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Strict Stationarity Testing and GLAD Estimation of Double Autoregressive Models

Id：2564 Date：20200306 Status：published ClickTimes：
作者Shaojun Guo, Dong Li, Muyi Li
正文In this article we develop a tractable procedure for testing strict stationarity in a double autoregressive model and formulate the problem as testing if the top Lyapunov exponent is negative. Without strict stationarity assumption, we construct a consistent estimator of the associated top Lyapunov exponent and employ a random weighting approach for its variance estimation, which in turn are used in a -type test. We also propose a GLAD estimation for parameters of interest, relaxing key assumptions on the commonly used QMLE. All estimators, except for the intercept, are shown to be consistent and asymptotically normal in both stationary and explosive situations. The finite-sample performance of the proposed procedures is evaluated via Monte Carlo simulation studies and a real dataset of interest rates is analyzed.
JEL-Codes：C15; C22
关键词：DAR model; GLAD estimation; Nonstationarity; Random weighting; Strict stationarity testing
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