Abstract
Following the exchange-rate paper by Kim and Roubini (J Monet Econ 45(3):561–586, 2000), we revisit the questions on monetary policy, exchange rate delayed overshooting, the inflationary puzzle, and the weak monetary transmission mechanism; but we do so for the open Indian economy. We further incorporate a superior monetary measure, the aggregation-theoretic Divisia monetary aggregate. Our paper confirms the efficacy of the Kim and Roubini (J Monet Econ 45(3):561–586, 2000) contemporaneous restriction, customized for the Indian economy, especially when compared with recursive structure, which is damaged by the price puzzle and the exchange rate puzzle. The importance of incorporating correctly measured money into the exchange rate model is illustrated, when we compare models with no-money, simple-sum monetary measures, and Divisia monetary measures. Our results are confirmed in terms of impulse response, variance decomposition analysis, and out-of-sample forecasting. In addition, we do a flip-flop variance decomposition analysis, finding two important phenomena in the Indian economy: (i) the existence of a weak link between the nominal-policy variable and real-economic activity, and (ii) the use of inflation-targeting as a primary goal of the Indian monetary authority. These two main results are robust, holding across different time period, dissimilar monetary aggregates, and diverse exogenous model designs.
Notes
Nelson (2003) offers an alternative role for money. He argues that money demand depends on a long-term interest rate. Nelson’s resulting specification of the Federal Reserve’s interest rate rule is a dynamic generalization of the conventional monetary policy rules by Taylor (1999) Taylor rule, which excludes money. Money now has a direct effect that is independent of the short-term interest rate. Nelson concludes that the effect is consistent with U.S. data. Anderson and Kavajecz (1994) argued for the use of monetary aggregates as either indicators and/or targets of monetary policy. Several more recent studies, such as Nicoletti-Altimari (2001); Trecoci and Vega (2002); Jansen (2004), and Assenmacher-Wesche and Gerlach (2006), have found a useful leading indicator role for monetary and credit aggregates with respect to low-frequency trends in inflation.
Differencing of variables does not provide gain in asymptotic efficiency and may cause loss of information regarding the co-movements, such as cointegrating relationships between variables. Hence, we use a VAR in levels.
The Indian monetary aggregates are defined as follows: M2 = currency with the public + demand deposits with banks + other deposits with the Reserve Bank of India + savings deposits with banks + term deposits with contractual maturity of up to and including one year with banks + certificate of deposits issued by banks; M3 = M2 + term deposits with contractual maturity of over one year with banks + call borrowings from non-depository financial corporations by banks; and L1 = M3 + all deposits with the Post Office Savings Banks (excluding National Savings Certificates).
The results with other models are available upon request.
The result for other models are available upon request.
The result for other models are available upon request.
References
Anderson RG, Kavajecz KA (1994) A historical perspective on the federal reserve’s monetary aggregates: definition, construction, and targeting. Fed Reserve Bank St. Louis Rev 76:1–31
Assenmacher-Wesche K, Gerlach S (2006) Interpreting Euro area inflation at high and low frequencies. Bureau of International Settlement Working Paper, n. 195
Barnett WA (1980) Economic monetary aggregate: an application of index number and aggregation theory. J Econ 14(September):11–48
Barnett WA (2012) Getting it wrong: how faulty monetary statistics undermine the fed, the financial system, and the economy. MIT Press, Cambridge
Barnett WA, Chauvet M (2011) Financial aggregation and index number theory. World Scientific, Singapore
Barnett WA, Kwag CH (2006) Exchange Rate Determination from Monetary Fundamentals: an Aggregation Theoretic Approach. Front Financ Econ 3(1):29–48
Barnett WA, Serletis A (2000) The theory of monetary aggregation. Elsevier, Amsterdam
Barnett WA, Chauvet M, Leiva-Leon D (2016) Real-time nowcasting of nominal GDP under structural break, J Econ 191(2):312–324
Belongia M, Ireland P (2015) Interest rates and money in the measurement of monetary policy. J Bus Econ Stat, Taylor & Francis Journals 33(2):255–269
Bernanke B (1986) Alternative explanations of money income correlation. In: Brunner K, Meltzer AH (eds) Real business cycles, real exchange rates, and actual policies, Carnegie-Rochester series on public policy 25. North Holland, Amsterdam, pp. 49–99
Brischetto A, Voss G (1999) A structural vector Autoregression model of monetary policy in Australia. Research discussion paper 1999–11, Reserve Bank of Australia
Bruggeman A, Camba-Mendez G, Fischer B, Sousa J (2005) Structural Filters for Monetary Analysis: the Inflationary Movements of Money in the Euro Area, ECB Working Paper, n. 470
Christiano LJ, Motto R, Rostagno M (2007) Two reasons why money and credit may be useful in monetary policy, NBER Working Paper, n 13502
Chrystal K, MacDonald R (1995) Exchange rates, financial innovation and Divisia money: the Sterling/dollar rate. J Int Money Financ 14:493–513
Cochrane JH (2007) Inflation Determination with Taylor Rules: a Critical Review, NBER Working Paper, n. 13409
Courtenay S, Thornton D (1987) Solving the 1980s’ velocity puzzle: a progress report, Fed Reserve Bank St. Louis Rev (August/September 1987):175–204
Diewert W (1976) Exact and superlative index numbers. J Econ 4:115–145
Doan T (2012) RATS handbook for Bayesian econometrics. Estima, Evanston
Doan T (2013) RATS manual, version 8.3. Estima, Evanston
Drake L, Mills TC (2005) A new empirically weighted monetary aggregate for the United States. Econ Inq 43(1):138–157
Goodfriend M, Lacker MJ (1999) Limited commitment and central bank lending, Economic Quarterly - Federal Reserve Bank of Richmond Fall, 1–27
Hamilton JD (1994) Time series econometrics. Princeton U. Press, Princeton
Ireland P (2001a) Sticky-price models of the business cycle - specification and stability. J Monet Econ 47:3–18
Ireland P (2001b) Money’s role in the monetary business cycle, Working Paper 8115, National Bureau of Economic Research
Jansen ES (2004) Modelling inflation in euro area, ECB Working Paper, n. 322
Kim S, Roubini N (2000) Exchange rate anomalies in the industrial countries: a solution with a structural VAR approach. J Monet Econ 45(3):561–586
Leeper E, Roush J (2003) Putting ‘M’ back in monetary policy. J Money Credit Bank 35(6):1217–1256
Masuch KS, Nicoletti-Altimari S, Rostagno M (2003) The Role of money in monetary policy making, Bureau of International Settlement Working Paper, n 1
Nelson E (2003) The future of monetary aggregates in monetary policy analysis. J Monet Econ 50(5):1029–1059
Nicoletti-Altimari S (2001) Does money lead inflation in euro area? ECB Working Paper, n. 63
Ramachandran M, Das R, Bhoi B (2010) The divisia monetary indices as leading indicators of inflation, Reserve Bank of India Development Research Group Study No.36, Mumbai
Schunk D (2001) The relative forecasting performance of the Divisia and simple sum monetary aggregate. J Money Credit Bank 33:272–283
Serletis A, Gogas P (2014) Divisia monetary aggregates, the great ratios, and classical money demand functions. J Money Credit Bank 46:229–241
Serletis A, Istiak K (2016) Are the responses of the U.S. economy asymmetric to positive and negative money supply shocks? Open Econ Rev, Springer 27(2):303–316
Serletis A, Rahman S (2013) The case for Divisia money targeting. Macroecon Dyn, Cambridge University Press 17(8):1638–1658
Serletis A, Rahman S (2015) On the output effects of monetary variability. Open Econ Rev 26:225–236
Sims CA (1980) Macroeconomics and reality. Econometrica 48:1–48
Taylor J (1999) Monetary policy rules. University of Chicago Press, Chicago
Trecoci C, Vega JL (2002) The information content of M3 for future inflation in euro area. Weltwirtschaftliches Arch 138(1):22–53
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We would like to thank the anonymous reviewers for their constructive comments that helped us improve the manuscript. We also appreciate some of the excellent comments received during the Financial Market and Nonlinear Dynamics (FMND) workshop.
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Barnett, W.A., Bhadury, S.S. & Ghosh, T. An SVAR Approach to Evaluation of Monetary Policy in India: Solution to the Exchange Rate Puzzles in an Open Economy. Open Econ Rev 27, 871–893 (2016). https://doi.org/10.1007/s11079-016-9403-2
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DOI: https://doi.org/10.1007/s11079-016-9403-2
Keywords
- Monetary policy
- Monetary aggregates
- Divisia monetary aggregates
- Structural VAR
- Exchange rate overshooting
- Liquidity puzzle
- Price puzzle
- Exchange rate puzzle
- Forward discount bias puzzle