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Working Paper
Forecasting Economic Activity with Mixed Frequency Bayesian VARs
Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate a large number of mixed frequency indicators into forecasts of economic activity. This paper evaluates the forecast performance of MF-BVARs relative to surveys of professional forecasters and investigates the influence of certain specification choices on this performance. We leverage a novel real-time dataset to conduct an out-of-sample forecasting exercise for U.S. real gross domestic product (GDP). MF-BVARs are shown to provide an attractive alternative to surveys of professional forecasters for ...
Working Paper
Understanding house price index revisions
Residential house price indexes (HPI) are used for a large variety of macroeconomic and microeconomic research and policy purposes, as well as for automated valuation models. As is well known, these indexes are subject to substantial revisions in the months following the initial release, both because transaction data can be slow to come in, and as a consequence of the repeat sales methodology, which interpolates the effect of sales over the entire period since the house last changed hands. We study the properties of the revisions to the CoreLogic House Price Index. This index is used both by ...
Working Paper
Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates
Recent decades have seen advances in using econometric methods to produce more timely and higher-frequency estimates of economic activity at the national level, enabling better tracking of the economy in real time. These advances have not generally been replicated at the sub–national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed– frequency Bayesian VAR model to address common ...
Working Paper
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 ...
Working Paper
Information in the revision process of real-time datasets
Rationality of early release data is typically tested using linear regressions. Thus, failure to reject the null does not rule out the possibility of nonlinear dependence. This paper proposes two tests which instead have power against generic nonlinear alternatives. A Monte Carlo study shows that the suggested tests have good finite sample properties. Additionally, we carry out an empirical illustration using a real-time dataset for money, output, and prices. Overall, we find strong evidence against data rationality. Interestingly, for money stock the null is not rejected by linear tests but ...
Working Paper
Time-varying Uncertainty of the Federal Reserve’s Output Gap Estimate
What is the output gap and when do we know it? A factor stochastic volatility model estimates the common component to forecasts of the output gap produced by the staff of the Federal Reserve, its time-varying volatility, and time-varying, horizon-specific forecast uncertainty. The common factor to these forecasts is highly procyclical, and unexpected increases to the common factor are associated with persistent responses in other macroeconomic variables. However, output gap estimates are very uncertain, even well after the fact. Output gap uncertainty increases around business cycle turning ...
Working Paper
Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting
We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produces for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these new data to study whether the staff forecasts efficiently and whether efficiency, or lack thereof, is time-varying. Prespecified regressions of forecast errors on forecast revisions show that the staff's ...
Working Paper
Tests of equal predictive ability with real-time data
This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy applied to direct, multi-step predictions from both non-nested and nested linear regression models. In contrast to earlier work in the literature, our asymptotics take account of the real-time, revised nature of the data. Monte Carlo simulations indicate that our asymptotic approximations yield reasonable size and power properties in most circumstances. The paper concludes with an examination of the real-time predictive content of various measures of economic activity for inflation.
Working Paper
The Fed's Asymmetric Forecast Errors
I show that the probability that the Board of Governors of the Federal Reserve System staff's forecasts (the "Greenbooks'") overpredicted quarterly real gross domestic product (GDP) growth depends on both the forecast horizon and also whether the forecasted quarter was above or below trend real GDP growth. For forecasted quarters that grew below trend, Greenbooks were much more likely to overpredict real GDP growth, with one-quarter ahead forecasts overpredicting real GDP growth more than 75% of the time, and this rate of overprediction was higher for further ahead forecasts. For forecasted ...
Working Paper
Forecasting of small macroeconomic VARs in the presence of instabilities
Small-scale VARs have come to be widely used in macroeconomics, for purposes ranging from forecasting output, prices, and interest rates to modeling expectations formation in theoretical models. However, a body of recent work suggests such VAR models may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. These methods include using different approaches to lag selection, observation windows for estimation, (over-) differencing, intercept correction, stochastically ...