European Central Bank?s Survey of Professional Forecasters. We present individual uncertainty measures and introduce individual point- and density-based measures of disagreement. The data indicate substantial heterogeneity and persistence in respondents? uncertainty and disagreement, with uncertainty associated with prominent respondent effects and disagreement associated with prominent time effects. We also examine the co-movement between uncertainty and disagreement and find an ...
Working Paper
BLP Estimation Using Laplace Transformation and Overlapping Simulation Draws
We derive the asymptotic distribution of the parameters of the Berry et al. (1995, BLP) model in a many markets setting which takes into account simulation noise under the assumption of overlapping simulation draws. We show that, as long as the number of simulation draws R and the number of markets T approach infinity, our estimator is ?m = ?min(R,T) consistent and asymptotically normal. We do not impose any relationship between the rates at which R and T go to infinity, thus allowing for the case of R
Working Paper
A Local Projections Approach to Difference-in-Differences Event Studies
Many of the challenges in the estimation of dynamic heterogeneous treatment effects can be resolved with local projection (LP) estimators of the sort used in applied macroeconometrics. This approach provides a convenient alternative to the more complicated solutions proposed in the recent literature on Difference in-Differences (DiD). The key is to combine LPs with a flexible ‘clean control’ condition to define appropriate sets of treated and control units. Our proposed LP-DiD estimator is clear, simple, easy and fast to compute, and it is transparent and flexible in its handling of ...
Working Paper
Linear and nonlinear econometric models against machine learning models: realized volatility prediction
This paper fills an important gap in the volatility forecasting literature by comparing a broad suite of machine learning (ML) methods with both linear and nonlinear econometric models using high-frequency realized volatility (RV) data for the S&P 500. We evaluate ARFIMA, HAR, regime-switching HAR models (THAR, STHAR, MSHAR), and ML methods including Extreme Gradient Boosting, deep feed-forward neural networks, and recurrent networks (BRNN, LSTM, LSTM-A, GRU). Using rolling forecasts from 2006 onward, we find that regime-switching models—particularly THAR and STHAR—consistently outperform ...
Working Paper
Parallel Trends Forest: Data-Driven Control Sample Selection in Difference-in-Differences
This paper introduces parallel trends forest, a novel approach to selecting optimal control samples when using difference-in-differences (DiD) in a relatively long panel data with little randomization in treatment assignment. Our method uses machine learning techniques to find control units that best meet the parallel trends assumption. We demonstrate that our approach outperforms existing methods, particularly with noisy, granular data. Applying the parallel trends forest to analyze the impact of post-trade transparency in corporate bond markets, we find that it produces more robust ...
Working Paper
Liquidity Networks, Interconnectedness, and Interbank Information Asymmetry
Network analysis has demonstrated that interconnectedness among market participants results in spillovers, amplifies or absorbs shocks, and creates other nonlinear effects that ultimately affect market health. In this paper, we propose a new directed network construct, the liquidity network, to capture the urgency to trade by connecting the initiating party in a trade to the passive party. Alongside the conventional trading network connecting sellers to buyers, we show both network types complement each other: Liquidity networks reveal valuable information, particularly when information ...
Working Paper
Manufacturing Sentiment: Forecasting Industrial Production with Text Analysis
This paper examines the link between industrial production and the sentiment expressed in natural language survey responses from U.S. manufacturing firms. We compare several natural language processing (NLP) techniques for classifying sentiment, ranging from dictionary-based methods to modern deep learning methods. Using a manually labeled sample as ground truth, we find that deep learning models partially trained on a human-labeled sample of our data outperform other methods for classifying the sentiment of survey responses. Further, we capitalize on the panel nature of the data to train ...
Working Paper
Social Security and High-Frequency Labor Supply: Evidence from Uber Drivers
We estimate the impact of anticipated transfers on labor supply using confidential driver-level data from Uber. Leveraging the staggered timing of Social Security retirement benefits within each month and a novel identification strategy, we find that the labor supply of older drivers declines by 2% on average in the week around benefit receipt—a precisely estimated but economically small effect. Individual-level analyses reveal that the average effect obscures heterogeneous micro-behavior: while the majority of drivers does not meaningfully adjust labor supply in response to social security ...