# How to estimate Structural VAR Models in EViews

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## how to estimate structural var models in eviews

In this tutorial, I will teach you how to estimate structural var models in eviews. Structural Vector Autoregression (SVAR) models are multivariate time series models that implement identification restrictions based on economic theory and/or other sensible assumptions. Some of the common restrictions implemented in the literature are Short-run restrictions, long-run restrictions, and sign restrictions. In this video, we will focus on long-run restrictions and will inspire our work on a paper written by Enders and Lee (1997).

By watching this video tutorial you will learn:

• Stationarity
• Requirements to use Long-Run Restrictions
• How to estimate the SVAR model
• Lag Length Criteria
• How to display structural impulse response functions
• How to implement the long-run restrictions
• Structural Variance Decomposition

And more!

Note: The model estimated in the tutorial is inspired and not an identical model as it is not my property. I use quarterly data, extend the dataset until 2019, and don’t replicate all the countries.

Let’s begin!

## structural vector autoregression: Blanchard and quah (1989)

In 1989, Blanchard and Quah (1989), proposed a new identification strategy for the parameters of a structural var. They proposed imposing restrictions on how shocks influence endogenous variables “in the long run”, and suggested limiting the responses of a variable to a shock.

A long run restriction is commonly associated with the concept of long run neutrality. Classical Macroeconomic Hypothesis specify that in the long run, permanent changes in nominal variables have no effect on real variables.

#### Examples

1. Permanent changes in the nominal money stock has no effect on the level of real output in the long run.
2. Permanent changes in the rate of inflation has no long-run effect on unemployment. (i.e, Picture a vertical Phillips Curve in the long run)
3. Permanent changes in the inflation rate has no long-run effect on the real interest rate. (Fisher Relationship in the long-run).

#### preliminary conclusion

Having provided diverse examples, imposing a long run restriction in our structural var model, implies limiting the cumulated responses of an endogenous variable in the model to a shock. For example, Blanchard and Quah (1989) estimated a bivariate VAR using Real GNP and Unemployment. The authors identified two shocks in the model: “supply shock” and “demand shock”.  The demand shock has no effect on real GNP in the long run, while a supply shock will have permanent effects.

## Example: real and nominal exchange rates

In our example, inspired on Enders & Lee (1997), we will try to decompose real and nominal exchange rate movements. We will see how real and nominal shocks affect real and nominal exchange rate movements. Please download the dataset to replicate the content.

#### Overview of the model

• There are two shocks: Nominal and Real shocks.
• Real shocks can cause permanent effects in the real exchange rate, but Nominal shocks can only affect the exchange rate in the short run.
• Picture a country that doubles their money supply. Price level and nominal exchange rate will double in the long run, while the real exchange rate will remain unaffected.

#### data

• We will estimate the model for Japan.
• We will use Quarterly Data: Period 1973:01 – 2019:04

## Requirements for the permanent effect

In order to use long run restrictions within an SVAR framework, at least one of the variables in our system needs to be non stationary (Cannot be I(0). It has has to be I(1)). Why?

• If the data we are using is stationary in levels, the
long-run impacts of shocks to the levels of the series are always zero.
• In other words, I(0) variables have no permanent components.

Note: Although we require at least one of the variables to be non stationary in levels, when estimating the model, the variables must be in a stationary form (i.e., applying differences).

#### test for stationarity

Having mentioned the requirements for the permanent effect, ensure to test for the stationarity of the variables. Ensure at least one of them is I(1). Ensure to conduct and report:

1. Graph Analysis
2. Correlogram Analysis

## imposing the long run restriction

$\begin{bmatrix} \triangle r_{t} \\ \triangle e_{t} \\ \end{bmatrix} = \begin{bmatrix} B_{11}(L) & B_{12}(L)\\ B_{21}(L) & B_{22}(L)\\ \end{bmatrix} * \begin{bmatrix} \epsilon_{rt} \\ \epsilon_{nt} \\ \end{bmatrix}$
• The first 2×1 vector is the real and nominal exchange rate variables in first difference.
• The last 2×1 vector represents the zero mean mutually uncorrelated real and nominal shocks.
• $B_{ij}$ coefficients represent the time path of the effects of the real and nominal shocks on the real and nominal exchange rates.
• The long run restriction implies that the cumulative effects of nominal shocks on real exchange rates are zero.
• Formally we are imposing:
$B_{12}(1)=\sum\nolimits_{j=0}^\infty b_{12}(j)=0$

## impulse response functions (irf): cumulative responses

#### Analysis of the impulse response functions

• The graph displays the cumulative impulse-response functions.
• The effect of a nominal shock on the real exchange rate vanishes in a short period.
•  The effect of a real shock on both real and nominal exchange rates is immediate. We can see a sudden and permanent increase.

## check out other free courses

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