Adaptive Order Determination for Constructing Time Series Forecasting Models

In time series modeling consistent criteria like Bayesian Information Criterion (BIC) outperform in terms of predictability loss-efficient criteria like Akaike Information Criterion (AIC) when data are generated by a finite-order autoregressive process, and the reverse is true when data are generated by an infinite-order autoregressive process. Since in practice we don’t know the data-generating process, it is useful to have an adaptive criterion that behaves as either a consistent or just as a loss-efficient criterion, whichever performs better. Here we derive such a criterion. Moreover, our criterion is adaptive to effective sample sizes and not sensitive to maximum a priori determined order limits.     

Estimating moving average parameters in the presence of measurement error

Suppose that a moving average time series X_t is not observed, but instead Y_t = X_t + ?_t is observed, where ?_t, is measurement error. Estimation of the parameters of X_t has previously been considered under the assumption that X_t and ?_t are uncorrelated. The case where X_t and ?_t have known cross covariances is considered here, and a method is described for estimating the parameters of X_t. A simulation compares four estimators for a MA(1) series parameter in the presence of measurement error.     

A Statistical Method for the Determination of the Appropriate Order in a General Class of Time Series Models

Abstract

We propose a method to determine the order q of a model in a general class of time series models. For the subset of linear moving average models (MA(q)), our method is compared with that of the sample autocorrelations. Since the sample autocorrelation is meant to detect a linear structure of dependence between random variables, it turns out to be more suitable for the linear case. However, our method presents a competitive option in that case, and for nonlinear models (NLMA(q)) it is shown to work better. The main advantages of our approach are that it does not make assumptions on the existence of moments and on the distribution of the noise involved in the moving average models. We also include an example with real data corresponding to the daily returns of the exchange rate process of mexican pesos and american dollars.     

Moving Average-Based Estimators of Integrated Variance

We examine moving average (MA) filters for estimating the integrated variance (IV) of a financial asset price in a framework where high-frequency price data are contaminated with market microstructure noise. We show that the sum of squared MA residuals must be scaled to enable a suitable estimator of IV. The scaled estimator is shown to be consistent, first-order efficient, and asymptotically Gaussian distributed about the integrated variance under restrictive assumptions. Under more plausible assumptions, such as time-varying volatility, the MA model is misspecified. This motivates an extensive simulation study of the merits of the MA-based estimator under misspecification. Specifically, we consider nonconstant volatility combined with rounding errors and various forms of dependence between the noise and efficient returns. We benchmark the scaled MA-based estimator to subsample and realized kernel estimators and find that the MA-based estimator performs well despite the misspecification.     

Linear models analysis of small samples of categorized ordinal response data

A study of the small sample properties of linear models analyses of contingency tables with ordered response categories is presented. Analytical and empirical study of the linear models approach of Williams and Grizzle suggests that modification would improve its performance in small sample situations. Subsequent simulation studies of the modified procedure show that for small samples it has acceptable performance with respect to Type I errors and is robust with respect to response distribution form and inequality among the sample sizes in the contingency table.     

Estimating the parameters of a BINMA Poisson model for a non-stationary bivariate time series

This article proposes a novel non-stationary BINMA time series model by extending two INMA processes where their innovation series follow the bivariate Poisson under time-varying moment assumptions. This article also demonstrates, through simulation studies, the use and superiority of the generalized quasi-likelihood (GQL) approach to estimate the regression effects, which is computationally less complicated as compared to conditional maximum likelihood estimation (CMLE) and the feasible generalized least squares (FGLS). The serial and bivariate dependence correlations are estimated by a robust method of moments.     

清末天主教和新教在华传教活动的异同

文章以法国和美国在中国的传教活动为例,选取几个不同角度考察了天主教和新教清末在华活动的异同,认为天主教和新教的历史渊源、社会背景所导致的不同属性是二者在华活动特点的最深刻导因。     

Order identification for gaussian moving averages using the covariation

In the traditional Box-Jenkins procedure for fitting ARMA time series models to data, the first step is order identification. The sample autocorrelation function can be used to identify pure moving average behavior. In this paper we consider using the autocovariation function identify the order of a univariate Gaussian time series. Simulation evidence indicates the suggested method may be a superior order identification tool when at least 100 observations are taken.     

基于协整—ARMA模型的河北省税收收入预测研究

经济税源是税收收入的基础。相关理论和实践都表明,税收与经济增长之间存在着一种长期均衡关系。为模拟这种均衡关系,建立了税收与经济的协整模型;为预测税基发展趋势,建立了经济产值的ARMA模型;对经济税基的预测进一步测算税收收入的季度规模,并用部分历史数据和外推数据对预测模型进行了验证。经证明,模型的预测精度较高,可用于对未来各季度税收收入的实际预测。