Showing results 1 to 9 of approximately 9.(refine search)
Real-time inflation forecasting in a changing world
This paper revisits the accuracy of inflation forecasting using activity and expectations variables. We apply Bayesian-model averaging across different regression specifications selected from a set of potential predictors that includes lagged values of inflation, a host of real activity data, term structure data, nominal data, and surveys. In this model average, we can entertain different channels of structural instability by incorporating stochastic breaks in the regression parameters of each individual specification within this average, allowing for breaks in the error variance of the ...
The Accuracy of Forecasts Prepared for the Federal Open Market Committee
We analyze forecasts of consumption, nonresidential investment, residential investment, government spending, exports, imports, inventories, gross domestic product, inflation, and unemployment prepared by the staff of the Board of Governors of the Federal Reserve System for meetings of the Federal Open Market Committee from 1997 to 2008, called the Greenbooks. We compare the root mean squared error, mean absolute error, and the proportion of directional errors of Greenbook forecasts of these macroeconomic indicators to the errors from three forecasting benchmarks: a random walk, a first-order ...
Two Measures of Core Inflation: A Comparison
Trimmed-mean Personal Consumption Expenditure (PCE) inflation does not clearly dominate ex-food-and-energy PCE inflation in real-time forecasting of headline PCE inflation. However, trimmed-mean inflation is the superior communications and policy tool because it is a less-biased real-time estimator of headline inflation and because it more successfully filters out headline inflation?s transitory variation, leaving only cyclical and trend components.
Forecasting Consumption Spending Using Credit Bureau Data
This paper considers whether the inclusion of information contained in consumer credit reports might improve the predictive accuracy of forecasting models for consumption spending. To investigate the usefulness of aggregate consumer credit information in forecasting consumption spending, this paper sets up a baseline forecasting model. Based on this model, a simulated real-time, out-of-sample exercise is conducted to forecast one-quarter ahead consumption spending. The exercise is run again after the addition of credit bureau variables to the model. Finally, a comparison is made to test ...
Measurement Errors and Monetary Policy: Then and Now
Should policymakers and applied macroeconomists worry about the difference between real-time and final data? We tackle this question by using a VAR with time-varying parameters and stochastic volatility to show that the distinctionbetween real-time data and final data matters for the impact of monetary policy shocks: The impact on final data is substantially and systematically different (in particular, larger in magnitude for different measures of real activity) from theimpact on real-time data. These differences have persisted over the last 40 years and should be taken into account when ...
Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy
This paper constructs hybrid forecasts that combine both short- and long-term conditioning information from external surveys with forecasts from a standard fixed-coefficient vector autoregression (VAR) model. Specifically, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of ...
Nowcasting U.S. Headline and Core Inflation
Forecasting future inflation and nowcasting contemporaneous inflation are difficult. We propose a new and parsimonious model for nowcasting headline and core inflation in the U.S. price index for personal consumption expenditures (PCE) and the consumer price index (CPI). The model relies on relatively few variables and is tested using real-time data. The model?s nowcasting accuracy improves as information accumulates over the course of a month or quarter, and it easily outperforms a variety of statistical benchmarks. In head-to-head comparisons, the model?s nowcasts of CPI infl ation ...
Frequency Dependence in a Real-Time Monetary Policy Rule
We estimate a monetary policy rule for the US allowing for possible frequency dependence?i.e., allowing the central bank to respond differently to more persistent innovations than to more transitory innovations, in both the unemployment rate and the inflation rate. Our estimation method uses real-time data in these rates?as did the FOMC?and requires no a priori assumptions on the pattern of frequency dependence or on the nature of the processes generating either the data or the natural rate of unemployment. Unlike other approaches, our estimation method allows for possible feedback in the ...
Forecasting GDP Growth with NIPA Aggregates
Beyond GDP, which is measured using expenditure data, the U.S. national income and product accounts (NIPAs) provide an income-based measure of the economy (gross domestic income, or GDI), a measure that averages GDP and GDI, and various aggregates that include combinations of GDP components. This paper compiles real-time data on a variety of NIPA aggregates and uses these in simple time-series models to construct out-of-sample forecasts for GDP growth. Over short forecast horizons, NIPA aggregates?particularly consumption and GDP less inventories and trade?together with these simple ...