Andrés, Javier, David López-Salido, and Javier Vallés (2006), Money in an Estimated Business Cycle Model of the Euro Area, Economic Journal, 116, 457–77.
Andrés, Javier, David Lopez-Salido, and Edward Nelson (2007), Money and the Natural Rate of Interest: Structural Estimates for the UK, the US and the Euro Area, Research Division Federal Reserve Bank of St. Louis Working Paper Series, 2007–05A.
Ashley, R., Clive W.J. Granger Granger, and Richar Schmalensee (1980), Advertising and Aggregate Consumption: An Analysis of Causality Econometrica 48: 1149–67.
Assenmacher-Wesche, Katrin and Stefan Gerlach (2006a), Interpreting Euro Area Inflation at High and Low Frequencies, BIS WorkingPaper, 195.
Assenmacher-Wesche, Katrin and Stefan Gerlach (2006b), Understanding the Link between Money Growth and Inflation in the Euro Area, Manuscript, February.
Assenmacher-Wesche, Katrin, Stefan Gerlach, and Toshitaka Sekine (2007), Monetary Factors and Inflation in Japan, Manuscript, January.
Berger, Helge, and Pär Österholm (2008a), Does Money Growth Granger-Cause Inflation in the Euro Area? Evidence from Out-of-Sample Forecasts Using Bayesian VARs, IMF Working Papers, WP/08/53.
Berger, Helge and Pär Österholm (2008b), Does Money Matter for U.S. Inflation? Evidence from Bayesian VARs, IMF Working Papers, WP/08/76.
Bernanke, Ben, Mark Gertler, and Simon Gilchrist (1996), The Financial Accelerator in a Quantitative Business Cycle Framework, Review of Economics and Statistics, 78(1), 1–15.
Christiano, Lawrence, Martin Eichenbaum, and Charles Evans (2005), Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy, Journal of Political Economy, 113(1), 1–45.
D’Agostino, Antonello, Domenico Giannone, and Paolo Surico (2006), (Un)Predictability and Macroeconomic Stability, ECB Working Paper, 605.
De Grauwe, Paul and Magdalena Polan (2005), Is Inflation Always and Everywhere a Monetary Phenomenon? Scandinavian Journal of Economics, 107(2), 239–59.
Forni, Mario, Marc Hallin, Marco Lippi, and Lucrezia Reichlin, (2003), Do Financial Variables Help Forecasting Inflation and Real Activity in the Euro Area?, Journal of Monetary Economics, 50, 1243–55.
Faust, Jon and Jonathan Wright (2007), Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset, NBER Working Paper, 13397.
Fischer, Björn, Michele Lenza, Huw Pill, and Lucrezia Reichlin (2008) Money and Monetary Policy: The ECB Experience 1999-2006, in: Andreas Beyer and Lucrezia Reichlin (eds.), The Role of Money—Money and Monetary Policy in the Twenty-First Century, Proceedings of the Forth ECB Central Banking Conference 9-10 November 2006, ECB: Frankfurt, 102-175.
- Search Google Scholar
- Export Citation
)| false ( Fischer, Björn, Michele Lenza, Huw Pill, and Lucrezia Reichlin 2008) Money and Monetary Policy: The ECB Experience 1999-2006, in: ( Andreas Beyerand Lucrezia Reichlin eds.), The Role of Money—Money and Monetary Policy in the Twenty-First Century, Proceedings of the Forth ECB Central Banking Conference 9-10 November 2006, ECB: Frankfurt, 102-175.
Fuhrer, Jeffrey (2000), Habit Formation in Consumption and Its Implications for Monetary-Policy Models, American Economic Review, 90(3), 367–90.
Galí, Jordi, Stefan Gerlach, Julio Rotemberg, Harald Uhlig, and Michael Woodford (2004), The Monetary Policy Strategy of the ECB Reconsidered, Monitoring the European Central Bank No. 5, CEPR: London.
Gerlach, Stefan and Lars Svensson (2003), Money and Inflation in the Euro Area: A Case for Monetary Indicators? Journal of Monetary Economics, 50(8), 1649–72.
Hallman, Jeffrey, Richard Porter, and David Small (1991), Is the Price Level Tied to the M2 Monetary Aggregate in the Long Run? The American Economic Review, 81(4), 841–58.
Nelson, Edward (2002), Direct Effects of Base Money on Aggregate Demand: Theory and Evidence, Journal of Monetary Economics, 49(4), 687–708.
Nelson, Edward (2003), The Future of Monetary Aggregates in Monetary Policy Analysis, Journal of Monetary Economics, 50(5), 1029–59.
Rudebusch, Glenn and Lars Svensson (2002), Eurosystem Monetary Targeting: Lessons From US. Data, European Economic Review, 46(3), 417–42.
Reynard, Samuel (2007), Maintaining Low Inflation, Money, Interest Rates, and Policy Stance, Journal of Monetary Economics, 54(5), 1441–71.
Scharnagl, Michael and Christian Schumacher (2007), Reconsidering the Role of Monetary Indicators for Euro Area Inflation from a Bayesian Perspective Using Group Inclusion Probabilities, Deutsche Bundesbank Discussion Paper, 09/2007.
Smets, Frank, and Raf Wouters, (2003), An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area, Journal of the European Economic Association 1 (5), September 2003, 1123–75.
Smets, Frank and Raf Wouters (2004), Forecasting with a Bayesian DSGE Model: an Application to the Euro Area, ECB Working Paper Series, No. 389.
Stavrev, Emil (2006), Measures of Underlying Inflation in the Euro Area: Assessment and Role for Informing Monetary Policy, IMF Working Papers, WP197.
Woodford, Michael (2003), Interest and Prices: Foundations of a Theory of Monetary Policy, Princeton University Press: Princeton.
Woodford, Michael (2006), How Important is Money in the Conduct of Monetary Policy? Queen’s Economics Department Working Paper No. 1104.
Woodford, Michael (2008), Does a “Two-Pillar Phillips Curve” Justify a Two-Pillar Monetary Policy Strategy? in: Andreas Beyer and Lucrezia Reichlin (eds.), The Role of Money—Money and Monetary Policy in the Twenty-First Century, Proceedings of the Forth ECB Central Banking Conference 9-10 November 2006, ECB: Frankfurt, 56-82.
- Search Google Scholar
- Export Citation
)| false ( Woodford, Michael 2008), Does a “Two-Pillar Phillips Curve” Justify a Two-Pillar Monetary Policy Strategy?in: ( Andreas Beyerand Lucrezia Reichlin eds.), The Role of Money—Money and Monetary Policy in the Twenty-First Century, Proceedings of the Forth ECB Central Banking Conference 9-10 November 2006, ECB: Frankfurt, 56-82.
Appendix I: Empirical Specifications
Appendix II: Bayesian Priors
We would like to thank Henning Weber for helpful comments and suggestions.
An alternative argument for incorporating money in the interest rate equation could be that money, for reasons not captured in the structural model, helps to forecast inflation or the central bank shares the households’ preference for a stable money stock. See Andres and others (2007).
The reason is that forward-looking price setters apply the households’ stochastic discount factor in their dynamic optimization problem, which, in turn, is influenced by money holdings.
Note that the direct and indirect impact of contemporaneous money on inflation move into opposite directions, with the overall impact being a question of the underlying parameters and, thus, ultimately an empirical matter.
Equation (1P*) combines a number of approaches. Hallman and others (1991), for instance, assume α= 1, which seems to be in line with Reynard’s (2007) empirical observation that the price gap influences inflation with considerable lags and some persistence. As a rule, the P* model does not restrict the expectations term, which, in principle, could take any form.
Note that the coefficients estimated for the DSGE models are semi-structural, as we do not enforce some of the restrictions implied by the deep parameters of the underlying model in the empirical implementation.
More specifically, the assumption of no inertia in aggregate demand is rejected by the data despite tight Bayesian priors.
Note that the within-model class comparison is not affected by this convention. To forecast the off-model conditional variables—in particular, the output and real money gaps and long-term money growth for the Phillips curve models, and money growth for the ARDL model—we proceed in two steps. For a given forecasting window, we first calculate these variables using a standard Hodrick-Prescott filter and then forecast them up to twelve quarters ahead using an ARMA process. This process is repeated each time the forecasting window is moved and a new observation is added.
The fact that our simulated out-of-sample exercise is based on revised instead of real time data should have no bearing on the results. A bias (if any) in forecasting accuracy that the use of revised data may create would influence all models simultaneously, leaving their relative performance unchanged. And indeed, in a recent paper, Faust and Wright (2007) show that the use of real time or revised data does not influence the relative performance of various model- and expert-based forecasts for inflation and output in the U.S. economy.
Table 1 further below, in addition, shows the relative rank of the models within each nested model class (see columns named “within model class”) based on the relative RMSE at the twelve quarter horizon and the average RMSE across all horizons, respectively.
Note that this is not simply an artifact of the NKM model lacking the ability to capture the persistence in the data, which may influence forecasting accuracy. In the empirical implementation, all estimated equations include lagged endogenous variables (see Section III and Appendix I).
Additional results available on request.
In part, this might be explained by the fact that our approach emphasizes within-class comparisons, and that we have made no effort to fine-tune the performance of a particular class of model vis-à-vis another. On the other hand, it is not immediately obvious how such an effort would alter the results.
In general, we follow the notation of the main text. For ease of exposition, we use Roman letters to abbreviate growth rates where required.