As an important initial step in the annual budget process, the President presents to Congress each January his budget with details of federal spending activity and priorities. Our paper is a statistical assessment of the merit of the budget figures submitted to Congress. We investigate the overall budget as well as several important specific accounts. An important aspect of our paper is the introduction of a nonparametric methodology which incorporates exact tests for assessing the unbiasedness, and the internal and external consistency of forecasts. The empirical evidence shows that the nonparametric results confirm the presence of bias in forecasts on the outlay side suggested by regression results, but tends to find fewer series exhibiting bias on the revenue side. On the other hand the nonparametric approach lends greater support to the conclusion that the government's budget projections do not fully exploit available information.

Many continuous-time term structure of interest rate models assume a factor structure where the drift and volatility functions are affine functions of the state-variable process. These models involve very specific parametric choices of factors and functional specifications of the drift and volatility. Moreover, under the affine term structure restrictions not all factors necessarily affect interest rates at all maturities simultaneously. This class of so-called affine models covers a wide variety of existing empirical as well as theoretical models in the literature. In this paper we take a very agnostic approach to the specification of these diffusion functions and test implications of the affine term structure restrictions. We do not test a specific model among the class of affine models per se. Instead, the affine term structure restrictions we test are based on the derivatives of the responses of interest rates to the factors. We also test how many and which factors affect a particular rate. These tests are conducted within a framework which models interest rates as functions of “fundamental” factors, and the responses of interest rates to these factors are estimated with nonparametric methods. We consider two sets of factors, one based on key macroeconomic variables, and one based on interest rate spreads. In general, despite their common use we find that the empirical evidence does not support the restrictions imposed by affine models. Besides testing the affine structure restrictions we also uncover a set of fundamental factors which appear remarkably robust in explaining interest rate dynamics at the long and short maturities we consider.

We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short-run from long-run movements. We formulate models with the long-term component driven by inflation and industrial production growth that are in terms of pseudo out-of-sample prediction for horizons of one quarter at par or outperform more traditional time series volatility models at longer horizons. Hence, imputing economic fundamentals into volatility models pays off in terms of long-horizon forecasting. We also find that macroeconomic fundamentals play a significant role even at short horizons.