Forecasting with real-time macroeconomic data: the ragged-edge problem and revisions

Real-time macroeconomic data are typically incomplete for today and the immediate past (?ragged edge?) and subject to revision. To enable more timely forecasts the recent missing data have to be dealt with. In the context of the U.S. leading index we assess four alternatives,paying explicit attention to publication lags and data revisions.

Aggregate Stock Market Illiquidity and Bond Risk Premia

We assess the effect of aggregate stock market illiquidity on U.S. Treasury bond risk premia. We find that the stock market illiquidity variable adds to the well established Cochrane-Piazzesi and Ludvigson-Ng factors. It explains 10%, 9%, 7%, and 7% of the one-year-ahead variation in the excess return for two-, three-, four-, and ve-year bonds respectively and increases the adjusted R2 by 3-6% across all maturities over Cochrane and Piazzesi (2005) and Ludvigson and Ng (2009) factors. The effects are highly statistically and economically significant both in and out of sample. We find that our result is robust to and is not driven by information from open interest in the futures market, long-run inflation expectations, dispersion in beliefs, and funding liquidity. We argue that stock market illiquidity is a timely variable that is related to " right-to-quality" episodes and might contain information about expected future business conditions through funding liquidity and investment channels

Forecasting Day-Ahead Electricity Prices

The daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average price. Multivariate models for the full panel of hourly prices significantly outperform univariate models of the daily average price, with reductions in Root Mean Squared Error of up to 16%. Substantial care is required in order to achieve these forecast improvements. Rich multivariate models are needed to exploit the relations between different hourly prices, but the risk of overfitting must be mitigated by using dimension reduction techniques, shrinkage and forecast combinations

Forecasting with real-time macroeconomic data: The ragged-edge problem and revisions

Real-time macroeconomic data are typically incomplete for today and the immediate past ('ragged edge') and subject to revision. To enable more timely forecasts the recent missing data have to be imputed. The paper presents a state-space model that can deal with publication lags and data revisions. The framework is applied to the US leading index. We conclude that including even a simple model of data revisions improves the accuracy of the imputations and that the univariate imputation method in levels adopted by The Conference Board can be improved upon