Testing for unit root processes in random coefficient autoregressive models
نویسندگان
چکیده
منابع مشابه
Testing for a Unit Root in a Random Coefficient
This paper proposes a new unit root test in the context of a random autoregressive coefficient panel data model, in which the null of a unit root corresponds to the joint restriction that the autoregressive coefficient has unit mean and zero variance. The asymptotic distribution of the test statistic is derived and simulation results are provided to suggest that it performs very well in small s...
متن کاملTesting for a Unit Root against Transitional Autoregressive Models
This paper considers the test of a unit root in transitional autoregressive models. In particular, we develop the asymptotic theory of the inf-t test for the null hypothesis of a unit root in a wide class of nonlinear autoregressive models having parameters that are identified only under the alternative of stationarity. Our framework is very general and allows for virtually all potentially inte...
متن کاملGaussian Processes for Functional-Coefficient Autoregressive Models
This work is concerned with nonlinear time series models and, in particular, with nonparametric models for the dynamics of the mean of the time series. We build on the functional-coefficient autoregressive (FAR) model of Chen and Tsay (1993) which is a generalization of the autoregressive (AR) model where the coefficients are varying and are given by functions of the lagged values of the series...
متن کاملEstimation in nonstationary random coefficient autoregressive models
We investigate the estimation of parameters in the random coefficient autoregressive model Xk = (φ+ bk)Xk−1 + ek, where (φ,ω 2, σ2) is the parameter of the process, Eb0 = ω2, Ee0 = σ 2. We consider a nonstationary RCA process satisfying E log |φ + b0| ≥ 0 and show that σ2 cannot be estimated by the quasi-maximum likelihood method. The asymptotic normality of the quasi-maximum likelihood estimat...
متن کاملEstimation in Random Coefficient Autoregressive Models
We propose the quasi-maximum likelihood method to estimate the parameters of an RCA(1) process, i.e. a random coefficient autoregressive time series of order 1. The strong consistency and the asymptotic normality of the estimators are derived under optimal conditions.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Econometrics
سال: 2008
ISSN: 0304-4076
DOI: 10.1016/j.jeconom.2007.09.002