Hello, I am new to VARs and currently building a SVAR with the following variables to analyse monetary policy shocks and their affects on house prices: House prices, GDP Growth, Inflation, Mortgage Rate first differenced, Central Bank Base Rate First Differenced and M4 Growth Rate. The aim of the VAR analysis is to generate impulse responses of house prices to monetary policy shocks, and understand the forecast error variance decomposition.
I'm planning on using the Base Rate and the M4 Growth Rate as policy instruments, for a time period spanning 1995 to 2015. Whilst all variables are reject the null hypothesis of non stationarity in an Augmented Dickey Fuller test, with 4 lags, the M4 growth rate fails to reject the null hypothesis.
Now, if I go ahead anyway and create a SVAR via recursive identification, the VAR is stable (eigenvalues within the unit circle), and the LM test states no autocorrelation at the 4 lag order.
Is my nonstationarity of M4 Growth Rate an issue here? As I am not interested in parameter estimates, but rather just impulse responses and the forecast error variance decomposition, there is no need for adjusting M4 Growth rate. Is that correct?
Alternatively I could first difference the M4 growth rate to get a measure of M4 acceleration, but I'm not sure what intuitive value that would add as a policy instrument.
Many thanks in advance for your help, please let me know if anything is unclear.
KB
VAR requires that all series should be I(0). You may check cointegration and in absence of cointegration, can take first difference of I(1) series.
You can use the ARDL method that does not require the same order of integration.
1) A couple of clarifications:
The ADF null is "unit root", i.e. NON-stationarity.
Impulse response profiles derive from the estimated coefficients.
2)
Take a closer look at a graph of the M4 growth rate. A non-stationary verdict can occur when a series is actually stationary around a more complex trend than specified in the test - for example a linear trend with a structural break.
I will answer this question by suggesting that you consider your basic model.
1. You have a strange mixture of nominal, real variables and growth rates.
2. Interest rates are the equivalent of log differences
3 If the growth rate of M4 is non stationary this implies that the log of M 4 is I(3). I find this hard to believe.
4. Are there seasonal effects in your data?
5. I would be very doubtful if ARDL is valid in your case.
6. If I understand correctly you intend to identify your system by using a particular ordering of your variables. It will be difficult to justify any particular order
7.I would be concerned that you might have trouble accounting for bubbles in house prices and subsequent falls. The 20 years that you are using was a very difficult time for house prices and they were out of equilibrium for a lot of this period. Your project is very ambitious.
8. I would recommend that you talk to your supervisor and agree an analysis that might be more feasible..
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