A0696
Title: Modeling and inferences for bivariate signed integer-valued autoregressive models
Authors: Sangyeol Lee - Seoul National University (Korea, South)
Minyoung Jo - Dankook University (Korea, South) [presenting]
Abstract: A first-order bivariate signed integer-valued autoregressive (BSINAR) model is examined, designed for analyzing time series of counts that may include negative values or exhibit negative autocorrelations or stochastic trends. For the estimation methods, the minimum density power divergence estimator (MDPDE) is considered well-known for its robustness against outliers. The limiting behavior of the MDPDE is examined under certain regularity conditions. The MDPDE is used to construct a score vector-based parameter change test. To assess the performance of the MDPDE and demonstrate its validity, a Monte Carlo simulation is conducted. The proposed methods are also applied to analyze earthquake data from the Earthquake Hazards Program of the United States Geological Survey (USGS) and financial data from Euro-Bund and BTP futures.
