Vector Autoregressive and Vector Error Correction Models

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Practical Problems with Reduced-Rank ML Estimators for Cointegration Parameters and a Simple Alternative (2005, unknown): “…of observations (T = 160), we also use Table 1: DGP Properties K r p Reference DGP (A) 3 1 5 y * t = (m t , gnp t , R t , t) β * = (1, −1, 4, −.0015) Lütkepohl (2004) DGP (B) 3 2 6 y t = (c t , i t , y t ), β = 1 0 −1 0 1 −1 King et al. (1991) DGP (C) 2 1 1 y t = (y 1t , y 2t ), β = 1 −1…”
Energy markets volatility modelling using GARCH (2014, background): “In fact, VAR and VEC are equivalent, as demonstrated by Lütkepohl (2004).”
Competition in Germany’s Minute Reserve Power Market: An Econometric Analysis (2014, background): “…2 t 1 t 2 t the information set has no effects on the optimal forecast of . y 1 t Instead, Granger-causality exists if the equation holds for at least one step, (see Lu¨tkepohl, 2004: 144). regres-h To avoid spurious regres-h sions, we ﬁrst have to check whether the MRP price series are stationary.”
Vector Autoregressive Models (2011, background): “Other expository treatments are Amisano and Giannini (1997), Watson (1994), Breitung, Brüggemann and Lütkepohl (2004) and Lütkepohl (2005).”
Specification, estimation and evaluation of vector smooth transition autoregressive models with applications (2014, background): “Lütkepohl (2004) draws attention to the possibility that the error covariance matrix of a vector model may in practice be nonconstant.”