Evaluating Information Variables for Monetary Policy in a Noisy Economic Environment
AbstractAs recent research has shown one of the most important impediments to a successful conduct of monetary policy arises from the measurement error associated with output and inflation observations and the uncertainty regarding estimates of unobservables such as potential output. (See, for example, recent papers by Orhanides (1999, 2000), Orphanides and van Norden (2000), Smets (1999), Rudebusch (2000), Sack and Wieland (2000), Orphanides, Porter, Tetlow and Finan (2000), Aoki (1999, 2000), Svensson and Woodford (1999, 2000), Dotsey and Hornstein (2000).) Faced with imperfect and incomplete information about these key macroeconomic variables, central banks have often put significant emphasis on potential leading indicators such as money growth, interest rate spreads, wage settlements, raw materials prices, etc.. Thus, in our study we investigate the value of specific indicators as information variables for monetary policy in a noisy economic environment. Such an evaluation seems particularly relevant for European monetary policy, which has to focus on new euro area-wide data and account for the possibility of structural changes in the wake of monetary union. For this reason, we chose the small euro area macro model estimated by Coenen and Wieland (2000) as our basic framework for analysis. In terms of methodology we rely on two alternative approaches to evaluating the usefulness of different information variables in a noisy economic environment. The first approach involves implementing the Kalman filter to obtain optimal estimates of output and inflation using all the information available in the model and determine the weight given to specific indicator variables. These optimal estimates can then be used as inputs for a Taylor-type policy rule. The second approach is to start from a simple policy rule that responds to output and inflation as observed, and then consider including other information variables and optimising over the respective response coefficients. As far as the sources of uncertainty are concerned, we start by introducing measurement error regarding output and inflation observations. For U.S. data an estimate of the revision process following the initial publication is available from Orphanides (1999). For euro area data, we construct our own estimates of the revision processes based on the data vintages available for the last few years. In a second step, we add a further source of uncertainty by treating potential output as an unobservable variable that is estimated based on the available information. Our analysis focuses on three types of information variables which central banks commonly point to: first the monetary aggregates, then financial market variables such a interest rate spreads and finally more institutional factors such as news regarding wage settlements. As to the value of money growth information, at least in the long run inflation is a monetary phenomenon. In other words, once output returns to potential increases in money growth should translate to more inflation. Furthermore, even in the short run money may provide useful information regarding the size of contemporaneous demand and price shocks. In the context of our model, money enters via a simple static and alternatively a richer dynamic money demand equation. For this purpose, we make use of euro area estimates obtained by Coenen and Vega (2000). To the extent that money growth data is itself is subject to revisions we also incorporate measurement error regarding money in our analysis. Financial variables such as interest rate spreads contain information about expected future interest rates and expected inflation. This information will be particularly useful to the central bank when it has only noisy measures of variables such as output and inflation and when expectations of market participants embody private information. In our empirical model, which contains both short and long-term rates, it is the long-term real interest rate which affects aggregate demand and ultimately inflation. News regarding wage settlements may be especially helpful as indicators of inflationary pressures in some European countries such as Germany and Italy where a large share of wage contracts is negotiated by unions. Such factors enter our model through overlapping wage contracts a la Taylor (1980) and Fuhrer and Moore (1995) and constitute a key determinant of future output and inflation.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 131.
Date of creation: 01 Apr 2001
Date of revision:
Contact details of provider:
Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html
More information through EDIRC
European Monetary Union; monetary policy; indicators; imperfect information; Kalman filter;
Find related papers by JEL classification:
- E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
- E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- D.A. Kendrick & H.M. Amman, 2008. "Comparison of Policy Functions from the Optimal Learning and Adaptive Control Frameworks," Working Papers 08-19, Utrecht School of Economics.
- Hans M. Amman & David A. Kendrick, 2003.
"A Classification System for Economic Stochastic Control Models,"
Computing in Economics and Finance 2003
114, Society for Computational Economics.
- David Kendrick & Hans Amman, 2006. "A Classification System for Economic Stochastic Control Models," Computational Economics, Society for Computational Economics, vol. 27(4), pages 453-481, June.
- D.A. Kendrick & H.M. Amman & M.P. Tucci, 2008. "Learning About Learning in Dynamic Economic Models," Working Papers 08-20, Utrecht School of Economics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).
If references are entirely missing, you can add them using this form.