"VAR" stands for *vector auto-regression*, which is a multiple time-series model / method. VAR is common in econometrics, & allows each time-series to be modeled based on its own previous values, & also the previous values of each of the other series, simultaneously. Thus, the series are given equal ...
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11 views
Imputation variance and explained variance (in vector autoregression)
I have a question concerning the coefficients of VAR models used on multiple imputed data (high missigness in some variables: up to 40%).
In particular I would like to know how the coefficients are ...
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27 views
Cointegration of a VAR(1) process
I am using a Johansen procedure to test for cointegration a vectorial 4-dim vector (timeserie).
First I tested for differential stationarity of each individual vector, all of those have a unit root ...
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36 views
Predict using VAR
If I arrive at an equation from VAR model; I know i can use it to predict at some relative period ahead of time by using predict(p1ct, n.ahead = 5 ci = 0.95); I ...
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1answer
40 views
Program Impulse Response Functions for VAR
I'm trying to program impulse response functions for a VAR model using Cholesky decomposition. The thing is I do not completely understand how I should do this when I read in the literature. Suppose I ...
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1answer
88 views
Positive & negative shocks in a VAR model and impulse response function in R
I have two general questions about impulse response functions in R using the package vars. Take a look at this code:
...
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1answer
85 views
Constructing a VECM with a mix of I(0) and I(1) variables
I've been using the Johansen Procedure to check and correct for cointegration in my model, by estimating a VECM instead of VAR. But now I want to estimate a new model, in which I expect the same ...
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62 views
Autocorrelation in Residuals for VAR model
I am making some macro three-variable VAR models in R, where one of my models have autocorrelation in the residuals, as tested by a B-G LM test.
The two models are:
...
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1answer
58 views
Ordering in VAR models
In Enders' 'Applied Econometric Time Series', I repeatedly stumbled upon the notion of the "ordering of a VAR model" and I am not sure I understand the concept right. As far as I understand it, the ...
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1answer
83 views
Monte Carlo: generating autocorrelated data from empirical distribution
my problem is the following: having a distribution function of daily casfhlows resulting from electricity trading, I need to calculate a yearly 99% VaR, i.e. the 1% percentile of yearly casfhlows ...
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73 views
weak exogeneity in VAR analysis
I have a problem interpreting the notion of "weak exogeneity in a VAR process". Assuming we have the following structural form:
$y_t = b_{12}z_t + \gamma_{11}y_{t-1} + \gamma_{12}z_{t-1} + ...
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18 views
Does a stationary VAR imply component time series are stationary?
What can be said about the relationship between a stationary VAR and its components?
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53 views
Sampling Stationary Vector Autoregression coefficients while Gibbs Sampling
I have been estimating a Bayesian Vector Autoregression using Gibbs Sampling. When constructing the posterior predictive distribution, I have noticed that when the simulated coefficients from the MCMC ...
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62 views
What's a stationary VAR?
What is a stationary VAR (vector autoregression)?
Can a VAR with non-stationary variables be stationary?
How do you test whether a VAR is stationary or non-stationary? (Example in ...
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94 views
samples from forecasts of VAR time series model in R
I'm trying to do a power analysis for a future experiment with time series financial data. We'll be splitting the data by random (actually, stratified) geographies, so we have a control and ...
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1answer
112 views
VAR vs STAR for space-time autoregression in Python
I want to use autoregressive model to build a predictor for some sets of spatio-temporal data. For example, I have historical traffic data (speeds at various segments of freeways). similarly, I have ...
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108 views
VAR/VECM Lags and Sample Size
Folks,
I have two related questions regarding VAR/VECM.
I get different co-integration results from the Johansen Procedure for VAR at different lags. At some lags (2,4,5,7) I reject the null ...
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64 views
Extending forecast model to ADL and comparing with VAR
I'm currently working on bond return data. I've data for 5,10,20 and 30 years. I've performed PCA, EFA. I also created an AR(11) model to forecast the return for the next month that's due. Now I want ...
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1answer
169 views
How do I use vector auto regression using the statsmodels library in python?
Sorry about the rookie question but I have been at the documentation for three days and couldn't figure much out. (Link To documentation page)
First, how do I load my own data? Must I store it in a ...
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78 views
How to convert from VAR (vector-auto-regression) on differences to original series. How to simulate orignal series from VAR on differences
I have been using the VAR {vars} program to find a fit for the following bi-variate time series (subset):
...
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76 views
Full information maximum likelihood VAR
Consider a $T \times n$ matrix of data $Y_{t}$ and its lagged values $Y_{t-1}$. The matrix $Y_{t}$ can be split into $$Y_{t}\equiv\left[\begin{array}{cc}
Y_{t,1} & Y_{t,2}\end{array}\right]
$$ ...
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217 views
R obtaining a VAR forecast when some variable outcomes are already known [closed]
I have four variables and would like to construct a VAR model I would then like to make a VAR forecast on one of the variables using my own data for the forecasts of the other three variables. Is ...
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1answer
197 views
Vector autoregression - number of lags
I am constructing a Vector autoregression model and I have used AIC to find how many lags I should use. Does 7 lags seem unreasonable? I am trying to find the impact the property market has had on the ...
