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Working Paper
Practice Makes Perfect: Learning Effects with Household Point and Density Forecasts of Inflation
This paper shows how both the characteristics and the accuracy of the point and density forecasts from a well-known panel data survey of households' inflationary expectations – the New York Fed's Survey of Consumer Expectations – depend on the tenure of survey respondents. Households' point and density forecasts of inflation become significantly more accurate with repeated practice of completing the survey. These learning gains are best identified when tenure-based combination forecasts are constructed. Tenured households on average produce lower point forecasts of inflation, perceive ...
Working Paper
Probability Forecast Combination via Entropy Regularized Wasserstein Distance
We propose probability and density forecast combination methods that are defined using the entropy regularized Wasserstein distance. First, we provide a theoretical characterization of the combined density forecast based on the regularized Wasserstein distance under the Gaus-sian assumption. Second, we show how this type of regularization can improve the predictive power of the resulting combined density. Third, we provide a method for choosing the tuning parameter that governs the strength of regularization. Lastly, we apply our proposed method to the U.S. inflation rate density forecasting, ...
Working Paper
Tail Sensitivity of US Bank Net Interest Margins: A Bayesian Penalized Quantile Regression Approach
Bank net interest margins (NIM) have been historically stable in the US on average, but this stability deteriorated in the post-2020 period, particularly in the tails of the distribution. Recent literature disagrees on the extent to which banks hedge interest rate risk, and past literature shows that credit risk and persistence are also important considerations for bank NIM. I use a novel approach to Bayesian dynamic panel quantile regression to document heterogeneity in US bank NIM estimated sensitivities to interest rates, credit risk, and own persistence. I find increased sensitivity to ...
Working Paper
No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates
We derive a Bayesian prior from a no-arbitrage affine term structure model and use it to estimate the coefficients of a vector autoregression of a panel of government bond yields, specifying a common time-varying volatility for the disturbances. Results based on US data show that this method improves the precision of both point and density forecasts of the term structure of government bond yields, compared to a fully fledged term structure model with time-varying volatility and to a no-change random walk forecast. Further analysis reveals that the approach might work better than an exact term ...