Project on Assessment of Key Issues Related to Monetary Policy
[Source: RBI Report on Currency & Finance 2003-04]

Module: 6 Monetary Transmission Mechanism

Annexure: Monetary Transmission: Methodological Issues

Notwithstanding recent progress in monetary research, monetary transmission remains a "black-box". The recent research on monetary transmission has, therefore, largely relied on vector autoregression (VAR) framework. A VAR framework treats all variables as endogenous in order to avoid infecting the model with spurious or false identifying restrictions and thus provides a convincing solution to the "black-box" problem. Although the VAR methodology has also been subjected to criticism, the method remains popular since it offers a straightforward solution to the simultaneity problem and appears to yield a reasonable characterisation of the economy's response to monetary policy (Kuttner and Mosser, 2002). A VAR framework focuses on the responses of the key macroeconomic variables to exogenous monetary policy shocks rather than to systematic component of monetary policy. This method also makes the lag structure and dominance of the channels transparent. In view of lack of a consensus on the workings of the transmission, the preference for VAR methodology in the recent literature comes from the minimum restrictions that it places on as to how the monetary shocks affect the economy. Accordingly, this Module (Box VII.6) uses a VAR framework to explore the monetary transmission dynamics in India. In view of significant structural changes in the economy, a study based on a long-period may be subject to the problem of structural breaks and the results may not fully reflect the current lags in the transmission. The empirical exercise in this section thus focuses on the post-liberalisation period (April 1994 to March 2004).

Two key issues in estimating a VAR model are the list of variables to be included and the identifying assumptions. In India, operating procedures have undergone significant shifts over the sample period. It is only recently that interest rates have emerged as signals of monetary policy stance. Moreover, despite a switch away from a monetary targeting framework, broad money continues to be an important information variable. Given that the period of empirical study covers the period 1994 onwards, when these changes to monetary policy framework have been gradually evolving, the VAR, therefore, includes both interest rates and broad money. As output stabilisation and price stability are the key objectives of monetary policy, these variables are included in the VAR. Finally, reflecting the opening up of the economy, exchange rate is also included in the VAR. Furthermore, ensuring orderliness in foreign exchange market is a key policy objective and, towards this objective, monetary policy has been occasionally tightened through increases in key policy rates over the sample period. This also provides a strong rationale for inclusion of exchange rate as an endogenous variable in the VAR. In all, the VAR model includes five endogenous variables.

As regards the identification assumptions, this Module estimated a recursive VAR with short-run restrictions on the contemporaneous effect of variables. A common identifying assumption in the VAR literature is that monetary policy can react to output and price developments contemporaneously (i.e., in the same period) but there is no contemporaneous output and prices reaction to monetary signals. Accordingly, output and prices are placed before monetary policy variables in the model. This ordering appears reasonable, especially given the use of monthly data. The variables are thus ordered as follows: index of industrial production (LIIP), wholesale price index (LWPI), Bank Rate (BRATE), broad money (LM3) and exchange rate (Rupees per US dollar) (LEXCH). Exchange rate, being a financial market variable, can react to monetary signals contemporaneously and, therefore, is placed last in the VAR. A sensitivity analysis undertaken on the ordering of variables indicated that the impulse responses are broadly unchanged whether exchange rate is placed after or before interest rate and money supply variables. In addition to the endogenous variables, oil prices (to control for supply shocks) are included as an exogenous variable. Real world exports and global non-fuel commodity prices are also included as exogenous variables to account for external demand and price shocks. Monthly dummies were included to control for seasonality. All the variables (except interest rate variables) are in logarithmic form. All the variables in the VAR are found to be non-stationary. As the option of differencing of series produces no gain in asymptotic efficiency and throws away information, the VAR is estimated in levels.

The results of the VAR model are typically analysed in terms of impulse response and variance decomposition. While impulse responses track the dynamics of key macro variables in response to an exogenous monetary policy shock, variance decomposition analysis attempts to quantify the role of monetary policy shocks in the volatility of key macroeconomic aggregates. These results are presented in Box VII.6.