Since our model is a quarterly VAR for M2 demand, centered seasonal dummy variables are included as exogenous variables. The following R code consists of 1) data loading, 2) VAR in level ( vars package), 3) VAR in difference ( vars package), and 4) VAR in difference ( tsDyn package). It is because of the fact that many researchers usually use VAR model in difference but some researchers prefer to use VAR in level irrespective of the stationarity. 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. In R, a sequence of elements which share the same data type is known as vector. Lower these scores are better since these criteria penalize models that use more parameters. A vector is a basic data structure which plays an important role in R programming.
Syntax to create a vector c (#Vector code for r how to#
Lag length (p) is selected by using several information criteria : AIC, HQ, SC, and so on. How to create a vector The function c () function is used to combine a sequence of data elements of the same basic data types in R. Given a vector of endogenous variables, \(X_t = (X_\]
#Vector code for r series#
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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