Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-05-20T06:28:21.324Z Has data issue: false hasContentIssue false


Published online by Cambridge University Press:  29 November 2018

Makram El-Shagi*
Henan University and Halle Institute for Economic Research
Address correspondence to: Makram El-Shagi, Center for Financial Development and Stability, School of Economics, Henan University, MingLun Campus, 475000 Kaifeng, Henan, China; e-mail:


It has repeatedly been shown that properly constructed monetary aggregates based on index number theory (such as Divisia money) vastly outperform traditional measures of money (i.e. simple sum money) in empirical models. However, opponents of Divisia frequently claim that Divisia is “too complex” for little gain. And indeed, at first glance it looks as if simple sum and Divisia sum exhibit similar dynamics. In this paper, we want to build deeper understanding of how and when Divisia and simple sum differ empirically using monthly US data from 1990M1 to 2007M12. In particular, we look at how they respond differently to monetary policy shocks, which seems to be the most essential aspect of those differences from the perspective of the policy maker. We use a very rich, fairly agnostic setup that allows us to identify many potential nonlinearities, building on a smoothed local projections approach with automatic selection of the relevant interaction terms. We find, that—while the direction of change is often similar—the precise dynamics differ sharply. In particular in times of economic uncertainty, when the proper assessment of monetary policy is most relevant, those existing differences are drastically augmented.

© Cambridge University Press 2018

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


The author is indebted to Harald Uhlig, Fabio Canova, Jane Binner, James Swofford, and the participants of the 3rd HenU/INFER Workshop on Applied Macroeconomics.


Barnett, W. A. (1980). Economic monetary aggregates: An application of index number and aggregation theory. Journal of Econometrics 14(1), 1148.CrossRefGoogle Scholar
Barnett, W. A. (n.d). Getting It Wrong: How Faulty Monetary Statistics Undermine the Fed, the Financial System, and the Economy. Cambridge, MA: The MIT Press.CrossRefGoogle Scholar
Barnett, W. A. (2007). Multilateral aggregation-theoretic monetary aggregation over heterogeneous countries. Journal of Econometrics 136(2), 457482.CrossRefGoogle Scholar
Barnett, W. A. (1978). The user cost of money. Economics Letters 1(2), 145149.CrossRefGoogle Scholar
Barnichon, R. a. C. B. (n.d.). Impulse Response Estimation by Smooth Local Projections. Technical report, Mimeo.Google Scholar
Binner, J. M., Bissoondeeal, R. K., Elger, T., Gazely, A. M. and Mullineux, A. W. (2005). A comparison of linear forecasting models and neural networks: An application to Euro inflation and Euro Divisia. Applied Economics 37(6), 665680.CrossRefGoogle Scholar
Breitung, J. and Roling, C. (2015). Forecasting inflation rates using daily data: A nonparametric MIDAS approach. Journal of Forecasting 34(7), 588603.CrossRefGoogle Scholar
Brenner, M. and Galai, D. (1989). New financial instruments for hedge changes in volatility. Financial Analysts Journal 45(4), 6165.CrossRefGoogle Scholar
El-Shagi, M. (n.d.). Much Ado about Nothing: Sovereign Ratings and Government Bond Yields in the OECD. Technical report. IWH discussion papers.Google Scholar
El-Shagi, M. and Giesen, S. (2013). Money and inflation: Consequences of the recent monetary policy. Journal of Policy Modeling 35(4), 520537.CrossRefGoogle Scholar
El-Shagi, M., Giesen, S. and Kelly, L. J. (2015). The quantity theory revisited: A new structural approach. Macroeconomic Dynamics 19(1), 5878.CrossRefGoogle Scholar
El-Shagi, M. and Kelly, L. J. (2016). For they know not what they do: An analysis of monetary policy during the great moderation. Applied Economics Letters 1–5.Google Scholar
El-Shagi, M. and Kelly, L. J. (2014). Liquidity in the liquidity crisis: Evidence from Divisia monetary aggregates in Germany and the European crisis countries. Economics Bulletin 34(1), 6372.Google Scholar
Halperin, B. (n.d.). Updated Romer and Romer (2004) Measure of Monetary Policy Shocks. Technical report.Google Scholar
Hjertstrand, P., Swofford, J. L. and Whitney, G. A. (2016). Mixed integer programming revealed preference tests of utility maximization and weak separability of consumption, leisure, and money. Journal of Money, Credit and Banking 48(7), 15471561.CrossRefGoogle Scholar
Jordà, Ò. (2005). Estimation and inference of impulse responses by local projections. American Economic Review 95(1), 161182.CrossRefGoogle Scholar
Jordà, Ò. (2009). Simultaneous confidence regions for impulse responses. The Review of Economics and Statistics 91(3), 629647.CrossRefGoogle Scholar
Keating, J. W., Kelly, L. J., Smith, A. L. and Valcarcel, V. J. (n.d.). A model of monetary policy shocks for financial crises and normal conditions. Technical report, University of Kansas, Department of Economics.Google Scholar
Keating, J. W., Kelly, L. J. and Valcarcel, V. J. (2014). Solving the price puzzle with an alternative indicator of monetary policy. Economics Letters 124(2), 188194.CrossRefGoogle Scholar
Mallows, C. L. (1973). Some comments on Cp. Technometrics 15(4), 661675.Google Scholar
Romer, C. D. and Romer, D. H. (2004). A new measure of monetary shocks: Derivation and implications. The American Economic Review 94(4), 10551084.CrossRefGoogle Scholar
Romer, C. D. and Romer, D. H. (1989). Does monetary policy matter? A new test in the spirit of Friedman and Schwartz. NBER Macroeconomics Annual 4, 121170.CrossRefGoogle Scholar
Tepper, J., Chauvet, M., Kelly, L. and Binner, J. M. (n.d.). Forecasting macroeconomic time series: A comparison of regime switching and recurrent neural network methods.Google Scholar
Tibshirani, R. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society. Series B 58(1), 267288.Google Scholar
Whaley, R. E. (2000). The investor fear gauge. The Journal of Portfolio Management 26(3), 1217.CrossRefGoogle Scholar