Time-Varying US Government Spending Anticipation in Real Time

Abstract

Due to legislation and implementation lags, forward-looking economic agents anticipate changes in fiscal policy variables before they actually occur. The literature shows that this foresight poses a challenge to the econometric analysis of fiscal policies. While most of the literature uses fully revised data to investigate the degree of fiscal foresight, we use forecasts from the Survey of Professional Forecasters (SPF), the Greenbook/Tealbook from the Federal Reserve, and the Real-Time Data Set for Macroeconomists. Furthermore, we distinguish between federal as well as state and local consumption & investment expenditures. We find that real-time data matter. Using the first release, the SPF nowcast was able to predict 43% of the out-of-sample fluctuation in federal government spending growth (only 24% using the most recent release). Moreover, the SPF was able to predict 60% and 52% of the cumulated growth in federal and state & local government spending growth over a 1-year horizon. We use the Diebold–Mariano tests and model confidence sets to investigate whether SPF forecasts significantly outperform the Greenbook projections and forecasts from purely backward-looking time series models. Compared to the SPF and Greenbook projections, the time series models perform inferior at most forecast horizons. In addition, so-called information advantage regressions reveal that most forecasts could be improved by using the information of the SPF. Using rolling windows, we document remarkable time-variation in the degree of fiscal foresight of the SPF and its information advantage against (augmented) autoregressive models and the Greenbook. Particularly during the 1980s and 2000s, we find a strong degree of anticipation for government spending at the federal level by the SPF and the central bank.