The Surrogate Henderson Filters in X-11

This paper explains the surrogate Henderson filters that are used in the X-11 variant of the Census Method II seasonal adjustment program to obtain trends at the ends of time series. It describes a prediction interpretation for these surrogate filters, justifies an approximation to the filters, proposed by Kenny & Durbin (1982), and proposes a further interpretation of the results. The starting point for the paper is unpublished work by Musgrave (1964a, 1964b). His work has continuing relevance to current seasonal adjustment practice. This paper makes that work generally available for the first time, and reviews and extends it.     

Single and multiple error state-space models for signal extraction

We compare the results obtained by applying the same signal-extraction procedures to two observationally equivalent state-space forms. The first model has different errors affecting the states and the observations, while the second has a single perturbation term which coincides with the one-step-ahead forecast error. The signals extracted from both forms are very similar but their variances are drastically different, because the states for the single-source error representation collapse to exact values while those coming from the multiple-error model remain uncertain. The implications of this result are discussed both with theoretical arguments and practical examples. We find that single error representations have advantages to compute the likelihood or to adjust for seasonality, while multiple error models are better suited to extract a trend indicator. Building on this analysis, it is natural to adopt a ‘best of both worlds’ approach, which applies each representation to the task in which it has comparative advantage.     

Decomposition of a state-space model with inputs

This article shows how to compute the in-sample effect of exogenous inputs on the endogenous variables in any linear model written in a state–space form. Estimating this component may be either interesting by itself, or a previous step before decomposing a time series into trend, cycle, seasonal and error components. The practical application and usefulness of this method is illustrated by estimating the effect of advertising on the monthly sales of Lydia Pinkham's vegetable compound.     

The Moments Do Not Determine a Distribution

Table 1 corrects the critical values for testing normality reported by Lilliefors (1967). The corrected table allows us to derive a simple analytic approximation to the upper tail probabilities of his test statistic for probabilities less than .10. With few exceptions, the approximation is more accurate than Lilliefors's original table.     

Realisations of finite-sample frequency-selective filters

A filtered data sequence can be obtained by multiplying the Fourier ordinates of the data by the ordinates of the frequency response of the filter and by applying the inverse Fourier transform to carry the product back to the time domain. Using this technique, it is possible, within the constraints of a finite sample, to design an ideal frequency-selective filter that will preserve all elements within a specified range of frequencies and that will remove all elements outside it.     

Issues Involved With the Seasonal Adjustment of Economic Time Series

In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modem time series analysis raise the question of whether seasonal adjustment should be performed at all. This in turn leads to a discussion in the second part of issues involved in seasonal adjustment. We state our opinions about the issues raised and review some of the work of other authors. First, we comment on reasons that have been given for doing seasonal adjustment and suggest a new possible justification. We then emphasize the need to define precisely the seasonal and nonseasonal components and offer our definitions. Finally, we discuss criteria for evaluating seasonal adjustments. We contend that proposed criteria based on empirical comparisons of estimated components are of little value and suggest that seasonal adjustment methods should be evaluated based on whether they are consistent with the information in the observed data. This idea is illustrated with an example.     

Issues Involved With the Seasonal Adjustment of Economic Time Series

In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modern time series analysis raise the question of whether seasonal adjustment should be performed at all. This in turn leads to a discussion in the second part of issues invloved in seasonal adjustment. We state our opinions about the issues raised and renew some of the work of our authors. First, we comment on reasons that have been given for doing seasonal adjustment and suggest a new possible justification. We then emphasize the need to define precisely the seasonal and nonseasonal components and offer our definitions. Finally, we discuss our criteria for evaluating seasonal adjustments. We contend that proposed criteria based on empirical comparisons of estimated components are of little value and suggest that seasonal adjustment methods should be evaluated based on whether they are consistent with the information in the observed data. This idea is illustrated with an example.     

Crossing probabilities for a square root boundary by a bessel process

Probabilities for Wiener and Bessel processes to cross a square root boundary are calculated and the Mellin transform of the distribution of the associated stopping time is given.The transform is inverted and a table of critical values for the crossing probability as a function of the observation time is included. Also an analytic expression for the crossing probability is computed for the case of constant drift.     

季节异方差序列的季节调整问题研究

季节调整是从经济序列中剔除季节成分的重要方法。季节异方差的存在,使经典的季节调整方法无法彻底分离出季节成分,致使季节调整失败。本文针对季节异方差问题提出用于季节调整的改进的HS模型,并定义改进的HS模型构造季节异方差检验LR统计量,通过蒙特卡洛模拟方法分析该检验的检验尺度和检验功效。最后,利用我国税收总额月度序列给出实证分析,并通过对比考察了改进的HS模型方法季节调整的有效性。     

A Modified Kolmogorov–Smirnov Test for Normality

In this article we propose an improvement of the Kolmogorov-Smirnov test for normality. In the current implementation of the Kolmogorov-Smirnov test, given data are compared with a normal distribution that uses the sample mean and the sample variance. We propose to select the mean and variance of the normal distribution that provide the closest fit to the data. This is like shifting and stretching the reference normal distribution so that it fits the data in the best possible way. A study of the power of the proposed test indicates that the test is able to discriminate between the normal distribution and distributions such as uniform, bimodal, beta, exponential, and log-normal that are different in shape but has a relatively lower power against the student's, t-distribution that is similar in shape to the normal distribution. We also compare the performance (both in power and sensitivity to outlying observations) of the proposed test with existing normality tests such as Anderson–Darling and Shapiro–Francia.     

Tables for the Lilliefors and Modified Cramer–von Mises Tests of Normality

We present new tabulations of the Lilliefors distribution and the modified Cramer–von Mises distribution, which are used to test for normality when the population mean and variance are unknown. Some practical remarks and an example are given.     

A note on spurious regression in seasonal time series

This paper considers spurious regression between two different types of seasonal time series: one with a deterministic seasonal component and the other with a stochastic seasonal component. When one type of seasonal time series is regressed on the other type and they are independent of each other, the phenomenon of spurious regression occurs. Asymptotic properties of the regression coefficient estimator and the associated regression ‘t-ratio’ are studied. A Monte Carlo simulation study is conducted to confirm the phenomenon of spurious regression and spurious rejection of seasonal cointegration for finite samples.     

Implementation of Kolmogorov–Smirnov P-value computation in Visual Basic®: implication for Microsoft Excel® library function

This paper investigates methodologies for evaluating the probabilistic value (P-value) of the Kolmogorov–Smirnov (K–S) goodness-of-fit test using algorithmic program development implemented in Microsoft® Visual Basic® (VB). Six methods were examined for the one-sided one-sample and two methods for the two-sided one-sample cumulative sampling distributions in the investigative software implementation that was based on machine-precision arithmetic. For sample sizes n≤2000 considered, results from the Smirnov iterative method found optimal accuracy for K–S P-values≥0.02, while those from the SmirnovD were more accurate for lower P-values for the one-sided one-sample distribution statistics. Also, the Durbin matrix method sustained better P-value results than the Durbin recursion method for the two-sided one-sample tests up to n≤700 sample sizes. Based on these results, an algorithm for Microsoft Excel® function was proposed from which a model function was developed and its implementation was used to test the performance of engineering students in a general engineering course across seven departments.