En el presente artículo se muestra un algoritmo iterativo para la estimación de parámetros de modelos ARMA en series temporales que tengan alguna observación ausente. Posteriormente se efectúa la demostración de la convergencia de dicho algoritmo. Se presenta un ejemplo de estimación basado en la simulación de series temporales con un ordenador y se exponen las conclusiones llevadas a cabo por el autor.
Sea {Xt}t ∈ Z+ una serie de tiempo estacionaria que sigue el modelo autorregresivo de orden 1: Xt = λ + ρXt-1 + et, siendo {et} variables aleatorias i.i.d. de media cero y varianza σ2; a partir de una muestra del proceso {X1, ..., Xn} se calcula en una primera etapa τ'n, estimador no paramétrico de la función de predicción τ(x) = E[Xt/Xt-1 = x] y Ω'n, estimador no paramétrico de la función de distribución asociada al proceso. Esto nos permite en una segunda etapa calcular estimaciones de los parámetros...
En este artículo se estudia, desde una perspectiva bayesiana, un proceso AR(1) con errores exponenciales, ARE(1): para ello se construye una nueva familia de distribuciones conjugadas, denotada por CDG, que permite construir una especie de filtro de Kalman para la estimación recursiva de los parámetros del modelo.
Usually the problem of drift estimation for a diffusion process is considered under the hypothesis of ergodicity. It is less often considered under the hypothesis of null-recurrence, simply because there are fewer limit theorems and existing ones do not apply to the whole null-recurrent class. The aim of this paper is to provide some limit theorems for additive functionals and martingales of a general (ergodic or null) recurrent diffusion which would allow us to have a somewhat unified approach...
The one sided unit root test of a first-order autoregressive model in the presence of an additive outlier is considered. In this paper, we present a formula to compute the size and the power of the test when an AO (additive outlier) occurs at a time k. A small sample case is considered only.
The one sided unit root test of a first-order autoregressive model in the presence of an additive outlier is considered. In this paper, we present a formula to compute the size and the power of the test when an AO (additive outlier) occurs at a time k. A small sample case is considered only.
The two sided unit root test of a first-order autoregressive model in the presence of an innovation outlier is considered. In this paper, we present three tests; two are usual and one is new. We give formulas computing the size and the power of the three tests when an innovation outlier (IO) occurs at a specified time, say k. Using a comparative study, we show that the new statistic performs better under contamination. A Small sample case is considered only.
We consider a fixed-design regression model with long-range dependent errors which form a moving average or Gaussian process. We introduce an artificial randomization of grid points at which observations are taken in order to diminish the impact of strong dependence. We estimate the variance of the errors using the Rice estimator. The estimator is shown to exhibit weak (i.e. in probability) consistency. Simulation results confirm this property for moderate and large sample sizes when randomization...