INFERENCE USING QUALITATIVE AND QUANTITATIVE INFORMATION WITH AN APPLICATION TO MONETARY POLICY

I. INTRODUCTION There has been a revival of interest in the use of qualitative information in macroeconomic analysis recently. An impetus for this revival is the apparent inability of modern statistical analyses that use only quantitative information to resolve significant macroeconomic controversies. For example, the direction of causality between money and output continues to be the subject of considerable debate. The use of qualitative information in macroeconomic analysis has a long tradition. Mitchell [1927] and Burns and Mitchell [1946] use the descriptions in Thorp's [1926] annals and several statistical series to identify the unobserved phase of the business cycle.(1) In their chapters on the Federal Reserve, Friedman and Schwartz [1963] use diaries, policy records, and monetary aggregates to identify the stance of monetary policy. The essence of this traditional method is that it is prudent to use both qualitative and quantitative information in the process of inference because of the potential gain in confidence associated with cross verification. Both types of information are needed to identify the asymmetries and nonlinearities that are characteristic of business cycles, policy initiatives, and other economic phenomena. Questions concerning the interpretation of results using qualitative information, however, have been raised.(2) A source of unease is the difficulty of assessing a researcher's distillation of the information in descriptive materials. No obvious metric for evaluating the coding of a qualitative variable exists. I present an alternative framework for combining qualitative and quantitative information in order to draw inferences of an unobservable variable. Quantitative information is an observable time-series variable. Qualitative information is an indicator variable (coded by a researcher) that reflects the distillation of literary materials. Motivated by the traditional method, I consider the case where both types of observable information are imperfect representations of an unobserved state variable. An iterative algorithm that uses quantitative and qualitative information to draw inferences of the unobservable variable is constructed. The algorithm produces an inference of the unobservable state variable for each sample period using the two types of information. It also produces a probabilistic assessment of whether the qualitative indicator is a reliable indicator of the true unobserved state. As discussed in more detail below, this algorithm is closely related to those of Cosslett and Lee [1985] and Hamilton [1989]. The algorithm is applied to the problem of determining the stance of monetary policy. A goal is to confront a problem that economic agents may actually face because of the secrecy surrounding the monetary policy process. A description of that problem is: How can observable information - qualitative and quantitative - be combined to infer the stance of monetary policy? Also, can the reliability of the qualitative information (or a distillation of it) be measured? I implement the algorithm using two prominent indicators of monetary policy. The paper is organized as follows. The alternative framework for using qualitative and quantitative information to draw inferences of an unobservable variable is presented in the next section. The application to the problem of inferring the stance of monetary policy is presented in section III. In this section, indicators of policy are introduced, the model is estimated, and the relationship between the dynamic conditional monetary policy inferences and an important forecaster of macroeconomic activity is explored. Section IV concludes. II. AN ALTERNATIVE FRAMEWORK FOR INFERENCE Stochastic Structure My alternative framework is based on a particular conception of the statistical model underlying the traditional method. Let [y.sub.t] denote a quantitative variable and [I.sub.t] denote a (0,1) qualitative variable. …