PARTIAL AUTOCORRELATION PROPERTIES FOR NON-STATIONARY AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. We are primarily interested in relating the partial autocorrelation behaviour of an autoregressive integrated moving-average process of order ( p, d, q ), { Z _i } say, with those of its D -differenced processes {(1 - B ) ^D Z _i } ( D = 1, …, d ). To this end, we evaluate the early partial correlations corresponding to serial correlations which initially follow a slow linear decline from unity. These partials, to a first approximation, take a small constant negative value from lag 2 onwards. We also demonstrate a relationship between the theoretical partials π _k and π _k (Δ) for a once-integrated process { Z _i } and its first-differenced process {(1 - B ) Z _i } respectively. These results carry over to cases where the non-stationary zeros are at -1, and differencing is replaced by the corresponding 'simplifying' transformation implicit in the operator 1 + B.     

ON THE CONSISTENCY OF LEAST SQUARES ESTIMATORS FOR A THRESHOLD AR(1) MODEL

Abstract. For the SETAR (2; 1,1) model

where {a_t(i)} are i.i.d. random variables with mean 0 and variance σ²(i), i = 1,2, and {a_t(l)} is independent of {a_t(2)}, we consider estimators of φ₁, φ ₂ and r which minimize weighted sums of the sum of squares functions for σ²(1) and σ²(2). These include as a special case the usual least squares estimators. It is shown that the usual least squares estimators of φ₁, φ₂ and r are consistent. If σ²(1) ≠σ²(2) conditions on the weights are found under which the estimators of r and φ₁ or φ₂ are not consistent.     

AUTOREGRESSIVE PROCESSES WITH NORMAL STATIONARY DISTRIBUTIONS

Abstract. For the strictly stationary AR( k ) process Z _t = Λ ( Z _{t -1}) + α _t , with Λ : R ^k → R , Z _{t -1}= [ Z _{t -1}, Z _{t -2},…, Z _t-k ] and { α _t } an independent identically distributed white noise process, we partially characterize the Λ for which the stationary distribution of Z _t is normal.     

Robustness of the autoregressive spectral estimate for linear processes with infinite variance

Consider a discrete-time linear process { x _t }, a one-sided moving average of independent identically distributed random variables {ε _t }, with the common distribution in the domain of attraction of a symmetric stable law of index δ∈ (0, 2) and the moving-average coefficients b ( j ) such that ε _t is invertible in terms of the present and possibly infinite past values of { x _t }. By treating { x _t } as if it is second-order stationary, a normalized spectral density function f (μ) is defined in terms of the b ( j ) and, having observed x ₁, ..., x _T , an autoregression of order k is fitted by the well-known Yule–Walker and least squares methods and the normalized autoregressive spectral estimators are constructed. On letting k ←∞ as T ←∞, but sufficiently slowly, these estimators are shown to be uniformly consistent for f (μ), the convergence rate being T ^−1/φ, φ > δ. The finite sample behaviour is investigated by a simulation study which also examines possible effects of considering 'non-invertible' models.     

ON LINEAR PROCESSES WITH GIVEN MOMENTS

Abstract. Let X _t = c ₀ Y _t + c ₁ Y _{t -1}+… be a linear process with known coefficients c _k , where Y _t is a strict white noise. Let m ₁, …, m _2r be given numbers. A method is presented to determine whether there exists a distribution of Y _t such that EX ^k _t = m _k for k = 1, …, 2 r . In the positive case, such a distribution of Y _t is described. Some explicit formulas for AR(1) and AR(2) models are derived. The results can be used for simulating a process with given moments of its stationary distribution. The procedure also enables proof that some stationary distributions cannot belong to the given linear process.     

NON-NEGATIVE AUTOREGRESSIVE MODELS

Abstract. Consider a stationary non-negative autoregressive (AR) model given x _t = b ₁ x _t -1, +…+ b _p x _t-p + e _t , where the e _t are independent identically distributed non-negative variables and b ₁, …, b _p are non-negative parameters, and all the roots of the equation 1 – b ₁ u –…– b _p u ^p = 0 are outside the unit circle. The stationary solution of the above AR model is called a stationary non-negative AR process. Let x ₁, x ₂, … x _n be an example of a stationary non-negative AR process. Under very general conditions strongly consistent estimators of the AR parameters b ₁, b ₂, …, b _p have been studied. In this paper a new procedure is proposed to estimate not only b ₁, b ₂, …, b _p but also b _o which is the essential lower bound of the variable e _t . We shall show that the new estimators obtained using the new procedure are consistent estimators of b _o, b ₁, …, b _p under the weakest condition which guarantees that the stationary non-negative AR model has a stationary non-degenerative solution.     

ASYMPTOTIC EXPANSIONS FOR THE DISTRIBUTIONS OF SERIAL CORRELATIONS

Abstract. Let X ₁, …, X _n be a random sample from a population with a distribution function F and let E ( X ₁) = 0, E ( X ₁²) < ∞. Let r ₁=Σ _{t =1} ^{n -1} X _t X _{t +1}/Σ _{t =1} ^{n -1}( X _t ²+ X _{t +1}²). We derive a proper Edgeworth type expansion for the sampling distribution of r ₁ under the assumption that F is a mixture of Gaussian distributions of one of two given types. The result can easily be extended to the sampling distributions of serial correlations of arbitrary lag s .     

NON-NEGATIVE AUTOREGRESSIVE PROCESSES

Abstract. Consider a stationary autoregressive process given by X _t = b ₁ X _{t -1}+…+ b _p X _t-p + Y _t , where the Y _t are independent identically distributed positive variables and b ₁,…, b _p are non-negative parameters. Let the variables X ₁,…, X _n be given. If p = 1 then it is known that b ₁*= min( X _t / X _{t -1}) is a strongly consistent estimator for b ₁ under very general conditions. In this paper the case p = 2 is analysed in detail. It is proved that min( X _t / X _{t -1})→ b ₁ almost surely (a.s.) and min( X _t / X _{t -2})→ b ₂+ b ₁² a.s. as n → 8. The convergence is very slow. Denote by b ₁* and b ₂* values of b ₁ and b ₂ respectively which maximize b ₂+ b ₂ under the conditions X _t - b ₁ X _{t -1}- b ₂ X _{t -2}≥ 0 for t = 3,…, n . We prove that b ₁* b ₁ and b ₂* b ₂ a.s. Simulations show that b ₁* and b ₂* are better than the least-squares estimators of the autoregressive coefficients when the distribution of Y _t is exponential.     

A RANDOM PARAMETER PROCESS FOR MODELING AND FORECASTING TIME SERIES

Abstract. A generalized autoregressive (GAR) process {Z ( t ) ; t = 0 , ±1, …} is defined to satisfy the recurrence relation Z(t) = A_θ (t)Z (t -l)+ u( t ), where {A_θ(t); t = 0,±1, …} is itself a stochastic process depending on a vector parameter θ and where {u( t ); t = 0, ±1, …} is white noise with Eu ² ( t ) = a ². This paper develops theory and methodology and implementing the class of GAR processes for time series modeling and forecasting. Conditions on the 'parameter process' { A _θ ( t ); t = 0, ±1, …} are obtained for the existence of a GAR process; necessary and sufficient conditions on { A_θ ( t ) ; t = 0, ±1, …} for existence of a stationary GAR process are also obtained. Procedures are developed for computing maximum likelihood estimates of the parameters 0 and u² and for computing the minimum mean squared error forecasts for GAR processes.     

STATIONARY PROCESSES WITH A FINITE NUMBER OF NON-ZERO CANONICAL CORRELATIONS BETWEEN FUTURE AND PAST

Abstract. The spectral densities f (γ) are determined for stationary random processes X (t) with continuous time which have the property that the number of non-zero canonical correlations between the past X(t) ( t ≤ 0) (more accurately the present and the past) and the future X(t) ( t ≥θ) is finite (equal to N ) at any θ≥τ for some r > 0. A method for finding all the corresponding canonical correlations P₁, …, P_N and the canonical variables X ₁^-, …, X _N^- and X ₁⁺, …, X _N⁺ is given. Similar results related to processes X(t) with discrete (integral) time are briefly considered.