On modeling panels of time series*

This paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a retail store. A panel of time series concerns the case where the cross‐sectional dimension and the time dimension are large. Often, there is no a priori reason to select a few series or to aggregate the series over the cross‐sectional dimension. The use of, for example, a vector autoregression or other types of multivariate models then becomes cumbersome. Panel models and associated estimation techniques are more useful. Due to the large time dimension, one should however incorporate the time‐series features. And, the models should not have too many parameters to facilitate interpretation. This paper discusses representation, estimation and inference of relevant models and discusses recently proposed modeling approaches that explicitly aim to meet these requirements. The paper concludes with some reflections on the usefulness of large data sets. These concern sample selection issues and the notion that more detail also requires more complex models.     

统计基础工作中引入时间价值因素分析

在统计基础工作中,经常利用数列来表示经济现象在时间变化上的发展规律,由于该数列是将指标值按时间先后顺序简单排列,并没有考虑到各指标值本身所发生的时间价值,所以对于析始指标值的分析,不能精确反映和发现经济现象的实质及其发展变化规律。引入时间价值因素后,各指标值将会根据时间价值因素的变化而发生变化,分析时将会更为准确的发现经济现象发展变化规律。     

Differencing of random walks and near random walks

The traditional rationale for differencing time series data is to attain stationarity. For a nearly non-stationary first-order autoregressive process—AR (1) with positive slope parameter near unity—we were led to a complementary rationale. If one suspects near non-stationarity of the AR (1) process, if the sample size is ‘small’ or ‘moderate’, and if good one-step-ahead prediction performance is the goal, then it is wise to difference the data and treat the differences as observations on a stationary AR (1) process. Estimation by Ordinary Least Squares then appears to be at least as satisfactory as nonlinear least squares. Use of differencing for an already stationary process can be motivated by Bayesian concepts: differencing can be viewed as an easy way to incorporate non-diffuse prior judgement—that the process is nearly non-stationary—into one's analysis. Random walks and near random walks are often encountered in economics. Unless one's sample size is large, the same statistical analyses apply to either.     

对经济增长的时间序列分析

时间序列分析在经济运用中作用十分明显。利用1980～2003年国内生产总值的相关资料,运用时间序列分析,应用SAS软件对经济增长时间序列进行模型识别、拟合、估计和预测,预测结果较为满意。而改革开放以来,投资在经济增长中的作用越来越明显,在对经济增长序列进行时间序列分析的同时,也结合回归分析建立经济增长和投资的回归-时间序列组合模型来进行分析。     

Modern Strategies for Time Series Regression

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.     

FDI流量变化对中国宏观经济的影响分析

改革开放以来,我国利用FDI的总额不断增长,规模不断扩大,为经济发展作出了极大贡献。为了解FDI对我国经济的具体影响,文章利用我国1983—2008年统计资料中的时间序列数据,利用VAR模型分析FDI对我国经济发展的影响和贡献。     

An Updated Literature Review of Distance Correlation and Its Applications to Time Series

The concept of distance covariance/correlation was introduced recently to characterise dependence among vectors of random variables. We review some statistical aspects of distance covariance/correlation function, and we demonstrate its applicability to time series analysis. We will see that the auto‐distance covariance/correlation function is able to identify non‐linear relationships and can be employed for testing the i.i.d. hypothesis. Comparisons with other measures of dependence are included.     

FDI流量变化对中国宏观经济的影响分析

改革开放以来,我国利用FDI的总额不断增长,规模不断扩大,为经济发展作出了极大贡献。为了解FDI对我国经济的具体影响,文章利用我国1983—2008年统计资料中的时间序列数据,利用VAR模型分析FDI对我国经济发展的影响和贡献。     

Statistical Scale Space Methods

The goal of statistical scale space analysis is to extract scale‐dependent features from noisy data. The data could be for example an observed time series or digital image in which case features in either different temporal or spatial scales would be sought. Since the 1990s, a number of statistical approaches to scale space analysis have been developed, most of them using smoothing to capture scales in the data, but other interpretations of scale have also been proposed. We review the various statistical scale space methods proposed and mention some of their applications.     

经济分析与预测模型中信息矩阵的病态问题分析

对常用的经济分析与预测模型中的线性回归、时间序列及灰色系统信息矩阵的病态问题进行了讨论。通过对统计资料附加干扰,基于最小二乘原理,得出每个模型中的每一参数与噪声的数值关系。指出在经济分析与预测模型的使用过程中,使用这类模型进行分析时必须考虑矩阵的病态问题,采取有效方法减轻或者消除信息矩阵的病态程度后方可使用这三种模型。     

Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys

A key requirement of repeated surveys conducted by national statistical institutes is the comparability of estimates over time, resulting in uninterrupted time series describing the evolution of finite population parameters. This is often an argument to keep survey processes unchanged as long as possible. It is nevertheless inevitable that a survey process will need to be redesigned from time to time, for example, to improve or update methods or implement more cost-effective data collection procedures. It is important to quantify the systematic effects or discontinuities of a new survey process on the estimates of a repeated survey to avoid a disturbance in the comparability of estimates over time. This paper reviews different statistical methods that can be used to measure discontinuities and manage the risk due to a survey process redesign.     

Correlated daily time series and forecasting in the M4 competition

We participated in the M4 competition for time series forecasting and here describe our methods for forecasting daily time series. We used an ensemble of five statistical forecasting methods and a method that we refer to as the correlator. Our retrospective analysis using the ground truth values published by the M4 organisers after the competition demonstrates that the correlator was responsible for most of our gains over the naïve constant forecasting method. We identify data leakage as one reason for its success, due partly to test data selected from different time intervals, and partly to quality issues with the original time series. We suggest that future forecasting competitions should provide actual dates for the time series so that some of these leakages could be avoided by participants.     

基于ARIMA模型的产业集群稳定性研究

通过统计分析产业集群月产出量数列平稳性,运用时间序列分析方法,建立产业集群ARIMA模型,预测在某一时间点的产业集群产出情况,为生态园产业链稳定性建设提供理论依据。通过实际数据研究,运用EViews软件,验证产业集群ARIMA模型可行性。为解决产业集群稳定性预测中"外部扰动"因素对稳定性的影响,提供了理论途径。为产业集群建设稳定持续的发展提供了科学有效的理论方法。     

Local influence analysis for Poisson autoregression with an application to stock transaction data

In statistical diagnostics and sensitivity analysis, the local influence method plays an important role and has certain advantages over other methods in several situations. In this paper, we use this method to study time series of count data when employing a Poisson autoregressive model. We consider case‐weights, scale, data, and additive perturbation schemes to obtain their corresponding vectors and matrices of derivatives for the measures of slope and normal curvatures. Based on the curvature diagnostics, we take a stepwise local influence approach to deal with data with possible masking effects. Finally, our established results are illustrated to be effective by analyzing a stock transactions data set.     

Bayesian inference in time series models using kernel quasi likelihoods

The paper takes up Bayesian inference in time series models when essentially nothing is known about the distribution of the dependent variable given past realizations or other covariates. It proposes the use of kernel quasi likelihoods upon which formal inference can be based. Gibbs sampling with data augmentation is used to perform the computations related to numerical Bayesian analysis of the model. The method is illustrated with artificial and real data sets.     

基于时间序列视角的2020年中国能源需求预测

能源与经济社会发展构成巢结构关系.能源预测是能源规划的基础.时间序列分析技术,基于其时原始数据相对简单的要求,实现对数据产生机制的科学理解和描述,实现未来值预报,是常用预测方法.本文利用时间序列分析技术,预测得到中国2020年能源需求总量为449074.91万吨标准煤.     

小波神经网络对沪深A300的分析和预测

针对金融时间序列非平稳性、非线性的特点,本文采用小波分析与人工神经网络相结合的方法,对沪深A300收盘价进行分析和预测。结果表明,小波神经网络有较强的预测能力,能达到预期效果。为了验证该方法的预测能力,进一步将时间序列数据多步分段,全方位地进行预测,并与小波-ARIMA模型、BP神经网络预测方法进行比较,体现了小波神经网络的预测优势。     

Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support

How effective are different approaches for the provision of forecasting support? Forecasts may be either unaided or made with the help of statistical forecasts. In practice, the latter are often crude forecasts that do not take sporadic perturbations into account. Most research considers forecasts based on series that have been cleansed of perturbation effects. This paper considers an experiment in which people made forecasts from time series that were disturbed by promotions. In all conditions, under-forecasting occurred during promotional periods and over-forecasting during normal ones. The relative sizes of these effects depended on the proportions of periods in the data series that contained promotions. The statistical forecasts improved the forecasting accuracy, not because they reduced these biases, but because they decreased the random error (scatter). The performance improvement did not depend on whether the forecasts were based on cleansed series. Thus, the effort invested in producing cleansed time series from which to forecast may not be warranted: companies may benefit from giving their forecasters even crude statistical forecasts. In a second experiment, forecasters received optimal statistical forecasts that took the effects of promotions into account fully. This increased the accuracy because the biases were almost eliminated and the random error was reduced by 20%. Thus, the additional effort required to produce forecasts that take promotional effects into account is worthwhile.