M1 Money Supply which can be obtained from the BSP’s websiteĬPI Inflation Rate which is reported monthly by the Philippine Statistics Authority and measures the relative increase in prices based on a Laspeyres price index. This is, by all accounts, the main policy rate that the Philippine central bank controls. Overnight Reverse Repurchase Rate (RRP) which is set by the Bangko Sentral ng Pilipinas. In this model, we will be using four variables. In particular, we will be using a framework developed by Sims (1992) using Philippine data. We will now apply the numerous concepts learned in VAR in an actual example. The assumption of no-serial correlation also holds in this regard. Doing this suggests that it is essentially equivalent to a generalized least squares. Moreover, estimates are also efficient in that all equations have identical regressors which minimizes the variation in each one. What we find is that estimates are consistent as only the lagged values of the endogenous variables are on the right-hand side of the equation. The estimation of a VAR is an Equation by Equation OLS. Consider a VAR system with only two variables. Commonly, we notate a VAR as a VAR(p) where p denotes the number of autoregressive lags in the system. Said simply, the VAR is essentially a generalization of the univariate autoregressive model.
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How does VAR look likeĪs we have mentioned previously, the VAR is a multivariate linear time series model where the endogenous variables in the system are functions of the lagged values of all endogenous variables. Moreover, VARs can help distinguish between competing theoretical models. It can pinpoint sources of fluctuations that traditional univariate models fail at. We note that VARs can investigate the response to shocks. The majority of empirical studies on forecasting suggest that the VAR has already eclipsed the traditional univariate forecasting models and theory-based structural equation models.Īpart from forecasting, VARs are also useful tools for structural analysis. We will show how VARs can be much better than standard univariate forecasting models, especially in determining the long-run. Firstly, it is a sophisticated forecasting tool. This is the thrust and the main use of the Vector Autoregression. It has been used widely in simulating macroeconomic shocks to the real economy and has been used heavily in policy simulations and forecasting. VARs are very useful especially in the field of macroeconomics. Essentially, it is macroeconomic modeling without much of the a-priori expectations getting in the way.
A VAR could model macroeconomic data informatively, without imposing very strong restrictions or relationships. This allows for a simple and flexible alternative to the traditional structural system of equations. Christopher Sims proposed the Vector Autoregression which is a multivariate linear time series model in which the endogenous variables in the system are functions of the lagged values of all endogenous variables.
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