Multivariate forecasting

Introduction to multiple time series

Testing for cointegration with Johansen

Vector Autoregression (VAR)

Vector Autoregression (VAR)

We consider a vector of observables, not just one

Autoregressive (AR) model for a vector.

VAR(p) looks \(p\) back.

The AR(\(p\)) model is:

\(y_t=\alpha + \sum_{i=1}^p\beta y_{t-i}+\epsilon_t\)

VAR(\(p\)) generalises this to where \(y_t\) is a vector. We define VAR(\(p\)) as:


\(y_t=c + \sum_{i=1}^pA_i y_{t-i}+\epsilon_t\)

VAR impulse response

Bayesian VAR

Structural models

Autoregressive Distributed Lag (ARDL) model

Include lagged y and lagged x (and current x)

If the processes are stationary, then we can use OLS. THIS IS A BROADER POINT! INTRO??



Error Correction Model

Static model

Like PAM we start with static estimator.


The ECM does a regression with first differences, and includes lagged error terms.

We start with a basic first-difference model.

\(\Delta y_t= \Delta x_t\)

We could also expand this to include laggs for both x and y. Here we don’t.

We know that long term \(y_t=\theta x_t\). We use the error from this in a first difference model.

\(\Delta y_t= \alpha \Delta x_t + \beta (y_{t-1}-\theta x_{t-1})\)

Page on identifying error terms

Also, page on Vector Error Correction Model (VECM)

Partial Adjustment Model

Estimating a static model

We start by estimating a static model.

\(y_t=\alpha + \theta x_t + \gamma_t\)


We then use this form an equilibrium for \(y_t\), \(y_t^*\).

\(y_t^*=\hat \alpha + \hat \theta x_t \)

The process depends on the difference from this equilibrium.

\(y_t-y_{t-1}=\beta (y_{t}^*-y_{t-1})+\epsilon_t \)

\(y_t-y_{t-1}=\beta (\hat \alpha + \hat \theta x_t -y_{t-1})+\epsilon_t \)

\(y_t=\beta \hat \alpha + \beta \hat \theta x_t + (1-\beta )y_{t-1}+\epsilon_t \)

\(y_t=\alpha y_{t-1}+(1-\beta )(y_{t}^*-y_{t-1})+\epsilon \)

The higher \(\beta \), the slower the adjustment.

If stationary, can we can use OLS.