Uncertainty is a central issue in monetary policy, as the quote from Alan Greenspan above illustrates. Empirical models, however, rarely take account of this, effectively assuming that policymakers ignore uncertainty. The evident focus of policymakers on uncertainty suggests that this assumption is invalid and therefore that empirical models of monetary policy must account for uncertainty. This article considers the effects of uncertainty about the true state of the economy on monetary policy, estimating a monetary policy rule that allows for this.

Our empirical model combines elements of Svensson’s (1997) model of inflation forecast targeting with models drawn from the theoretical literature on optimal monetary policy when there is uncertainty about the true state of the economy, most prominently Svensson and Woodford (2003, 2004) and Swanson (2004). In existing models of monetary policy under certainty, monetary policy affects inflation and the output gap directly, so it is optimal for policymakers to use these variables in forming monetary policy. This is the basis for the Taylor rule (Taylor 1993) model of monetary policy and its subsequent refinements (e.g., Woodford 2003).

Following the literature on monetary policy under uncertainty, our model assumes instead that monetary policy affects the state of the economy, which in turn affects inflation and the output gap. The optimal monetary policy rule is then a certainty equivalent function of the state of the economy. However, the state of the economy is unobserved, so policymakers must infer this from observations of inflation and the output gap. These latter variables therefore act as indicator variables for monetary policy. The optimal predictor of the true state of the economy is a linear function of inflation and the output gap whose parameters are functions of the variances of these variables, which we assume to be time varying.

The resultant empirical model resembles the familiar Taylor rule but where the coefficients on inflation and the output gap are functions of the variances of these variables. An increase in, for example, the variance of inflation reduces the parameter on inflation and increases the parameter on the output gap in the equation for the expected state of the economy. This leads to a smaller weight on inflation and a larger weight on the output gap in the monetary policy rule. Similarly, an increase in the variance of the output gap reduces the weight on the output gap and increases the weight on inflation in the equation for the expected state of the economy, resulting in a lower weight on the output gap and a corresponding larger weight on inflation. As a result, the model makes two main testable predictions. First, policymakers should respond less vigorously to variables that are more uncertain, so the weight on inflation in the policy rule should be lower when inflation is more uncertain and similarly for the output gap (Peersman and Smets 1999; Rudebusch 2001; Smets 2002; Soderstrom 2002; Srour 2003; Swanson 2004; Walsh 2004). Second, uncertainty about one variable may strengthen the response to the other variable, so the weight on the output gap may be larger when inflation is less certain and vice versa (cf. Peersman and Smets 1999; Swanson 2004). (1)

We estimate a system of equations, comprising a monetary policy rule whose parameters are functions of the variances of inflation and the output gap and equations for inflation and the output gap whose error terms have GARCH processes, from which these variances are derived. We use data since 1983 since this is when the Federal Reserve Bank (Fed) switched to using the interest rate as the tool of monetary policy and since continuity in monetary policy objectives has allowed stable policy rules to be estimated over this period (e.g., Judd and Rudebusch 1998). We find that the behavior of monetary policymakers is consistent with the predictions of the theoretical literature. Monetary policy responds less to inflation and the output gap when these variables are more uncertain. We also find that the response to inflation is stronger when the output gap is more uncertain and vice versa. We quantify the impact of uncertainty by constructing a measure of the counterfactual interest rate, which would have been observed if there had been no uncertainty. We find that the impact of uncertainty was most marked in 1983, when uncertainty increased interest rates by up to 140 basis points, in 1990-1991, when uncertainty reduced interest rates by up to 80 basis points, and in 1996-2001, when uncertainty reduced interest rates by up to 70 basis points over 5 yr.

1. Open Market Operations
2. Discount Rates
3. Reserve Requirements

Since open market operations is the tool used most, we will cover it. Here’s how it works: When the economy is growing too fast and the Fed is worried about the inflation rate, it will sell government securities from its portfolio to the open market. This decreases bank reserves, which means the money supply decreases. When there are less bank and businesses have to pay the bank more in order to borrow. This discourages consumers and businesses from borrowing. Less borrowing means less spending, which slows the economy and eventually can reduce price pressures.

When the economy is growing too slowly and the inflation rate is low the Fed will buy government securities, such as Treasury bills and notes. This increases bank reserves, which increases the money supply and causes short-term interest rates to decrease. Reduced rates induce consumers and businesses to borrow. Consumers will borrow money for items such as automobiles or home loans. Businesses borrow to build their inventories or finance a new factory. As a result, economic growth will accelerate.

The Fed will also leave rates unchanged if the economy is growing at a moderate pace with low inflation or if they feel the economy will slow down by itself. They will even take a wait-and-see approach with regard to how slowly the economy is growing and the rate of inflation, before determining monetary policy.

The bond market plays close attention to the activities of the Federal Reserve, which is why it’s important for us as well.

The Federal Reserve has three goals:

1. Moderate economic growth (not too fast, not too slow)
2. Low unemployment
3. Low inflation

How does the Fed determine whether they are reaching these goals? They watch the same economic indicators as we do. In other words, they monitor the reports that are released by the Labor Department, the segments of our economy.

For instance, the Gross Domestic Product (GDP) consists of four major components: (1) consumption; (2) investment; (3) government; (4) exports. Most of the key economic indicators fall into one of the above categories. For example:

• Retail sales would fall under consumption.
• Business inventories and housing starts would fall under investment.
• Construction Spending would fall under government.
• Trade would fall under exports.

If the key economic indicators continue to come in strong, the GDP will increase. If the indicators come in weak, it will decrease. In other words, Gross Domestic Product measures economic growth.

Opinions have tended to be divided on the question of the preferred monetary policy framework for the euro area, although recently, there appears to be a consensus emerging in favor of informal inflation targeting, accompanied by monitoring of monetary aggregates and other indicators. In any event, policy discussions have in general tended to focus less on questions relating to the real effects of monetary policy in the EU than on the issue of the appropriate framework for conducting monetary policy in the euro area. This may be partly due to the fact that many of the issues pertaining to identification of the monetary transmission mechanism tend to be econometric rather than economic. Nevertheless, a proper understanding of possible differences in the effects of changes in monetary policy on activity among the EU countries is crucial for an appreciation of the difficulties that may arise from the implementation of a unified monetary policy throughout the euro area. And this issue is the main focus of the paper.

Recent empirical studies of the effects of monetary policy on activity have focused mainly on a subset of EU countries. Gerlach and Smets (1995), using a vector autoregression (VAR) approach with long-run identifying restrictions, found that the effects of a change in the monetary stance on output were somewhat larger in Germany than in France or Italy, while the United Kingdom fell somewhere in between. However, the differences in the transmission of monetary policy documented in the Gerlach-Smets study were not found to be very large. Barran, Coudert, and Mojon (1996) estimate a VAR using the recursive Choleski identifying assumptions to document the differences in the transmission of monetary policy for a group of EU countries. They find that the effect of a contractionary monetary shock on output is relatively long lasting in Germany, with output (relative to baseline) bottoming out about 10 quarters after the shock, somewhat less long lasting in the United Kingdom with output bottoming out after about 8 quarters, whilst in France output reaches the trough about 6 quarters after the shock. A recent Bank of England study by Britton and Whitley (1997), which simulates a variant of the Mundell-Flemming model to analyze the transmission mechanism, found that the response of output to an interest rate shock was smaller in the United Kingdom than in Germany or France, but that the differences in the transmission of monetary policy among these countries were not very large. Dornbusch, Favero, and Giavazzi (1998) estimate the impact of a coordinated monetary policy move on activity in a group of EU countries, controlling for intra-European exchange rates. They find that the “impact-effects” of a change in monetary policy are similar in Germany, France, and the United Kingdom, but smaller than in Sweden and Italy. The full effects of the coordinated monetary policy move are, however, lower in the United Kingdom than in Germany and France, a result that is broadly consistent with that of Britton and Whitley (1997).

An interesting finding that emerges from these studies, which use different estimation strategies, is that there are differences in the effects of monetary policy on activity among the large EU countries. However, these differences do not correspond closely to popular perceptions about how output may be expected to respond to changes in monetary policy. In particular, some of these studies indicate that the response of output to monetary policy actions is not more sensitive in the United Kingdom than it is in some “core” EU countries.

This paper analyzes the nature of the differences in the effects of monetary policy on activity in the EU by examining a larger set of EU countries than previous studies. Moreover, unlike previous empirical studies, which have each relied on one particular model specification for estimating the dynamics of the transmission of monetary policy, this paper examines the robustness of the estimates of the response of output to monetary shocks in the different EU countries with respect to alternative specifications of the VAR approach. The main finding is that, based on estimates using the VAR approach, the EU countries fall into two broad groups as far as the transmission of monetary policy is concerned. In one group (Austria, Belgium, Finland, Germany, the Netherlands, and the United Kingdom) output (relative to baseline) typically bottoms out about 11 to 12 quarters following a contractionary monetary shock, with the decline in output being in the range of 0.7 to 0.9 percent from the baseline. In the other group (Denmark, France, Italy, Portugal, Spain, and Sweden), output typically bottoms out about 5 to 6 quarters after a contractionary monetary shock, with the decline in output being in the range of 0.4 to 0.6 percent from baseline. It is interesting to note in this context that while these two groups of EU countries bear a relatively close resemblance to the “core” and the “periphery,” respectively, that are distinguished in the literature on asymmetric shocks, there are some important differences. The response of activity to monetary shocks in Finland and the United Kingdom corresponds more closely to that of the “EU core,” whereas the real effects of monetary policy in France appear to correspond more closely to that of the “EU periphery.” It is, of course, important to note that these results are derived on the basis of past relationships between monetary shocks and activity in the EU countries. EMU will constitute a regime shift that could well lead to shifts in behavioral relationships