![]() ![]() In principle, we need to check for the stationarity of each variables but in this exercise, we use both models of VAR in level and VAR in difference. In particular, when dealing with money demand function, it is important to include seasonal or monthly dummy variables to capture cyclical variations of money demand due simply to different seasonal and monthly patterns of payments or receipts. It seems that there is a cointegration among money, income and interest rates. ![]() On the other hand, the negative relationship between money and interest rate is found since the interest one gives up is the opportunity cost of holding money. Visual inspections of this data in level show that real income rises, there is a demand for more money to make the transactions. From the following results, we set lag lengths of VAR in level and VAR in difference to 2 and 1 respectively. This work can be done easily by using VARselect() function with a maximum lag. Lower these scores are better since these criteria penalize models that use more parameters. Lag length (p) is selected by using several information criteria : AIC, HQ, SC, and so on. Given a vector of endogenous variables, \(X_t = (X_\] In principle, there are three basic pairs of (model, data) in the context of the vector time series modeling. ![]() When there are the presence of long-term equilibrium relationships, a vector error correction model (VECM) is used, which consists of a VAR model and error correction equations. We also consider VAR in level and VAR in difference and compare these two forecasts.įor a vector times series modeling, a vector autoregressive model (VAR) is used for describing the short-term dynamics. We use vars and tsDyn R package and compare these two estimated coefficients. This post gives a brief introduction to the estimation and forecasting of a Vector Autoregressive Model (VAR) model using R. ![]()
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