It is generally believed that financial liberalisation erodes the potency of monetary policy by rendering tenuous the link between monetary aggregates and important macro-economic magnitudes (such as income, prices and exchange rates). This could be accounted for by several factors such as the blurring of the distinction between money and near-money, the breakdown of the money demand function and the easing of credit and liquidity constraints. This hypothesis has been tested in the context of developed economies, where it has derived a measure of support. However, the empirical evidence to test this proposition is somewhat scanty, in the context of less developed countries (LDCs) and emerging market economies (EMEs). This article uses a Structural Vector Auto Regressive (SVAR) model to address this problem in the Indian context over the period January 1980 to August 2004. Using monthly data separate SVAR models are estimated for the pre-liberalisation (1980:03 to 1991:12) and post-liberalisation phases (1992:01 to 2004:08), with a view to recording differential responses (if any) of industrial output, exchange rates and prices to changes in monetary impulses. The analysis brings out that some, though not substantial, differences are evident in this regard over the two periods. The article concludes by spelling out certain reorientations necessary in the conduct of monetary policy in a financially liberalised environment.
Akhtar, M.A. (1983), ‘ Financial Innovations and Their Implications for Monetary Policy: An International Perspective ’, BIS Economic Papers, 9.
2.
Andrews, D.W.K. (1993), ‘ Tests for Parameter Instability and Structural Change with Unknown Change Point ’, Econometrica, 61: 821–56 .
3.
Andrews, D.W. and J.C. Monahan (1992), ‘ An Improved Heteroscedasticity and Auto-correlation Consistent Covariance Matrix Estimator ’, Econometrica, 60: 953–66 .
4.
Angeloni, I. , A. Kashyap , B. Mojon and D. Terlizzese (2002), ‘Monetary Transmission in the Euro Area: Where Do We Stand?’, Working Paper No. 114, European Central Bank .
5.
Bagliano, F.C. and C.A. Favero (1997), ‘Measuring Monetary Policy with VAR Models: An Evaluation’, Discussion Paper No. 1743, Centre for Economic Policy Research, London .
6.
Barnett, W.A. (1980), ‘ Economic Monetary Aggregates: An Application of Aggregation and Index Number Theory ’, Journal of Econometrics, 14 (1): 11–48 .
7.
Barnett, W.A. and P.A. Spindt (1982), ‘Divisia Monetary Aggregates: Their Compilation, Data and Historical Behaviour’, Federal Reserve Board Publication Services, Staff Study No. 116, Washington DC .
8.
Bayoumi, T. and P. Koujianou (1989), ‘Financial Deregulation and Consumption’, mimeo, Washington DC: International Monetary Fund .
9.
Bernanke, B.S. and I. Mihov (1998), ‘ Measuring Monetary Policy ’, Quarterly Journal of Economics, 112: 869–902 .
10.
Bernanke, B.S. , J. Boivin and P. Eliasz (2005), ‘ Measuring Monetary Policy: A Factor Augmented Vector Autoregressive (FAVAR) Approach ’, The Quarterly Journal of Economics, 120(1): 387–422 .
11.
Blundell-Wignall, A. , F. Brown and P. Manasse (1990), ‘Monetary Policy in the Wake of Financial Liberalisation’, Working Paper No. 77, OECD, Department of Economics and Statistics .
12.
Brand, C. , D. Gerdesmeier and B. Roffia (2002), ‘Estimating the Trend of M3 Income Elasticity Underlying the Reference Value for Monetary Growth’, European Central Banking Occasional Paper No. 3, European Central Bank, Frankfurt .
13.
Brown, F. and P. Manasse (1989), ‘The Information Content of the Term Structure of Interest Rates: Theory and Practice’, Working Paper No. 69, OECD, Department of Economics and Statistics .
14.
Calza, A. and J. Sousa (2003), ‘Why has Broad Money Demand been More Stable in the Euro Area than in other Economies?: A Literature Review’, in Otmar Issuing (ed.), Background Studies for the ECB's Evaluation of its Monetary Policy Strategy, Chapter 9. Frankfurt: European Central Bank .
15.
Cotula, F. (1984), ‘ Financial Innovation and Monetary Control in Italy ’, Banca Nazionale del Lavoro Quarterly, 37: 219–56 .
16.
Doornik, J.A. and D.F. Hendry (1995), PcFiml 8.0: Interactive Econometric Modelling of Dynamic Systems. London: Chapman & Hall .
17.
Engle, R.F. and R. Susmel (1993), ‘ Common Volatility in International Equity Markets ’, Journal of Business and Economic Statistics, 11(April): 167–76 .
18.
Gabb, W. and A. Mullineux (1995), ‘Financial Innovation, Monetary Aggregates and Monetary Policy in the UK and Germany’, Discussion Paper No. 95-17, Department of Economics, University of Birmingham .
19.
Garretsen, H. and J. Swank (1998), ‘ The Transmission of Interest Rate Changes and the Role of Bank Balance Sheets: A VAR Analysis for the Netherlands ’, Journal of Macroeconomics, 20: 325–39 .
20.
Ghysels, E. , A. Guay and A. Hall (1995), ‘Predictive Tests for Structural Change with Unknown Breakpoint’, Cirano Scientific Series, No. 95-20.
21.
Ghysels, E. and A. Hall (1990), ‘ A Test for Structural Stability of Euler Conditions Parameters Estimated via the Generalized Method of Moments Estimator ’, International Economic Review, 31: 355–64 .
22.
Goodfriend, M. (1993), ‘ Interest Rate Policy and the Inflation Scare Problem 1979–1992 ’, FRB of Richmond Economic Quarterly, 1(Winter): 1–23 .
23.
Goodhart, C. (1986), ‘ Financial Innovation and Monetary Control ’, Oxford Review of Economic Policy, 2: 79–102 .
24.
Goodhart, C. (1989), ‘ The Conduct of Monetary Policy ’, Economic Journal, 99: 293–346 .
25.
Gordon, D. and E.M. Leeper (1994), ‘ The Dynamic Impacts of Monetary Policy: An Exercise in Tentative Identification ’, Journal of Political Economy, 102: 1228–47 .
26.
Grilli, V. and N. Roubini (1995), ‘Liquidity and Exchange Rates: Puzzling Evidence from the G7 Countries’, mimeo, Yale University .
27.
Hansen, L. (1982), ‘ Large Sample Properties of Generalized Method of Moments Estimators ’, Econometrica, 50(3): 1029–54 .
28.
Hansen, L. and K.J. Singleton (1982), ‘ Generalized Instrumental Variables Estimation of Non-linear Rational Expectations Models ’, Econometrica, 50: 1269–86 .
29.
Hoffman, D. and A. Pagan (1989), ‘ Post-sample Prediction Tests of Generalized Method of Moments Estimators ’, Oxford Bulletin of Economics and Statistics, 51: 333–43 .
30.
Johansen, S. and K. Juselius (1990), ‘ Maximum Likelihood Estimation and Inference on Cointegration with Applications to the Demand for Money ’, Oxford Bulletin of Economics and Statistics, 52: 169–210 .
31.
Jordan, J.L. (1984), ‘Financial Innovation and Monetary Policy’, in Federal Reserve Bank of St. Louis (ed.), Financial Innovations: Their Impact on Monetary Policy and Financial Markets, pp. 135–50. Boston: Kluwer-Nijhoff .
32.
Joshi, V. and I.M.D. Little (1996), India's Economic Reforms 1991–2001. New York: Oxford University Press .
33.
Kane, E.J. (1984), ‘Microeconomic and Macroeconomic Origins of Financial Innovation’, in Federal Reserve Bank of St. Louis (ed.), Financial Innovations: Their Impact on Monetary Policy and Financial Markets, pp. 3–20. Boston: Kluwer-Nijhoff .
34.
Kashyap, Anil K. and J.C. Stein (2000), ‘ What do One Million Observations Say about the Transmission of Monetary Policy ’, American Economic Review, 90: 407–28 .
35.
Kim, S. (1999), ‘ Do Monetary Policy Shocks Matter in the G-7 Countries? Using Common Identifying Assumptions about Monetary Policy Across Countries ’, Journal of International Economics, 48: 387–412 .
36.
Mohan, Rakesh (2005), ‘ Financial Sector Reforms in India: Policies and Performance Analysis ’, Economic and Political Weekly (Special Issue), XL(12): 4723–28 .
37.
Newey, W.K. and K.D. West (1987), ‘ Hypothesis Testing with Efficient Method of Moments Estimation ’, International Economic Review, 28: 777–87 .
38.
Normandin, M. and L. Phaneuf (1996), ‘The Liquidity Effect: Testing Identification Conditions Under Time-varying Conditional Volatility’, Working Paper No. 40, Center for Research on Economic Fluctuations and Employment, University of Quebec at Montreal .
Podolski, T.M. (1986), Financial Innovations and the Money Supply. Oxford: Basil Blackwell .
41.
Rangarajan, C. (2004), Select Essays on the Indian Economy ( R. Kannan [ed.]). New Delhi: Academic Foundation .
42.
Reserve Bank of India (RBI) (1998), ‘Working Group on Money Supply Analytics and Methodology of Compilation’, RBI, Mumbai .
43.
Romer, C.D. and D.H. Romer (1994), ‘ Monetary Policy Matters ’, Journal of Monetary Economics, 34: 75–88 .
44.
Sen, K. and R. Vaidya (1997), The Process of Financial Liberalisation in India. New Delhi: Oxford University Press .
45.
Shenton, L.R. and K.O. Bowman (1977), ‘ A Bivariate Model for the Distribution of √b1 and b2 ’, Journal of the American Statistical Association, 72: 206–11 .
46.
Sims, C.A. (1992), ‘ Interpreting the Macroeconomic Time Series Facts: The Effects of Monetary Policy ’, European Economic Review, 36(5): 975–1000 .
47.
Sims, C.A. and T. Zha (1995), ‘Error Bands for Impulse Responses’, Working Paper No. 95–6, Federal Reserve Bank of Atlanta .
48.
Sterne, G. and T. Bayoumi (1993), ‘Temporary Cycles or Volatile Trends? Economic Fluctuations in 21 OECD Countries’, Working Paper No. 13, Bank of England .
49.
Strongin, S. (1995), ‘ The Identification of Monetary Policy Disturbances: Explaining the Liquidity Puzzle ’, Journal of Monetary Economics, 35: 463–98 .
50.
Tobin, J. (1983), ‘ Financial Structure and Monetary Rules ’, Kredit und Kapital, 2: 155–71 .
51.
Vasudevan, A. (2003), Central Banking For Emerging Market Economies. New Delhi:Academic Foundation .
52.
Wright, J.H. (1997), ‘ The Limiting Distribution of Post-sample Stability Tests for GMM Estimation when the Potential Break Data is Unknown ’, Oxford Bulletin of Economics and Statistics, 59(2): 299–303 .
53.
Wrobel, E. and M. Pawlowska (2002), ‘Monetary Transmission in Poland: Some Evidence on Interest Rate and Credit Channels’, Paper No. 24, National Bank of Poland .
