Stationary Vector Autoregressive Representation of Error Correction Models

Generalization error bounds for stationary autoregressive models

We derive generalization error bounds for stationary univariate autoregressive (AR) models. We show that imposing stationarity is enough to control the Gaussian complexity without further regularization. This lets us use structural risk minimization for model selection. We demonstrate our methods by predicting interest rate movements.

Automated Estimation of Vector Error Correction Models

Model selection and associated issues of post-model selection inference present well known challenges in empirical econometric research. These modeling issues are manifest in all applied work but they are particularly acute in multivariate time series settings such as cointegrated systems where multiple interconnected decisions can materially a¤ect the form of the model and its interpretation. ...

Modeling Equilibrium Relationships: Error Correction Models with Strongly Autoregressive Data

Political scientists often argue that political processes move together in the long run. Examples include partisanship and government approval, conflict and cooperation among countries, public policy sentiment and policy activity, economic evaluations and economic conditions, and taxing and spending. Error correction models and cointegrating relationships are often used to characterize these eq...

Vector Multiplicative Error Models: Representation and Inference∗

The Multiplicative Error Model introduced by Engle (2002) for positive valued processes is specified as the product of a (conditionally autoregressive) scale factor and an innovation process with positive support. In this paper we propose a multivariate extension of such a model, by taking into consideration the possibility that the vector innovation process be contemporaneously correlated. The...

Vector Autoregressive Models