FINANCIAL MARKET VARIABLES DO NOT PREDICT REAL ACTIVITY

I. INTRODUCTION The past decade has seen an extensive empirical reassessment of the information content of financial market variables believed to be sensitive to monetary policy. Particularly provocative are papers suggesting that some interest rates and interest rate spreads contain more information about economic activity than monetary aggregates. This paper reviews important methodological pitfalls in these studies. We then show that none of the commonly employed indicators of monetary policy contain incremental information useful in forecasting real economic activity, as measured by industrial production. Two conclusions are possible. Either monetary policy innovations have no significant real effects, or we (collectively) have failed in our efforts to measure monetary policy. The money-income causality literature spawned by Christopher Sims in the 1970s has broadened in recent years to encompass a diverse set of measures of monetary policy, more comprehensive and sophisticated empirical assessment strategies, and a richer array of explanations for observed correlations between financial market variables and economic activity. "Early" contributors to this literature, such as Stock and Watson [1989], Friedman and Kuttner [1992], and Bernanke and Blinder [1992], present evidence that particular interest rates and spreads not only dominate monetary aggregates as predictors of economic performance, but are remarkably powerful predictors. Stock and Watson [1989] and Friedman and Kuttner [1992] demonstrate that the spread between the interest rates on commercial paper and treasury bills is highly significant in explaining movements in real activity, while Bernanke and Blinder [1992] made an equally convincing case for the federal funds rate. These conclusions are qualified, however, in subsequent work. Both Bernanke [1990] and Stock and Watson [1993], for example, point out that the predictive power of the paper-bill spread (and interest rates more generally) weakened during the second half of the 1980s and the early 1990s. The results reported in these and related papers form the empirical core of the debate over the relative merits of various interest rates, spreads and monetary aggregates as indicators of the stance of monetary policy. Subsequent empirical work has seen a shift from "atheoretical" to "structural" vector autoregressions. Strongin [1992], Gordon and Leeper [1994] and Bernanke and Mihov [1995], for example, focus on more refined measures of monetary policy derived from explicit models of the reserve market and central bank operating procedures. These papers address concerns that go beyond discriminating among variables on the basis of predictive power. They grapple directly with the fundamentally difficult issue of identifying monetary policy shocks by positing and estimating structural models that take the form of identified VARs. The usefulness of these efforts as well as earlier efforts to measure monetary policy is, in turn, questioned by Rudebusch [1996], who finds that the studies' econometric results are fragile and at odds with other evidence on the nature of the Fed's reaction function and monetary policy surprises. The first part of our paper illustrates some of the methodological pitfalls common in the literature cited above, and complements recent work by Bernanke [1990], Hess and Porter [1993], Thoma and Gray [1995], Rudebusch [1996], and Emery [1996]. We begin by demonstrating the advantages of rolling (recursive) regression techniques that directly and comprehensively address the question of sample sensitivity. These techniques are used to illustrate the extreme sensitivity to sample period of the causality statistics and variance decompositions commonly used to assess the explanatory power of financial market variables. Indeed, in the applications under review here, such in-sample measures of fit are sometimes heavily influenced by individual monthly observations. …