Economic determinants of the nominal treasury yield curve

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Abstract

Macroeconomic shocks account for most of the variability of nominal Treasury yields, inducing parallel shifts in the level of the yield curve. We develop a new approach to identifying macroeconomic shocks that exploits model-based empirical shock measures. Technology shocks shift yields through their effect on expected inflation and the term premium. Shocks to preferences for current consumption affect yields through their impact on real rates and expected inflation. For both shocks, the systematic reaction of monetary policy is an important transmission pathway. We find little evidence that fiscal policy shocks are an important source of interest rate variability.

Introduction

Treasury yields are thought to assimilate vast amounts of information about the economy, including information on the current stance of monetary and fiscal policy, as well as expectations of future economic activity, real interest rates, and inflation. For these reasons one would expect to find links between movements in the nominal Treasury yields and observed macroeconomic shocks.

While a major theme of finance research is to understand the factors that move the term structure, little work to date has focused on observable macroeconomic factors. Rather, most recent work on the term structure assumes that interest rate changes are driven by unobserved factors.1 An important exception is Ang and Piazzesi (2003). They introduce two observable macroeconomic factors into a Dai and Singleton (2000)-type affine model of the yield curve. They find that their inflation and activity factors explain up to 85% of the long-horizon variance of shorter-term yields, but have a much smaller effect on long yields.

In this paper, we ask how macroeconomic impulses affect the nominal yield curve. Our first exercise confirms Ang and Piazzesi's (2003) result that most of the variability of short- and medium-term yields is driven by macroeconomic factors. Unlike these authors, however, we find that macro impulses also account for most of the variance of long-term yields as well. The key source of this discrepancy is that, unlike Ang and Piazzesi (2003), our model incorporates interest rate smoothing, in that interest rates depend on their own lagged values. We show that when interest rate smoothing is omitted, the importance of macroeconomic shocks for interest rates is severely attenuated.

Our second set of results provides evidence on how specific types of shocks affect the yield curve. To identify economic shocks, we develop an approach that is new to the VAR literature. Instead of imposing a priori covariance restrictions on the relation between the VAR innovations and shocks, we infer these relationships from empirical measures of economic shocks that economists have proposed, often based on dynamic general equilibrium models. Our model-based measures include: Basu et al. (2001) measure of technology shocks; Blanchard and Perotti's (2000) measure of fiscal policy shocks; and a measure of marginal-rate-of-substitution (MRS) shocks similar to that studied by Hall (1997). We show how this information is easily incorporated into the analysis of impulse responses.

We find that our MRS shock moves output, real interest rates, and inflation in the same direction, inducing a large, significant, and persistent response in all nominal rates and shifting the yield curve level. In contrast, an expansionary technology shock moves output and the real rate up but drives expected inflation down, so its effect on nominal interest rates is, in principle, ambiguous. However, we find that the expected inflation response dominates, so the expansionary technology shock tends to reduce interest rates of all maturities. Our model-based measure of fiscal shocks does not have a significant impact on interest rates.

Our third set of results relates to the transmission mechanisms by which these shocks move the yield curve. We find that the systematic response of monetary policy is an important pathway whereby macroeconomic shocks affect interest rates. Monetary policy generally reacts to these shocks in the manner predicted by the Taylor (1993) principle: shocks that increase expected inflation or the gap between actual and potential output tend to increase the Federal funds rate. Longer-term interest rates are affected by expectations of changes in the funds rate. In addition, technology shocks directly affect term premiums.

The remainder of this paper is structured as follows: In Section 2 we describe our basic statistical framework. In Section 3 we conduct a preliminary empirical exploration on the effect of macroeconomic factors on the yield curve. This section uses an eigenvalue decomposition of impulse responses to examine the implications of interest rate smoothing for the transmission of macroeconomic shocks. Section 4 develops our identification methodology that uses model-based shock measures, and Section 5 explains how we implement this methodology empirically. Section 6 presents our empirical findings. Section 7 concludes the paper.

Section snippets

Basic statistical framework

We use the following vector autoregression (VAR) framework throughout our empirical analysis. Let Zt be an (n-1)×1 vector of nonfinancial macroeconomic variables; let FFt denote the federal funds rate (included as the instrument of monetary policy); let Yt(Zt,FFt); and let Rt denote an m×1 vector of zero-coupon Treasury yields. We estimate versions of the following structural VAR:A0GHYtRt=A˜(L)0C˜(L)D˜(L)Yt-1Rt-1+ɛtγt,where A is an n×n nonsingular matrix; H is an m×m nonsingular matrix; G is

Macroeconomic shocks as drivers of interest rates

Our first exercise using framework (1), our “baseline model”, explores the fraction of interest rate variability that can be attributed to macroeconomic shocks. We use monthly data from January 1959 through December 2000, with Z(IP,P,PCOM).2 Here, IP denotes the logarithm of industrial production, P denotes the logarithm of the personal consumption expenditure chain-weight price index, and PCOM denotes the smoothed

Identifying structural shocks using model-based measures

According to the evidence of Section 3, a large fraction of the interest rate variance for all maturities is accounted for by macroeconomic impulses. However, unless substantially more structure is imposed on the model, this description of the data's conditional second moment properties represents an incomplete characterization of the economic determinants of the nominal yield curve. In particular, Eq. (3) implies that identification of the structural shock vector ɛt requires restricting matrix

Model-based measures of structural shocks

To implement the model-based identification strategy described above in Section 4, we must obtain model-based measures of macroeconomic driving shocks. In this section we describe four quarterly model-based measures that we use: technology, preference, fiscal policy and monetary policy.

Empirical results

In this section we explore how macroeconomic shocks affect the term structure using the identification strategy described in Sections 4 and 5. The results are displayed in Figs. 1 and 2. These figures display the responses of macroeconomic variables and yields to the identified MRS and technology shocks. In addition, we plot the responses of the 1-month real rate14 and the 60-month term premium.15

Conclusion

This paper presents empirical evidence that macroeconomic factors account for most of the movement in nominal Treasury yields of maturities ranging from 1 month through 5 years. Technology shocks and shocks to the MRS between consumption and leisure strongly influence the level of the yield curve. In particular, the MRS shock increases both real interest rates and inflation, which together serve to raise the level of the nominal yield curve. The technology shocks produce competing influences,

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  • Cited by (0)

    The paper represents the views of the authors and should not be interpreted as reflecting the views of the Federal Reserve Bank of Chicago or the Federal Reserve System. We have benefitted from helpful comments from Monika Piazzesi, Tao Zha, and an anonymous referee.

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