Empirical likelihood for martingale differences
研究了向量观测值为鞅差分时的经验似然方法，在条件Lindeberg条件下证明了Wilks型定理，并推广到近似鞅差分；应用包括线性与非线性自回归模型、ARCH(1)模型及异方差非线性自回归模型的置信域构建，以及马尔可夫链的分块经验似然。
In this article, we consider an empirical likelihood with vector observations that are martingale differences and prove a Wilks' type theorem under a conditional Lindeberg condition. We then generalize this result to approximate martingale differences. As applications of the first result we discuss the construction of confidence regions for various time series models, including linear and nonlinear autoregressive models, an ARCH(1) model, and nonlinear autoregressive models with heteroskedasticity. The second result is used to derive a Wilks' type theorem for a blockwise empirical likelihood for Markov chains.