Time Series Concepts for Conditional Distributions

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Sieve inference on possibly misspecified semi-nonparametric time series models (2014, background): “1 Local Geometry The convergence rate (2) of Chen and Shen (1998) implies that b T 2 BT B0 with probability approaching one, where B0 f 2 A : k 0k C T log(log(T ))g; BT B0 \ AT : (9) Hence, we now regard B0 as the e¤ective parameter space and BT as its sieve space.”
Efficient Estimation of Copula-Based Semiparametric Markov Models (2009, background): “Granger (2003) defines persistence (such as “long memory” or “short memory”) for general nonlinear time series models via copulas.”
Granger-causality in quantiles between financial markets: Using copula approach ☆ (2014, unknown): “Following Hong and Li (2005), we set h 1⁄4 b σR−1=6, where b σ is the standard error ofb utþ1.”
A Nonparametric Test for Granger Causality in Distribution With Application to Financial Contagion (2014, methodology): “Indeed, as emphasized by Granger (2003) and Engle and Manganelli (2004), the timeseries behavior of quantiles can vary considerably across the distribution because of long memory or nonstationarity.”
Inhomogeneous Dependence Modeling with Time-Varying Copulae (2009, unknown): “For a concise survey on stylized empirical facts from financial returns see Cont (2001) and Granger (2003).”