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Working Paper
Macroeconomic Indicator Forecasting with Deep Neural Networks
Cook, Thomas R.; Smalter Hall, Aaron (2017-09-29)
Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models {{p}} that exhibit model dependence and have high data demands. {{p}} We explore deep neural networks as an {{p}} opportunity to improve upon forecast accuracy with limited data and while remaining agnostic as to {{p}} functional form. We focus on predicting civilian unemployment using models based on four different neural network architectures. Each of these models outperforms bench- mark models at short time horizons. One model, ...
Research Working Paper , Paper RWP 17-11

Report
Multimodality in Macro-Financial Dynamics
Giannone, Domenico; Boyarchenko, Nina; Adrian, Tobias (2019-11-01)
We estimate the evolution of the conditional joint distribution of economic and financial conditions. While the joint distribution is approximately Gaussian during normal periods, sharp tightenings of financial conditions lead to the emergence of additional modes. The U.S. economy has historically resolved quickly to the “good” mode, but we conjecture that poor policy choices could lead to prolonged periods of multimodality. We argue that multimodality arises naturally in a macro-financial intermediary model with occasionally binding intermediary constraints.
Staff Reports , Paper 903

Working Paper
Better the Devil You Know: Improved Forecasts from Imperfect Models
Oh, Dong Hwan; Patton, Andrew J. (2021-11-05)
Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis- specied model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspecication of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we nd signicant fore- cast improvements from ...
Finance and Economics Discussion Series , Paper 2021-071

Working Paper
Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book
Winkelmann, Lars; Neely, Christopher J.; Bibinger, Markus (2017-04-26)
An extensive empirical literature documents a generally negative correlation, named the ?leverage effect,? between asset returns and changes of volatility. It is more challenging to establish such a return-volatility relationship for jumps in high-frequency data. We propose new nonparametric methods to assess and test for a discontinuous leverage effect ? i.e. a relation between contemporaneous jumps in prices and volatility ? in high-frequency data with market microstructure noise. We present local tests and estimators for price jumps and volatility jumps. Five years of transaction data from ...
Working Papers , Paper 2017-12

Report
On binscatter
Cattaneo, Matias D.; Crump, Richard K.; Farrell, Max H.; Feng , Yingjie (2019-02-01)
Binscatter, or a binned scatter plot, is a very popular tool in applied microeconomics. It provides a flexible, yet parsimonious way of visualizing and summarizing mean, quantile, and other nonparametric regression functions in large data sets. It is also often used for informal evaluation of substantive hypotheses such as linearity or monotonicity of the unknown function. This paper presents a foundational econometric analysis of binscatter, offering an array of theoretical and practical results that aid both understanding current practices (that is, their validity or lack thereof) as well ...
Staff Reports , Paper 881

Working Paper
Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective
Liu, Laura (2018-05-22)
This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients and cross-sectional heteroskedasticity. The panel considered in this paper features a large cross-sectional dimension N but short time series T. Due to the short T, traditional methods have difficulty in disentangling the heterogeneous parameters from the shocks, which contaminates the estimates of the heterogeneous parameters. To tackle this problem, I assume that there is an underlying distribution of heterogeneous parameters, ...
Finance and Economics Discussion Series , Paper 2018-036

Working Paper
Bayesian Estimation and Comparison of Conditional Moment Models
Simoni, Anna; Shin, Minchul; Chib, Siddhartha (2019-12-09)
We provide a Bayesian analysis of models in which the unknown distribution of the outcomes is speci?ed up to a set of conditional moment restrictions. This analysis is based on the nonparametric exponentially tilted empirical likelihood (ETEL) function, which is constructed to satisfy a sequence of unconditional moments, obtained from the conditional moments by an increasing (in sample size) vector of approximating functions (such as tensor splines based on the splines of each conditioning variable). The posterior distribution is shown to satisfy the Bernstein-von Mises theorem, subject to a ...
Working Papers , Paper 19-51

Working Paper
Better Bunching, Nicer Notching
Seegert, Nathan; McCallum, Andrew H.; Bertanha, Marinho (2021-01-12)
We study the bunching identification strategy for an elasticity parameter that summarizes agents' response to changes in slope (kink) or intercept (notch) of a schedule of incentives. A notch identifies the elasticity but a kink does not, when the distribution of agents is fully flexible. We propose new non-parametric and semi-parametric identification assumptions on the distribution of agents that are weaker than assumptions currently made in the literature. We revisit the original empirical application of the bunching estimator and find that our weaker identification assumptions result in ...
Finance and Economics Discussion Series , Paper 2021-002

Working Paper
Nonparametric Estimation of Lerner Indices for U.S. Banks Allowing for Inefficiency and Off-Balance Sheet Activities
Wheelock, David C.; Wilson, Paul W. (2019-04-16)
The Lerner index is widely used to assess firms' market power. However, estimation and interpretation present several challenges, especially for banks, which tend to produce multiple outputs and operate with considerable inefficiency. We estimate Lerner indices for U.S. banks for 2001-18 using nonparametric estimators of the underlying cost and profit functions, controlling for inefficiency, and incorporating banks' off-balance-sheet activities. We find that mis-specification of cost or profit functional forms can seriously bias Lerner index estimates, as can failure to account for ...
Working Papers , Paper 2019-12

Working Paper
Bayesian Nonparametric Learning of How Skill Is Distributed across the Mutual Fund Industry
Fisher, Mark; Jensen, Mark J.; Tkac, Paula A. (2019-03-01)
In this paper, we use Bayesian nonparametric learning to estimate the skill of actively managed mutual funds and also to estimate the population distribution for this skill. A nonparametric hierarchical prior, where the hyperprior distribution is unknown and modeled with a Dirichlet process prior, is used for the skill parameter, with its posterior predictive distribution being an estimate of the population distribution. Our nonparametric approach is equivalent to an infinitely ordered mixture of normals where we resolve the uncertainty in the mixture order by partitioning the funds into ...
FRB Atlanta Working Paper , Paper 2019-3

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