Search Results
Working Paper
Censored Density Forecasts: Production and Evaluation
This paper develops methods for the production and evaluation of censored density forecasts. Censored density forecasts quantify forecast risks in a middle region of the density covering a specified probability, and ignore the magnitude but not the frequency of outlying observations. We propose a new estimator that fits a potentially skewed and fat-tailed density to the inner observations, acknowledging that the outlying observations may be drawn from a different but unknown distribution. We also introduce a new test for calibration of censored density forecasts. An application using ...
Working Paper
Missing Data Substitution for Enhanced Robust Filtering and Forecasting in Linear State-Space Models
Replacing faulty measurements with missing values can suppress outlier-induced distortions in state-space inference. We therefore put forward two complementary methods for enhanced outlier-robust filtering and forecasting: supervised missing data substitution (MD) upon exceeding a Huber threshold, and unsupervised missing data substitution via exogenous randomization (RMDX).Our supervised method, MD, is designed to improve performance of existing Huber-based linear filters known to lose optimality when outliers of the same sign are clustered in time rather than arriving independently. The ...
Working Paper
Censored Density Forecasts: Production and Evaluation
This paper develops methods for the production and evaluation of censored density forecasts. The focus is on censored density forecasts that quantify forecast risks in a middle region of the density covering a specified probability, and ignore the magnitude but not the frequency of outlying observations. We propose a fixed-point algorithm that fits a potentially skewed and fat-tailed density to the inner observations, acknowledging that the outlying observations may be drawn from a different but unknown distribution. We also introduce a new test for calibration of censored density forecasts. ...
Report
Monetary Policy across Inflation Regimes
Does the effect of monetary policy depend on the prevailing level of inflation? In order to answer this question, we construct a parsimonious nonlinear time series model that allows for inflation regimes. We find that the effects of monetary policy are markedly different when year-over-year inflation exceeds 5.5 percent. Below this threshold, changes in monetary policy have a short-lived effect on prices, but no effect on the unemployment rate, giving a potential explanation for the recent “soft landing” in the United States. Above this threshold, the effects of monetary policy surprises ...
Working Paper
Addressing COVID-19 Outliers in BVARs with Stochastic Volatility
Incoming data in 2020 posed sizable challenges for the use of VARs in economic analysis: Enormous movements in a number of series have had strong effects on parameters and forecasts constructed with standard VAR methods. We propose the use of VAR models with time-varying volatility that include a treatment of the COVID extremes as outlier observations. Typical VARs with time-varying volatility assume changes in uncertainty to be highly persistent. Instead, we adopt an outlier-adjusted stochastic volatility (SV) model for VAR residuals that combines transitory and persistent changes in ...
Working Paper
Addressing COVID-19 Outliers in BVARs with Stochastic Volatility
The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that ...