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There are two general approaches to analyzing central bank behaviour in formulating monetary policy. One is to carefully examine the central bank's legislative act and public statements such as reports, publications, policy statements, public speeches and interviews to analyze what central bankers say they are trying to accomplish. Known as `narrative measures of monetary policy', examples of this approach include the classic analysis by Friedman and Schwartz (1963) and the work by Romer and Romer (1989), Boschen and Mills (1991) and Judd and Rudebusch (1999). While this approach can uncover some useful information regarding central bank's behaviour, it falls short of formally modelling the policy formulation process. See Leeper (1993) for a critical review of this approach. In addition, Walsh (2003) states that the narrative approach captures both exogenous shifts in policy and the endogenous response of monetary policy to economic development. Hence, analytical study of central bank's behaviour to isolate these two factors is dicult.

The second approach, and the one that will be used in this thesis, is to apply statistical methods to detect the systematic relationships between the actual movement of the central bank's instrument and other macroeconomic variables. If policymakers behave purposefully, with well-dened preferences for achieving dierent goals, it may be pos- sible to uncover these preferences and goals from the empirical response of the interest

rates to other macroeconomic variables. With that, this section outlines two empirical approaches that have been used in the literature to analyze a central bank's behaviour in formulating monetary policy.

2.3.1 Estimating a Central Bank's Reaction Function

As we mentioned in Section 2.1, there are a large number of empirical studies which estimate the systematic components of monetary policy using HMT type interest rate rule. In these studies, a central bank's reaction function is estimated as a single equa- tion. The estimation method used to estimate the reaction function is dependent on the model specication of the interest rate rule. Ordinary least squares (OLS) is applied for the case when the HMT interest rate rule uses contemporaneous and backward looking set-up, while the Instrumental Variables (IV) and the Generalized Method of Moments (GMM) is favoured when the HMT rule uses the forward looking specication.

Dennis (2004) states that one of the reasons why using estimated interest rate rules to describe monetary policy behaviour is attractive and widely used in the literature is that they are able to capture the systematic relationship between interest rates and macroeconomic variables. He argues that the estimated rules from this exercise can be viewed as approximations to the decision rules used by the central banks. These estimates are then commonly used in the literature to analyze and evaluate central bank behaviour in setting monetary policy. The outcome of this approach provides a few important contributions to understanding central bank behaviour in formulating monetary policy. It indicates the suitability of modelling the central bank reaction function by the HMT type interest rate rule. The ability of the interest rate rule to track the historical movement of central bank instruments provides information on its systematic behaviour in making monetary policy decisions. In addition, the result of this approach is also used to provide the answer to the question regarding policy evaluation. Analysis on the policy evaluation is done by looking at whether a central bank's past behaviour in setting the interest rate fullls the Taylor's principle.

The main feature of this approach is that the policy reaction function is estimated as a single equation. The reaction function is constructed on an ad hoc basis, without a need to specify or estimate the underlying central bank loss function or the structure of the economy. The essential point is that estimated policy rules are reduced-form equations,

which are uninformative of policy issues that involve structural parameters or that require a structural interpretation because they are formulated, estimated, analyzed, and interpreted in the absence of a fully specied economic model (Lucas (1976)). While it is relatively simple, the main limitation of this approach is that it is only able to provide a descriptive analysis of a central bank's systematic behaviour in setting past interest rates. To reveal what central banks aim to achieve through their policy actions, it is necessary to recognize central banks behave purposefully when setting policy to achieve a specic goal. Due to this fact, important information regarding central banks' behaviour in formulating monetary policy cannot be obtained from the reduced form approach. Hence, this approach could not provide answers to the questions related to what are the policy objectives that a central bank wants to pursue and its relative preferences between these policy objectives. Lastly, since the estimated policy rules are reduced-form equations, analysis on the possible evolution of policymakers' behaviour cannot be conducted.

2.3.2 Estimating Central Bank's Policy Preference

To address the limitations of the narrative approach, the empirical approach goes one step further. It looks at the central bank's optimal control problem as a whole and tries to estimate simultaneously parameters for the central bank's loss function, reaction function and model equations representing the economy. Identifying and estimating parameters of the model equations representing the economy is much more straightfor- ward and has been widely done in the empirical literature involving the New Keynesian model. In contrast, estimating parameters in a central bank's loss function from the real data is far more complicated, mainly because a central bank's loss function itself is not observable. What is observed from the central bank's action over time is the actual movement of its monetary instrument (interest rates) and the evolution of the state variables. In this regard, the estimation exercise needs to infer the parameters in the loss function by extracting the information from the observed policy action taken by the central bank as well as the evolution of economic variables over time. As such, this approach involves an estimation procedure that searches over the parameters of the model representing the economic structure, for values that reconcile a policy rule that ts the data and compares it with one that minimizes the expected loss function. There are several examples in the literature that apply this approach to the case of

central banks in developed economies. Using the closed economy set-up, Salemi (1995), Favero and Rovelli (2003), Ozlale (2003), and Dennis (2004, 2006), investigated the case of the US Federal Reserve, while Assenmacher-Wesche (2006) did a country comparison involving the US, Germany and Japan. Kam, Lees, and Liu (Forthcoming) took the approach one step further and used the open economy model to estimate the central banks' preferences in several developed, ination targeting countries.

There are three estimation methods that have been used to estimate the system of simultaneous equations for this approach. The rst two are the maximum likelihood method as used in Ozlale (2003) and Dennis (2004, 2006) and the GMM method as applied in Favero and Rovelli (2003). Lastly, Kam, Lees, and Liu (Forthcoming) employ the Bayesian method.