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Article

Keywords:
autoregressive process; estimating parameters; multidimensional process; nonlinear process; nonnegative process
Summary:
Let $\mathbb{e}_t=(e_{t1},\dots ,e_{tp})^{\prime }$ be a $p$-dimensional nonnegative strict white noise with finite second moments. Let $h_{ij}(x)$ be nondecreasing functions from $[0,\infty )$ onto $[0,\infty )$ such that $h_{ij}(x)\le x$ for $i,j=1,\dots ,p$. Let $\mathbb{U}=(u_{ij})$ be a $p\times p$ matrix with nonnegative elements having all its roots inside the unit circle. Define a process $\mathbb{X}_t=(X_{t1},\dots ,X_{tp})^{\prime }$ by \[ X_{tj}=u_{j1}h_{1j}(X_{t-1,1})+\dots +u_{jp}h_{pj}(X_{t-1,p})+ e_{tj} \] for $j=1,\dots ,p$. A method for estimating $\mathbb{U}$ from a realization $\mathbb{X}_1,\dots ,\mathbb{X}_n$ is proposed. It is proved that the estimators are strongly consistent.
References:
[1] H.-Z. An: Non-negative autoregressive models. J. Time Ser. Anal. 13 (1992), 283–295. Zbl 0767.62070
[2] H.-Z. An, F. Huang: Estimation for regressive and autoregressive models with non-negative residual errors. J. Time Ser. Anal. 14 (1993), 179–191. MR 1212017
[3] J. Anděl: On AR(1) processes with exponential white noise. Commun. Statist. – Theory Methods 17 (1988), 1481–1495. MR 0945799 | Zbl 0639.62082
[4] J. Anděl: Nonlinear nonnegative AR(1) processes. Commun. Statist. – Theory Methods 18 (1989), 4029–4037. MR 1058926 | Zbl 0696.62347
[5] J. Anděl: Non-negative autoregressive processes. J. Time Ser. Anal. 10 (1989), 1–11.
[6] J. Anděl: Nonlinear positive AR(2) processes. Statistics 21 (1990), 591–600. DOI 10.1080/02331889008802269 | MR 1087287 | Zbl 0714.62087
[7] J. Anděl, V. Dupač: An extension of the Borel lemma. Comment. Math. Univ. Carolinae 30 (1989), 403–404. MR 1014141
[8] B. Auestad, D. Tjøstheim: Identification of nonlinear time series: First order characterization and order determination. Biometrika 77 (1990), 669–687. DOI 10.1093/biomet/77.4.669 | MR 1086681
[9] C. B. Bell, E. P. Smith: Inference for non-negative autoregressive schemes. Commun. Statist. – Theory Methods 15 (1986), 2267–2293. DOI 10.1080/03610928608829248 | MR 0853011
[10] R. Davis, W. McCormick: Estimation for first-order autoregressive process with positive or bounded innovations. Stochastic Processes Appl. 31 (1989), 237–250. DOI 10.1016/0304-4149(89)90090-2 | MR 0998115
[11] P. D. Feigin, S. I. Resnick: Estimation for autoregressive processes with positive innovations. Commun. Statist. – Stochastic Models 8 (1992), 479–498. MR 1182425
[12] D. A. Jones: Stationarity of non-linear autoregressive processes. Tech. Rep., Institute of Hydrology, Wallingford, Oxon, U.K., 1977.
[13] H. Tong: Non-linear Time Series. Clarendon Press, Oxford, 1990. Zbl 0716.62085
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