Predicting economic activity is important for numerous reasons. It is important for business firms because it aids in deciding how much capacity will be needed to meet future demand. It is important for various government agencies when forecasting budgetary surpluses or deficits. And it is important for the Federal Reserve (the Fed) in deciding the stance of current monetary policy. One set of variables that are potentially useful in forecasting economic activity are financial variables.
Financial market participants are forward-looking, and as a result the prices of various securities embody expectations of future economic activity. This pricing behavior implies that data from financial markets may reasonably be expected to help forecast the growth rate of the economy. Using financial variables to aid in economic projections, therefore, is fairly commonplace. In particular, the yield curve spread between long- and short-term interest rates has received a lot of recent attention. Although not the first to consider the implications that the spread has for predicting economic activity, Stock and Watson (1989) provided much of the impetus for further research by finding that the spread was an important component of their newly constructed index of leading economic indicators. Estrella and Hardouvelis (1991) also thoroughly document the significant relationship between interest rate spreads and future output growth.
Unfortunately, one of the spread's major predictive failures occurred immediately after the publication of these influential articles. Namely, the spread failed to predict the 1990-91 recession. In light of that occurrence, a number of papers reinvestigated the spread's predictive content. Among these are the works of Estrella and Mishkin (1997, 1998), Haubrich and Dombrosky (1996), Plosser and Rouwenhorst (1994), and Dueker (1997). These studies mainly concluded that the spread still contains significant information for predicting economic activity.
This article reinforces the view that the spread is generally a useful variable in predicting future growth in real GDP but also indicates that it has become less useful in recent years. In particular, the recent accuracy of the spread's prediction of GDP growth, both in-sample and out-of-sample, is less precise than over earlier sample periods. In fact, adding the spread to a VAR containing lagged output growth and short-term interest rates increases the root mean squared error of the out-of-sample forecast errors over the period 1985 to 1997.
After briefly reviewing relevant literature, I informally characterize the joint behavior of output growth and the spread. From this characterization it is clear that there is a relationship between the two variables, although that relationship is far from perfect. I then attempt to expand on the existing literature by analyzing the predictive content of the spread along a number of new dimensions. In particular, I examine whether there are nonlinearities in the relationship and whether the predictive content of the spread is closely associated with the stance of monetary policy. Further, the results here indicate the important differences between evaluations based on in-sample versus outof-sample predictive power. Presumably, it is the latter that is most relevant for judging the ability to forecast.
1. RELATED LITERATURE
There is a wide and growing literature that examines the term structure of interest rates' predictive content for economic activity. The review given here is selective and focuses on articles that significantly influenced the statistical tests carried out later in this article.1 One of the most influential studies is that of Stock and Watson (1989), which systematically attempts to construct a new index of leading economic indicators. Their approach is to examine combinations of 55 various macroeconomic variables and select the combination that best predicts future economic activity. …