NONCAUSAL VECTOR AUTOREGRESSION

By Markku Lanne, Pentti Saikkonen. Econometric Theory, 2013

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Filtering, Prediction and Simulation Methods for Noncausal Processes (2016, methodology): “In this aspect, our approach differs from Lanne and Saikkonen (2013) and Lanne and Luoto (2014), where the autoregressive polynomial matrix is defined as a product of a causal and a noncausal components of predetermined orders.”
Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments (2018, unknown): “One approach is to assume that the shocks are independent and non-Gaussian, and to exploit higher order moment restrictions to identify the causal structure (Lanne and Saikkonen, 2013; Gospodinov and Ng, 2015; Gouri eroux et al., 2017).”
Autoregressive models for matrix-valued time series (2018, background): “On the other hand, to retain a MAR(1) model with possibly non-causal coefficient matrices, non-causal vector autoregression may be considered (Davis and Song, 2012; Lanne and Saikkonen, 2013).”
Bayesian inference on structural impulse response functions (2019, methodology): “Lanne & Saikkonen (2013) develop asymptotic theory for a modified VAR model that allows for noninvertibility, but they do not consider structural estimation.”