DEALING WITH UNCERTAINTY: ROBUST RULES IN MONETARY POLICY

We argue that in seeking to insure against model uncertainty, monetary policy makers are often ready to trade ex post performance for greater certainty in the outcome. They thus look for rules that although not optimal ex post, have certain properties that qualify them as robust. We apply first, Gul's approach of ‘disappointment’ aversion to describe policy makers' aversion to uncertainty and then define the properties the notion of ‘robustness’ entails. With these two tools we then link the desirability of such robust rules to the degree of policy makers' aversion to uncertainty. We thus show that provided such robust rules exist, a larger degree of disappointment aversion leads to a greater emphasis on robustness in policy implementation.     

Multiple aspect testing for case-control designs

When testing for the equality of two distributions in a case-control design with treatment effects presumed to act possibly on more than one aspect, different tests may be properly considered for testing for different features of a null hypothesis, leading to the multiple aspect testing issue. Two different aspects are therefore of interest: the location-aspect, based on the comparison of location indexes, and the distributional-aspect, based on the comparison of the empirical distribution functions. A simulation study shows that the combined testing procedure exhibits a good robust overall performance, and an application in biomedical research is also presented.     

Monotonicity of Rank Statistics in some Non–parametric: Testing Problems

For some non–parametric testing problems (one–sided two–sample problem, k –sample trend problem, testing independence against positive dependence) a partial ordering, denoted by ≥, over the alternatives is defined. This partial ordering expresses the strength of the deviation from the null–hypothesis. All familiar rank tests turn out to become more powerful under "increasing" alternatives; that is, all familiar rank statistics preserve the ordering stochastically in samples whenever it is present between underlying distributions. As a tool, the sample equivalence of ≥ is introduced as a partial ordering over pairs of permutations. Functions, defined on pairs of permutations, which preserve this ordering are studied.     

Testing equality of variances in the analysis of repeated measurements

The problem of comparing the precisions of two instruments using repeated measurements can be cast as an extension of the Pitman-Morgan problem of testing equality of variances of a bivariate normal distribution. Hawkins (1981) decomposes the hypothesis of equal variances in this model into two subhypotheses for which simple tests exist. For the overall hypothesis he proposes to combine the tests of the subhypotheses using Fisher's method and empirically compares the component tests and their combination with the likelihood ratio test. In this paper an attempt is made to resolve some discrepancies and puzzling conclusions in Hawkins's study and to propose simple modifications.
The new tests are compared to the tests discussed by Hawkins and to each other both in terms of the finite sample power (estimated by Monte Carlo simulation) and theoretically in terms of asymptotic relative efficiencies.     

中国城市化与经济增长的动态计量分析

为探讨我国城市化与经济增长之间的相互作用和相互影响,文章依据1978~2004年的时序数据,利用协整检验、格兰杰因果检验、误差修正模型、脉冲响应及方差分解等方法,对城市化水平与经济增长的关系进行动态计量分析。结果发现,经济增长是城市化水平提高的格兰杰原因,经济增长对城市化产生较大的正向冲击效应,而城市化对经济增长的作用强度不大;城市化水平受人均GDP影响的效应逐步增强,受自身影响的效应不断减弱,而人均GDP受自身波动影响的效应不断上升,受城市化水平影响的强度逐步下降。对我国城市化与经济增长关系的深入认识,有利于各级政府在推动城市化和促进经济增长的过程中采取合理对策,避免走入误区。     

Trading frequency and event study test specification

We examine the effects of thin trading on the specification of event study tests. Simulations of upper and lower tail tests are reported with and without variance increases on the event date across levels of trading volume. The traditional standardized test is misspecified for thinly traded samples. If return variance is unlikely to increase, then Corrado's rank test provides the best specification and power. With variance increases, the rank test is misspecified. The Boehmer et al. standardized cross-sectional test (Event-study methodology under conditions of event-induced variance, Journal of Financial Economics 30, pp. 253–272) is properly specified, but not powerful, for upper-tailed tests. Lower-tailed alternative hypotheses can best be evaluated using the generalized sign test.     

Diversification Benefits of Japanese Real Estate Over the Last Four Decades

This paper studies the benefits of diversifying into real estate and other assets that typify the wealth held by Japanese investors. We examine movements in mean variance frontiers by employing spanning tests to assess the statistical significance of frontier shifts. We also investigate the impact of shifts in mean variance frontiers before and after the precipitous decline in Japanese real estate and stock market values that began in 1990. Spanning tests show that real estate, short and long-term bonds, and Japanese equity provide significant diversification benefits. We find that mean variance frontiers shift after 1990. Statistically significant shifts are also economically important as measured by Sharpe ratio changes. Although significant, the portfolio weights on Japanese real estate are relatively small compared to their composition found in surveys of Japanese household wealth.     

Using Market Expectations to Test for Speculative Bubbles in the Crude Oil Market

The wide fluctuations of oil prices from 2003 to 2008 have attracted the interest of academics and policymakers. A popular view is that these fluctuations were caused by speculative bubbles due to the increased financialization of oil futures markets. This hypothesis, however, is difficult to examine since the fundamental price of oil is unobservable and, therefore, econometric evidence in favor of bubbles may actually be due to misspecified market fundamentals. In this paper, we extend two recently proposed methodologies for bubble detection that alleviate this problem by using market expectations of future prices. Both methodologies provide no evidence of speculative bubbles.     

Measuring the liquidity part of volume

Based on the concept that the presence of liquidity frictions can increase the daily traded volume, we develop an extended version of the mixture of distribution hypothesis model (MDH) along the lines of Tauchen and Pitts (1983) to measure the liquidity portion of volume. Our approach relies on a structural definition of liquidity frictions arising from the theoretical framework of Grossman and Miller (1988), which explains how liquidity shocks affect the way in which information is incorporated into daily trading characteristics. In addition, we propose an econometric setup exploiting the volatility–volume relationship to filter the liquidity portion of volume and infer the presence of liquidity frictions using daily data. Finally, based on FTSE 100 stocks, we show that the extended MDH model proposed here outperforms that of Andersen (1996) and that the liquidity frictions are priced in the cross-section of stock returns.     

Forecast combinations under structural break uncertainty

This paper proposes two new weighting schemes that average forecasts based on different estimation windows in order to account for possible structural change. The first scheme weights the forecasts according to the values of reversed ordered CUSUM (ROC) test statistics, while the second weighting method simply assigns heavier weights to forecasts that use more recent information. Simulation results show that, when structural breaks are present, forecasts based on the first weighting scheme outperform those based on a procedure that simply uses ROC tests to choose and forecast from a single post-break estimation window. Combination forecasts based on our second weighting scheme outperform equally weighted combination forecasts. An empirical application based on a NAIRU Phillips curve model for the G7 countries illustrates these findings, and also shows that combination forecasts can outperform the random walk forecasting model.