Showing 1 - 10 of 614
Many time series exhibit unconditional heteroskedasticity, often in addition to conditional one. But such time-varying volatility of the data generating process can have rather adverse effects when inferring about its persistence; e.g. unit root and stationarity tests possess null distributions...
Persistent link: https://www.econbiz.de/10010375374
This note puts forward a new modelling approach that includes both fractional integration and autoregressive processes in a unified framework. The proposed model is very general and includes other more standard approaches such as the AR(F)IMA models. Some Monte Carlo evidence shows that the...
Persistent link: https://www.econbiz.de/10015426971
In this paper we derive tests for parameter constancy when the data generating process is non-stationary against the hypothesis that the parameters of the model change smoothly over time. To obtain the asymptotic distributions of the tests we generalize many theoretical results, as well as new...
Persistent link: https://www.econbiz.de/10002570513
In this paper, we show how to estimate consistently the degree of fractional integration at a given frequency θ, for both stationary and non stationary long-memory process. The statistics used are the periodogram for values θn which converge to θ with an appropriate rate. We also introduce...
Persistent link: https://www.econbiz.de/10014187525
This paper introduces a novel way of differentiating a unit root from a stationary alternative. We write up the model consisting of zero and nonzero parameters. If the lagged dependent variable has a coefficient of zero, we know that the variable has a unit root. We exploit this property and...
Persistent link: https://www.econbiz.de/10014212098
The purpose of this paper is to investigate the asymptotic null distribution of stationarity and nonstationarity tests when the distribution of the error term belongs to the normal domain of attraction of a stable law in any finite sample but the error term is an i.i.d. process with finite...
Persistent link: https://www.econbiz.de/10014075550
The random coefficient autoregressive model has been utilized for modeling financial time series because it possesses features that are often observed in financial time series. When the mean of the random autoregressive coefficient is one, it is called the stochastic unit root model. This paper...
Persistent link: https://www.econbiz.de/10014107239
While differencing transformations can eliminate nonstationarity, they typically reduce signal strength and correspondingly reduce rates of convergence in unit root autoregressions. The present paper shows that aggregating moment conditions that are formulated in differences provides an orderly...
Persistent link: https://www.econbiz.de/10013148982
In an important generalization of zero frequency autoregressive unit root tests, Hylleberg, Engle, Granger, and Yoo (1990) developed regression-based tests for unit roots at the seasonal frequencies in quarterly time series. We develop likelihood ratio tests for seasonal unit roots and show that...
Persistent link: https://www.econbiz.de/10013153597
Seemingly absent from the arsenal of currently available "nearly efficient" testing procedures for the unit root hypothesis, i.e. tests whose local asymptotic power functions are indistinguishable from the Gaussian power envelope, is a test admitting a (quasi-)likelihood ratio interpretation. We...
Persistent link: https://www.econbiz.de/10013156595