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Jel Classification:C14 

Working Paper
Nonparametric Estimation of Lerner Indices for U.S. Banks Allowing for Inefficiency and Off-Balance Sheet Activities

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

Report
On binscatter

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
Macroeconomic Forecasting in Times of Crises

We propose a parsimonious semiparametric method for macroeconomic forecasting during episodes of sudden changes. Based on the notion of clustering and similarity, we partition the time series into blocks, search for the closest blocks to the most recent block of observations, and with the matched blocks we proceed to forecast. One possibility is to compare local means across blocks, which captures the idea of matching directional movements of a series. We show that our approach does particularly well during the Great Recession and for variables such as inflation, unemployment, and real ...
Finance and Economics Discussion Series , Paper 2017-018

Working Paper
Better Bunching, Nicer Notching

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
Robust Inference in First-Price Auctions : Experimental Findings as Identifying Restrictions

In laboratory experiments bidding in first-price auctions is more aggressive than predicted by the risk-neutral Bayesian Nash Equilibrium (RNBNE) - a finding known as the overbidding puzzle. Several models have been proposed to explain the overbidding puzzle, but no canonical alternative to RNBNE has emerged, and RNBNE remains the basis of the structural auction literature. Instead of estimating a particular model of overbidding, we use the overbidding restriction itself for identification, which allows us to bound the valuation distribution, the seller's payoff function, and the optimal ...
Finance and Economics Discussion Series , Paper 2019-006

Working Paper
The Evolution of Scale Economies in U.S. Banking

Continued consolidation of the U.S. banking industry and a general increase in the size of banks has prompted some policymakers to consider policies that discourage banks from getting larger, including explicit caps on bank size. However, limits on the size of banks could entail economic costs if they prevent banks from achieving economies of scale. This paper presents new estimates of returns to scale for U.S. banks based on nonparametric, local-linear estimation of bank cost, revenue and profit functions. We report estimates for both 2006 and 2015 to compare returns to scale some seven ...
Working Papers , Paper 2015-21

Working Paper
Local Polynomial Regressions versus OLS for Generating Location Value Estimates: Which is More Efficient in Out-of-Sample Forecasts?

As an alternative to ordinary least squares (OLS), we estimate location values for single family houses using a standard housing price and characteristics dataset by local polynomial regressions (LPR), a semi-parametric procedure. We also compare the LPR and OLS models in the Denver metropolitan area in the years 2003, 2006 and 2010 with out-of-sample forecasting. We determine that the LPR model is more efficient than OLS at predicting location values in counties with greater densities of sales. Also, LPR outperforms OLS in 2010 for all 5 counties in our dataset. Our findings suggest that LPR ...
Working Papers , Paper 2015-14

Working Paper
The Taylor rule and forecast intervals for exchange rates

This paper attacks the Meese-Rogoff (exchange rate disconnect) puzzle from a different perspective: out-of-sample interval forecasting. Most studies in the literature focus on point forecasts. In this paper, we apply Robust Semi-parametric (RS) interval forecasting to a group of Taylor rule models. Forecast intervals for twelve OECD exchange rates are generated and modified tests of Giacomini and White (2006) are conducted to compare the performance of Taylor rule models and the random walk. Our contribution is twofold.> ; First, we find that in general, Taylor rule models generate tighter ...
Globalization Institute Working Papers , Paper 22

Working Paper
Revealing Cluster Structures Based on Mixed Sampling Frequencies

This paper proposes a new nonparametric mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data. The nonparametric MIDAS estimation method is more flexible and substantially simpler to implement than competing approaches. We show that the proposed clustering algorithm successfully recovers true membership in the cross-section, both in theory and in simulations, without requiring prior knowledge of the number of clusters. This methodology is applied to a mixed-frequency Okun's law model for state-level data in the U.S. and ...
Finance and Economics Discussion Series , Paper 2020-082

Working Paper
The role of jumps in volatility spillovers in foreign exchange markets: meteor shower and heat waves revisited

This paper extends the previous literature on geographic (heat waves) and intertemporal (meteor showers) foreign exchange volatility transmission to characterize the role of jumps and cross-rate propagation. We employ heterogeneous autoregressive (HAR) models to capture the quasi-long-memory properties of volatility and the Shapley-Owen R2 measure to quantify the contributions of components. We conclude that meteor showers are more influential than heat waves, that jumps play a modest but significant role in volatility transmission and that significant, bidirectional cross-rate volatility ...
Working Papers , Paper 2014-034

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