NONCAUSAL VECTOR AUTOREGRESSION

Lecture 6 : Vector Autoregression ∗

Noncausal Vector Linear Prediction Filters

A new impulse noise detection technique from gray-scale and multichannel images is presented. It is based on block correlation of the noncausal linear prediction. The linear prediction coefficients are calculated using a block of the image at one instant. These coefficients are valid within that block only and are used to predict a pixel in a sliding window within the block. The sample is predi...

Structured Regularization for Large Vector Autoregression

The vector autoregression (VAR), has long proven to be an effective method for modeling the joint dynamics of macroeconomic time series as well as forecasting. One of the major disadvantages of the VAR that has hindered its applicability is its heavy parameterization; the parameter space grows quadratically with the number of series included, quickly exhausting the available degrees of freedom....

Fully Modified Least Squares and Vector Autoregression

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A Bayesian Poisson Vector Autoregression Model

Multivariate count models are rare in political science, despite the presence of many count time series. This article develops a new Bayesian Poisson vector autoregression (BaP-VAR) model that can characterize endogenous dynamic counts with no restrictions on the contemporaneous correlations. Impulse responses, decomposition of the forecast errors, and dynamic multiplier methods for the effects...