中国宏观经济统计数据异常性和波动性特征的计量检验:1953～2001

对于宏观经济统计数据的异常性和波动性进行分析，已成为研究数据质量的最核心内容之一。本文从经济系统的角度运用随机方差扩大模型对我国36个宏观经济时间序列的数据质量进行了全面分析，发现了数据异常及波动的特点和规律。研究结论表明，大部分异常点的出现或多或少都是以聚集成堆的形式出现，它们之间有深刻的内在联系，异常点的出现大多与各种历史因素以及外部冲击有关；几乎所有的原始序列都有显著的偏度，过多的峰度也是明显的，因此它们被显著地拒绝认为服从正态分布；名义序列的特征在更大程度上受到异常点的影响。     

宏观经济统计数据结构变化分析及其对中国的实证

对于宏观经济统计数据的结构变化进行分析已成为研究数据质量的核心内容之一。本文从经济系统的角度运用联合估计诊断模型对我国 3 6个宏观经济时间序列的结构变化进行了全面的分析 ,发现了数据异常的特点和规律。研究结论表明 :大部分异常点的出现或多或少都是以聚集成堆的形式出现的 ,它们之间存在深刻的内在联系 ,孤立的异常点不是我国宏观经济时间序列的主要特征 ;几乎所有的原始序列都有显著的偏度 ,过多的峰度也是明显的 ,因此它们被显著地拒绝认为服从正态分布 ;大部分变量的原始序列和异常点修正后序列虽然都呈现出非ARCH特征 ,但是ARCH2、ARCH4、ARCH8的P值却有一定程度的不同。     

Estimators for the common principal components model based on reweighting: influence functions and Monte Carlo study

The common principal components model for several groups of multivariate observations is a useful parsimonious model for the scatter structure which assumes equal principal axes but different variances along those axes for each group. Due to the lack of resistance of the classical maximum likelihood estimators for the parameters of this model, several robust estimators have been proposed in the literature: plug-in estimators and projection-pursuit (PP) type estimators. In this paper, we show that it is possible to improve the low efficiency of the projection-pursuit estimators by applying a reweighting step. More precisely, we consider plug-in estimators obtained by plugging a reweighted estimator of the scatter matrices into the maximum likelihood equations defining the principal axes. The weights considered penalize observations with large values of the influence measures defined by Boente et al. (2002). The new estimators are studied in terms of theoretical properties (influence functions and asymptotic variances) and are compared with other existing estimators in a simulation study.     

An outlier test for linear processes — II. Large contamination

In Flak/Schmid (1993) an outlier test for linear processes was introduced. The test statistic bases on a comparison of each observation with a one-step predictor. It was assumed that an upper bound for the total number of outlierss _n is known, wheren denotes the sample size. The asymptotic distribution of the test statistic was derived under the assumption thats _n/n → 0 ands _n → ∞ asn → ∞. This note deals with the asymptotic behaviour of this quantity, ifs _n/n →p ₀ ∈ (0, 1).     

A Hausman-type test to detect the presence of influential outliers in regression analysis

In regression analysis, classical estimations may be excessively influenced by a few atypical observations. We propose a Hausman-type test to balance robustness and efficiency and to check whether a robust method should be implemented. An economic application is presented.     

An outlier test for linear processes

In this paper we introduce an outlier test for linear processes. It is assumed that an upper bound for the number of outliers is known which is not too big in relation to the sample size. The test statistic bases on the comparison of the observations with certain predictors. We discuss the asymptotical behaviour of the test statistic under the null hypothesis ‘no outlier’ and derive the asymptotic distribution for the case that the distribution of the squared white noise process belongs to a certain subset of the domain of attraction of the Gumbel distribution. Especially the most important case in applications, the Gaussian white noise is included.     

Modeling the volatility of real GDP growth: The case of Japan revisited

Previous studies [e.g., Hamori, S., 2000. Volatility of real GDP: some evidence from the United States, the United Kingdom and Japan. Japan and the World Economy 12, 143–152; Ho, K.Y., Tsui, A.K.C., 2003. Asymmetric volatility of real GDP: some evidence from Canada, Japan, the United Kingdom and the United States. Japan and the World Economy 15, 437–445; Fountas, S., Karanasos, M., Mendoza, A., 2004. Output variability and economic growth: the Japanese case. Bulletin of Economic Research 56, 353–363] find high volatility persistence of economic growth rates using generalized autoregressive conditional heteroskedasticity (GARCH) specifications. This paper reexamines the Japanese case, using the same approach and showing that this finding of high volatility persistence reflects the Great Moderation, which features a sharp decline in the variance as well as two falls in the mean of the growth rates identified by Bai and Perron's [Bai, J., Perron, P., 1998. Estimating and testing linear models with multiple structural changes. Econometrica 66, 47–78; Bai, J., Perron, P., 2003. Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18, 1–22] multiple structural change test. Our empirical results provide new evidence. First, excess kurtosis drops substantially or disappears in the GARCH or exponential GARCH model that corrects for an additive outlier. Second, using the outlier-corrected data, the integrated GARCH effect or high volatility persistence remains in the specification once we introduce intercept-shift dummies into the mean equation. Third, the time-varying variance falls sharply, only when we incorporate the break in the variance equation. Fourth, the ARCH in mean model finds no effects of our more correct measure of output volatility on output growth or of output growth on its volatility.     

The minimum weighted covariance determinant estimator

In this paper, we introduce weighted estimators of the location and dispersion of a multivariate data set with weights based on the ranks of the Mahalanobis distances. We discuss some properties of the estimators like the breakdown point, influence function and asymptotic variance. The outlier detection capacities of different weight functions are compared. A simulation study is given to investigate the finite-sample behavior of the estimators. The research of Stefan Van Aelst was supported by a grant of the Fund for Scientific Research-Flanders (FWO-Vlaanderen) and by IAP research network grant nr. P6/03 of the Belgian government (Belgian Science Policy).     

Trend inflation estimates for Thailand from disaggregated data

This paper constructs a new trend inflation measure for Thailand based on the multivariate unobserved components model with stochastic volatility and outlier adjustments (MUCSVO) of Stock and Watson (2016). Similar to core inflation, the MUCSVO produces an estimate of trend inflation utilizing information in disaggregated data, but also allows for time-varying weights that depend on the volatility, persistence and comovement of the underlying sectoral inflation series. Based on the empirical results, the majority of sectoral weights show significant time-variation in contrast to their relatively stable expenditure shares. Volatile food and energy sectors that are typically excluded from core inflation measures also turn out to help explain approximately 10 percent of MUCSVO trend inflation rate movements. Compared against other benchmark trend inflation measures, we show that the MUCSVO delivers trend estimates that are smoother, more precise, and are able to forecast average inflation over the 1–3 year horizon more accurately both in-sample and out-of-sample, especially since the year 2000.     

Collapsibility hypotheses and diagnostic bounds in regression analysis

Deleted-case diagnostic statistics in regression analysis are based on changes in estimates due to deleting one or more cases. Bounds on these statistics, suggested in the literature for identifying influential cases, are widely used.  In a linear regression model for Y in terms of X and Z, the model is “collapsible” with respect to Z if the Y−X relation is unchanged by deleting Z from the model. Deleted-case diagnostic statistics can be viewed as test statistics for collapsibility hypotheses in the mean shift outlier model. It follows that, for any given case, all deleted-case statistics test the same hypothesis, hence all have the same p-value, while the bounds correspond to different levels of significance among the several statistics. Furthermore, the bound for any particular deleted-case statistic gives widely varying levels of significance over the cases in the data set. Received: April 1999