Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering

Volume 2014 (2014), Article ID 231592, 5 pages

http://dx.doi.org/10.1155/2014/231592
Research Article

Integer-Valued Moving Average Models with Structural Changes

1Statistics School, Southwestern University of Finance and Economics, Chengdu 611130, China

2School of Economics, Southwestern University of Finance and Economics, Chengdu 611130, China

Received 2 March 2014; Revised 22 June 2014; Accepted 7 July 2014; Published 21 July 2014

Academic Editor: Wuquan Li

Copyright © 2014 Kaizhi Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

It is frequent to encounter integer-valued time series which are small in value and show a trend having relatively large fluctuation. To handle such a matter, we present a new first order integer-valued moving average model process with structural changes. The models provide a flexible framework for modelling a wide range of dependence structures. Some statistical properties of the process are discussed and moment estimation is also given. Simulations are provided to give additional insight into the finite sample behaviour of the estimators.

1. Introduction

Integer-valued time series occur in many situations, often as counts of events in consecutive points of time, for example, the number of births at a hospital in successive months, the number of road accidents in a city in successive months, and big numbers even for frequently traded stocks. Integer-valued time series represent an important class of discrete-valued time series models. Because of the broad field of potential applications, a number of time series models for counts have been proposed in literature. McKenzie [1] introduced the first order integer-valued autoregressive, INAR , model. The statistical properties of the INAR are discussed in McKenzie [2], Al-Osh and Alzaid [3]. The model is further generalized to a th-order autoregression, INAR( ), by Alzaid and Al-Osh [4] and Du and Li [5]. The th-order integer-valued moving average model, INMA( ), was introduced by Al-Osh and Alzaid [6] and in a slightly different form by McKenzie [7]. Ferland et al. [8] proposed an integer-valued GARCH model to study overdispersed counts, and Fokianos and Fried [9], Weiß [10], and Zhu and Wang [1113] made further studies. Györfi et al. [14] proposed a nonstationary inhomogeneous INAR process, where the autoregressive type coefficient slowly converges to one. Bakouch and Ristić [15] introduced a new stationary integer-valued autoregressive process of the first order with zero truncated Poisson marginal distribution. Kachour and Yao [16] introduced a class of autoregressive models for integer-valued time series using the rounding operator. Kim and Park [17] proposed an extension of integer-valued autoregressive INAR models by using a signed version of the thinning operator. Zheng et al. [18] proposed a first order random coefficient integer-valued autoregressive model and got its ergodicity, moments, and autocovariance functions of the process. Gomes and Canto e Castro [19] presented a random coefficient autoregressive process for count data based on a generalized thinning operator. Existence and weak stationarity conditions for these models were established. A simple bivariate integer-valued time series model with positively correlated geometric marginals based on the negative binomial thinning mechanism was presented by Ristić et al. [20], and some properties of the model are also considered. Pedeli and Karlis [21] considered a bivariate INAR (BINAR ) process where cross correlation is introduced through the use of copulas for the specification of the joint distribution of the innovations.

Structural changes in economic data frequently correspond to instabilities in the real world. However, most work in this area has been concentrated on models without structural changes. It seems that the integer-valued autoregressive moving average (INARMA) model with break point has not attracted too much attention. For instance, a new method for modelling the dynamics of rain sampled by a tipping bucket rain gauge was proposed by Thyregod et al. [22]. The models take the autocorrelation and discrete nature of the data into account. First order, second order, and threshold models are presented together with methods to estimate the parameters of each model. Monteiro et al. [23] introduced a class of self-exciting threshold integer-valued autoregressive models driven by independent Poisson-distributed random variables. Basic probabilistic and statistical properties of this class of models were discussed. Moreover, parameter estimation was also addressed. Hudecová [24] suggested a procedure for testing a change in the autoregressive models for binary time series. The test statistic is a maximum of normalized sums of estimated residuals from the model, and thus it is sensitive to any change which leads to a change in the unconditional success probability. Structural change is a statement about parameters, which only have meaning in the context of a model. In our discussion, we will focus on structural change in the simple count data model, the first order integer-valued moving average model, whose coefficient varies with the value of innovation. One of the leading reasons is that piecewise linear functions can offer a relatively simple approximation to the complex nonlinear dynamics.

The rest of this paper is divided into four sections. In Section 2, we give the definition and basic properties of the new INMA model with structural changes. Section 3 discusses the estimation of the unknown parameters. We test the accuracy of the estimation via simulations in Section 4. Section 5 includes some concluding remarks.

2. Definition and Basic Properties

Definition 1. Let be a process with state space ; let , , and , , be positive integers. The process is said to be first order integer-valued moving average model with structural change (INMASC ) if satisfies the following equation: where is a sequence of independent and identically distributed Poisson random variables with mean and , .

The aim of this section is to provide expressions for the moments and stationary of INMASC model. For this purpose, we introduce the following notations:

Theorem 2. The numerical characteristics of are as follows:

Proof. (i) It is easy to get the mean and variance of by using the law of iterated expectations:

(ii) Moreover,

(iii) Note the correlation between and ; we have

Theorem 3. Let be the process defined by the equation in (1); then the is a covariance stationary process.

Proof. Both the unconditional mean and the unconditional variance of the are finite constant. And the autocovariance function does not change with time. Thus a stationary process.

Theorem 4. Suppose is INMASC process. Then(i) ;(ii) , .

Proof. (i) From definition and Theorem 2, we have that and are independent whenever . According to Theorem 9.1 of DasGupta [25], the process is a stationary 1-dependent sequence. Therefore we can complete the proof.

(ii) For , it follows that For , For , where , , and . Then note that implies for , .

Theorem 5. Let be a INMASC process according to Definition 1. Let be the sample mean of ; then the stochastic process is ergodic in the mean.

Proof. Since , .

From Theorem in Brockwell and Davis [26], we get Then converges in probability to . Therefore, the process is ergodic in the mean.

Theorem 6. Suppose is a INMASC process; then where .

The proof of Theorem 6 is similar to Theorem 4 given in Yu et al. [27]. It is easy to verify; we skip the details.

3. Estimation of Parameters

In this paper, we consider one method, namely, moment estimation. An advantage of the method is that it is simple and often produces good results. The estimation problem of INMASC parameters is complex. In fact, for the INMASC processes, the conditional distribution of the given is the convolution of the distribution of the arrival process and one thinning operation . On the other hand, there are too many unknown parameters of the model, such as , , , , and , , whereas the number of moment conditions is small.

Therefore we cannot estimate all the parameters unless additional assumptions are made. Then, we assume that the number of break point is two and assume that the value of break point , , and the mean of innovation are also known. Thus, here we estimate INMASC model with two break points. Under these assumptions, all the parameters , , , and , , are known. We only need to estimate the autoregressive coefficients , , and . Using the sample mean and sample covariance function, we can get the moment estimators via solving the following equations: If you want to estimate all parameters, you can use GMM method based on probability generating functions introduced by BräKnnäK and Hall [28]. But they found covariance matrix of estimators depends on and the orders besides the model parameters in a highly complex way. Thus we do not use this method here. In next section, simulations are provided to give insight into the finite sample behaviour of these estimators.

4. Simulation Study

Consider the following INMASC model: where is a sequence of i.i.d. For fixed , follows a Poisson distribution with mean .

The parameters values considered in this model are listed as follows:(model  A) , with , , ;(model  B) , with , , ;(model  C) , with , , .

We use the above models to generate data and then use moment methods to estimate the parameters. We computed the empirical bias and the mean square error (MSE) based on 300 replications for each parameter combination. These values are reported within parenthesis in Table 1.

tab1
Table 1: Bias and mean square error for models A, B, and C.

From the results in Table 1, we can see moment estimation is good estimation methods producing estimators whose bias and MSEs are small when the sample sizes are larger. In addition, this method is fast and easy to implement. It is perhaps not surprising that the MSEs are larger when these sample sizes are smaller. As to be expected, both the bias and the MSEs converge to zero with increasing sample size .

5. Conclusion

Based on some limitations of the present count data models, a new INMA model is introduced to model structural changes. Expressions for mean, variance, and autocorrelation functions are given. Stationary and other basic statistical properties are also obtained. We derived moment estimators of the unknown parameters. Furthermore, we constructed several simulations to evaluate the performance of the estimators of model parameters.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper

Acknowledgments

This work is supported by the National Natural Science Foundation of China (no. 71201126), the Science Foundation of Ministry of Education of China (no. 12XJC910001), and the Fundamental Research Funds for the Central Universities (nos. JBK140211 and JBK120405).

References

  1. E. McKenzie, “Some simple models for discrete variate time series,” Water Resources Bulletin, vol. 21, no. 4, pp. 635–644, 1985. View at Publisher · View at Google Scholar · View at Scopus
  2. E. McKenzie, “Autoregressive moving-average processes with negative-binomial and geometric marginal distributions,” Advances in Applied Probability, vol. 18, no. 3, pp. 679–705, 1986. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  3. M. A. Al-Osh and A. A. Alzaid, “First order integer-valued autoregressive INAR(1) process,” Journal of Time Series Analysis, vol. 8, no. 3, pp. 261–275, 1987. View at Publisher · View at Google Scholar · View at MathSciNet
  4. A. A. Alzaid and M. Al-Osh, “An integer-valued pth-order autoregressive structure INAR(p) process,” Journal of Applied Probability, vol. 27, no. 2, pp. 314–324, 1990. View at Publisher · View at Google Scholar · View at MathSciNet
  5. J. G. Du and Y. Li, “The integer-valued autoregressive (INAR(p)) model,” Journal of Time Series Analysis, vol. 12, no. 2, pp. 129–142, 1991. View at Publisher · View at Google Scholar · View at MathSciNet
  6. M. Al-Osh and A. A. Alzaid, “Integer-valued moving average (INMA) process,” Statistical Papers, vol. 29, no. 4, pp. 281–300, 1988. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. E. McKenzie, “Some ARMA models for dependent sequences of Poisson counts,” Advances in Applied Probability, vol. 20, no. 4, pp. 822–835, 1988. View at Publisher · View at Google Scholar · View at MathSciNet
  8. R. Ferland, A. Latour, and D. Oraichi, “Integer-valued GARCH process,” Journal of Time Series Analysis, vol. 27, no. 6, pp. 923–942, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. K. Fokianos and R. Fried, “Interventions in INGARCH processes,” Journal of Time Series Analysis, vol. 31, no. 3, pp. 210–225, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. C. H. Weiß, “The INARCH(1) model for overdispersed time series of counts,” Communications in Statistics: Simulation and Computation, vol. 39, no. 6, pp. 1269–1291, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  11. F. Zhu, “A negative binomial integer-valued GARCH model,” Journal of Time Series Analysis, vol. 32, no. 1, pp. 54–67, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. F. Zhu, “Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued GARCH models,” Journal of Mathematical Analysis and Applications, vol. 389, no. 1, pp. 58–71, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. F. Zhu and D. Wang, “Diagnostic checking integer-valued ARCH(p) models using conditional residual autocorrelations,” Computational Statistics and Data Analysis, vol. 54, no. 2, pp. 496–508, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  14. L. Györfi, M. Ispány, G. Pap, and K. Varga, “Poisson limit of an inhomogeneous nearly critical INAR(1) model,” Acta Universitatis Szegediensis, vol. 73, no. 3-4, pp. 789–815, 2007. View at Google Scholar · View at MathSciNet
  15. H. S. Bakouch and M. M. Ristić, “Zero truncated Poisson integer-valued AR(1) model,” Metrika, vol. 72, no. 2, pp. 265–280, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. M. Kachour and J. F. Yao, “First-order rounded integer-valued autoregressive ((RINAR(1)) process,” Journal of Time Series Analysis, vol. 30, no. 4, pp. 417–448, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. H.-Y. Kim and Y. Park, “A non-stationary integer-valued autoregressive model,” Statistical Papers, vol. 49, no. 3, pp. 485–502, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. H. Zheng, I. V. Basawa, and S. Datta, “First-order random coefficient integer-valued autoregressive processes,” Journal of Statistical Planning and Inference, vol. 137, no. 1, pp. 212–229, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  19. D. Gomes and L. Canto e Castro, “Generalized integer-valued random coefficient for a first order structure autoregressive (RCINAR) process,” Journal of Statistical Planning and Inference, vol. 139, no. 12, pp. 4088–4097, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. M. M. Ristić, A. S. Nastić, K. Jayakumar, and H. S. Bakouch, “A bivariate INAR(1) time series model with geometric marginals,” Applied Mathematics Letters, vol. 25, no. 3, pp. 481–485, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. X. Pedeli and D. Karlis, “A bivariate INAR(1) process with application,” Statistical Modelling, vol. 11, no. 4, pp. 325–349, 2011. View at Google Scholar
  22. P. Thyregod, J. Carstensen, H. Madsen, and K. Arnbjerg-Nielsen, “Integer valued autoregressive models for tipping bucket rainfall measurements,” Environmetrics, vol. 10, no. 4, pp. 395–411, 1999. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Monteiro, M. G. Scotto, and I. Pereira, “Integer-valued self-exciting threshold autoregressive processes,” Communications in Statistics: Theory and Methods, vol. 41, no. 15, pp. 2717–2737, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. Š. Hudecová, “Structural changes in autoregressive models for binary time series,” Journal of Statistical Planning and Inference, vol. 143, no. 10, pp. 1744–1752, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. A. DasGupta, Asymptotic Theory of Statistics and Probability, Springer, New York, NY, USA, 2008. View at MathSciNet
  26. P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods, Springer, New York, NY, USA, 1991. View at Publisher · View at Google Scholar · View at MathSciNet
  27. K. Yu, D. Shi, and H. Zou, “The random coefficient discretevalued time series model,” Statistical Research, vol. 28, no. 4, pp. 106–112, 2011. View at Google Scholar
  28. K. BräKnnäK and A. Hall, “Estimation in integer-valued moving average models,” Applied Stochastic Models in Business and Industry, vol. 17, no. 3, pp. 277–291, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus