Yasutomo Murasawa

A New Coincident Index of Business Cycles Based on Monthly and Quarterly Series

Popular monthly coincident indices of business cycles, e.g., the composite index and the Stock-Watson coincident index, have two shortcomings. First, they ignore information contained in quarterly indicators such as real GDP. Second, they lack economic interpretation; hence the heights of peaks and the depths of troughs depend on the choice of an index. This paper extends the Stock-Watson coincident index by applying maximum likelihood factor analysis to a mixed-frequency series of quarterly real GDP and monthly coincident business cycle indicators. The resulting index is related to latent monthly real GDP.

A Coincident Index, Common Factors, and Monthly Real GDP

The Stock–Watson coincident index and its subsequent extensions assume a static linear one‐factor model for the component indicators. This restrictive assumption is unnecessary if one defines a coincident index as an estimate of monthly real gross domestic products (GDP). This paper estimates Gaussian vector autoregression (VAR) and factor models for latent monthly real GDP and other coincident indicators using the observable mixed‐frequency series. For maximum likelihood estimation of a VAR model, the expectation‐maximization (EM) algorithm helps in finding a good starting value for a quasi‐Newton method. The smoothed estimate of latent monthly real GDP is a natural extension of the Stock–Watson coincident index.

Output gap estimation and monetary policy in China.

Using the Bayesian multivariate Beveridge-Nelson decomposition method, this paper estimates Chinas output gap based on a multivariate dynamic model featuring distinct interactions among real output, inflation, money, and the exchange rate in China during the period 1980-2010. The authors compare the statistical nature and potential forecasting effects of the resulting multivariate gap measure on monetary policy with those of the output gap measures based on univariate models. The empirical results show that only the measure based on the multivariate system significantly predicts monetary policy, which indicates that the output gap estimated by the multivariate system contains more information than the traditional measures for macroeconomic policy adjustments do.

Measuring Inflation Expectations Using Interval‐Coded Data*

To quantify qualitative survey data, the Carlson-Parkin method assumes normality, a time-invariant symmetric indifference interval, and long-run unbiased expectations. Interval-coded data do not require these assumptions. Since April 2004, the Monthly Consumer Confidence Survey in Japan asks households their price expectations a year ahead in seven categories with partially known boundaries; thus one can identify up to six parameters including an indifference interval each month. This paper compares normal, skew normal, and skew t distributions, and finds that the skew t distribution fits the best throughout the period studied. The results help to understand the dynamics of heterogeneous expectations.

Distribution-free statistical inference for generalized Lorenz dominance based on grouped data

One income distribution is preferable to another under any increasing and Schur-concave (S-concave) social welfare function (SWF) if and only if the generalized Lorenz (GL) curve of the first distribution lies above that of the second. Thus, testing for GL dominance of one distribution over another is of interest. The paper focuses on inference based on grouped data and makes two contributions: (i) it gives a new formula for the asymptotic variance-covariance matrix of a vector of sample GL curve ordinates, interpreting it as a method-of-moments (MM) estimator, and (ii) it proposes a new test for multivariate inequality restrictions, of which GL dominance is a special case. For the Japanese household income data grouped into deciles, the test accepts the null hypothesis that income distribution in Japan improved from 1979 to 1994.