"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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27 views

Multicollinearity, variable selection for cointegration testing in ARDL and VECM/VAR frameworks

I have 15 variables some of which are highly correlated. I want to run a cointegration test in the ARDL and VAR/VECM frameworks. Due to the correlation multicollinearity is a big problem; however, I ...
4
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2answers
112 views

Covariance of two time series driven by a restricted VAR(1) model

Suppose that I have two time series $X_n$ and $Y_n$ where: $$ X_n = \rho_x X_{n-1} + \epsilon_n $$ and $$ Y_n = \rho_y Y_{n-1} + \rho_{xy}X_n +z_n $$ Here, $z_n,\epsilon_n$ are independent random ...
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1answer
40 views

Modelling stationary and integrated time series in one system

I am currently investigating commodities and their impact on the oil price. I have 8 variables of different stationarities $y$ = dependent variable (oil price) is non-stationary I(1); three ...
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39 views

Vector autoregression: many variables (10), short sample (100)

Suppose there are ten observation sites along the road. A, B, C, D, E, F, G, H, I, J. We obtain data at each site once in a day, in this order. That is, first go to the site A at 9:00a.m., and when ...
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1answer
36 views

VAR/VECM/ARDL optimal lag selection

Question 1: Is it necessary to consider AIC and the BIC criteria when selecting the lag for a VAR, VECM or ARDL model OR can I use something else? Example: Can I pick 12 lags because the model simply ...
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1answer
36 views

Varying orders of integration - VAR/VECM model

I am building a VAR model, and have gotten a thorough set of guidelines through a question I asked a little while ago. However, I am left with some questions based on the following quote from Step 3 ...
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1answer
34 views

Vector autoregression for mix of stationary and nonstationary variables

I am currently investigating the impact of certain indicators such as GDP and inflation on the stock market. However some of my variables are non-stationary and some stationary in levels. All ...
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0answers
10 views

GLS estimator of a VAR process

I'm studiying how to derive the GLS estimator of a VAR process. I have studied the basics well, but I don't get the last passage here: Why the product can be rewritten as a quadratic form? Intuitively ...
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29 views

Comparison of different impulse response functions (IRFs)

I try to compare the impulse responses from different studies. However, I am not sure if my conclusions are correct. This is my problem: Two studies that use monthly data report the responses of ...
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1answer
91 views

VAR forecasting methodology

I am building a VAR model to forecast the price of an asset and would like to know whether my method is statistically sound, whether the tests I have included are relevant and if more are needed to ...
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0answers
29 views

VAR and cointegration

I'm facing an issue with this VAR models exercise; I want to know whether I solved it correctly or not. Point a) is easy, we choose $p=2$ since the information criteria has its minimum value there. ...
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0answers
5 views

Is it possible to combine linear VAR restrictions with SVAR A/B restrictions?

I have been exploring the various restrictions that are commonly applied to (S)VAR models in textbooks on multivariate time series, noting that linear restrictions on VAR models seem to be treated ...
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1answer
38 views

Granger causality - lag=1?

I have a question related to Granger Causality testing. Is it okay to use a lag-length of lag=1 in my Granger-test? The optimum lag length selection in my R ...
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1answer
24 views

Portmanteau test results R

When reading a VAR model tutorial I was confused by the below excerpt on the Portmanteau test for autocorrelation. My questions are: 1) How does one interpret the results of the below demonstration? ...
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12 views

Large negative error correction term in short-run dynamic analysis

Does it make sense to have a statistically significant error correction term of -1.999? The model has passed the necessary diagnostic and stability tests.  
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0answers
37 views

Times series homework: VAR model

I am looking for help in understanding my homework. I do not understand how to solve this problem. I hope some of you can help me on how to approach it or suggest some references.
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1answer
45 views

Does the Granger Causality test in the “vars” package make sense?

We all understand that the Granger Causality test entails constructing two models. The first one is simply an autoregressive model with $y_{t-1}$ being the single independent variable. The second one ...
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1answer
20 views

VAR lag selection heavily depends on maximum lag investigated

I am fitting an Error Correction Model with two monthly price time series. In Stata I am using the varsoc command to determine the number of lags that are ...
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0answers
51 views

VAR estimation results

I have two time series, say, x1 and x2. I want to build a VAR model with these. Unfortunately, estimation of my VAR model fails ...
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2answers
39 views

Selecting best ARIMA model with regressors and dummy variable

I have data on GDP, employment rate, inflation and production on two countries and I like to make some ARIMA models. I have done this before, but not with including regressors. Also, the time period ...
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1answer
29 views

Literature on VARs

I'm looking for literature where vector autoregressions are comprehensively described. I'd like to know which consequences some assumptions lead to. For example, which properties of estimates follow ...
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0answers
25 views

How to know if a VAR model has Granger causality in the short run?

I have the following VAR model Does this VAR imply that there is Granger causality in the short-run? Does $y_{1t}$ cause $y_{2t}$, or does $y_{2t}$ cause $y_{1t}$ in the short-run? Or do they cause ...
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1answer
57 views

What are the more Advanced models for time series

As far as my studies go, I did: ARIMA in all sauces Dynamic linear models/state space model. The basics VAR(IMA) VECM I then tried to see if there is a model that combines some or most of the ...
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3answers
110 views

VAR model residuals having significant correlation at lag 12

I have tried to fit a VAR model for two stationary time series dlogsl_ts and dlogllc_ts(tested by PP test and ADF test), the ...
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1answer
42 views

Interpretation of VAR results on R

I am trying to run VAR on the first differences of XLE and Brent futures. Prior to this I have already tested that the series in levels are I(1) and are not cointegrated. Below are my results using ...
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0answers
108 views

Building VAR (Vector Autoregression) model with dummy variables in R

I came across vars package in [R] and it seems the package does everything I need for a VAR model. The only exception is that I need to define dummy variables. For example think that my dependent ...
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41 views

Autocorrelated Returns?

I'm trying to compute some VAR models for the Amgen Pharmaceutical company (NasdaqGS: AMGN), however I've noticed that the daily returns seem to be significantly autocorrelated at a number of lags ...
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51 views

Stationary vs Stability

I am searching for an example of an unstable VAR($p$) process (its reverse characteristic polynomial has no roots inside and on the complex unit circle) which is stationary. I come up with this ...
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0answers
21 views

Stability of VAR$(p)$: Prove $\det(I_{Kp}-\tilde{A}z)=\det(I_k-A_1z-\ldots-A_pz^p)$

Given we have a VAR$(p)$ process written in the companion form $$\tilde{y}=\tilde{v}+\tilde{A}\tilde{y}_{t-1}+\tilde{u}_t$$ where $$\tilde{A}=\left(\begin{array}{ccccc} A_1& A_2 & \ldots ...
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29 views

How should I use R to perform VECM if I have known the cointegrating vector?

could anybody help me out with this? I have found the cointegrating vector using DOLS, and now I want to impose that cointegrating vector in the VECM model, could I still use functions in ...
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1answer
59 views

Dataset for vector autoregression in R [closed]

I am using R for vector autoregression. I can't find data that can fit the model. Can you help me please? Or can you suggest another alternative such as how to get my own data?
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22 views

Stable VAR($p$) procress: Is there an easy way to do this?

Assume a $K$-dimensional VAR($p$) process given by $$y_t=\nu+A_1y_{t-1}+\ldots+A_py_{t-p}+u_t$$ This process is called stable if the roots of the reverse characteristic polynomial are bigger than 1 in ...
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0answers
52 views

Granger Causality lag / Impulse Response

I have a question regarding the connection between Granger Causality Analysis and a VAR Impulse Response Function. Let's say we intend to conduct a Granger causality analysis and first make sure that ...
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111 views

R - Impulse Response Interpretation

Assume we have following R script that enables to plot the Impulse Response Function with package vars: ...
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1answer
79 views

Testing Restrictions on Beta (long run coefficients), R example

The code given below estimates a VEC model with 4 cointegrating vectors. It is a reproducible code, so just copy and paste into your R console (or script editor). ...
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0answers
43 views

Cointegration and long run restrictions

Below is a typical output from a 5 variable VECM model with 4 cointegrating vectors with $r-1$ restrictions on each equation. Therefore, the top part of the output is a diagonal matrix by default. For ...
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33 views

Cointegrating vectors/equations

I am experimenting with 5 variable co-integration model via simulations. Below are output of cointegrating vectors from two different data sets. As you can see there are 4 long run equations in each ...
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0answers
38 views

VAR Stability - Lag Order Selection

I followed this excellent tutorial on the implementation of Granger causality: http://davegiles.blogspot.de/2011/04/testing-for-granger-causality.html and applied the method with an R script. My date ...
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1answer
101 views

Lag Length from a VAR and Vector Error Correction Model (VECM)

A colleague wrote a paper and I am reviewing it for him to make sure it is good. In the paper, the author estimated a VAR to determine the optimal lag length based on the Schwartz Criterion. Then ...
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0answers
33 views

Prediction intervals for levels using a VAR model in second differences

Given a VAR model for the second differences of a vector time series, $\Delta^2 y$, how to obtain the one-step-ahead (and possibly $h$-step-ahead) prediction intervals for the series in levels, $y$? ...
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0answers
48 views

Using Bayesian econometrics to forecast macro data (BVAR model)

I am in the middle of a Bayesian class. I have to make a project where I implement Bayesian statistics. I have chosen to do this on macro data. As far as I can see the optimal model to forecast ...
1
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1answer
59 views

Granger causality test in VAR framework: small sample

I am using Granger Causality Test in VAR framework to test the causal relationship between renewable energy consumption, gross domestic product (GDP) and carbon dioxide emissions in one country using ...
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0answers
38 views

Multivariate Linear Regression - Unexpected Sign of Coefficient - Mitigation Options via different models

I am trying to forecast corporate earnings for a number of companies using multivariate linear regression. As independent variables I am using: "Oil Price Change" "GDP Growth" "Exchange Rate ...
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1answer
89 views

How do I model the time series with only one but huge level shift into a VAR-Model?

I am about to analyze the relationship of several variables using a VAR/VECM-Model. But one of the series presents a massive level shift that occurs within one period (caused by policy change) like ...
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0answers
46 views

Confidence interval for sum of forecasts

I've got two time series, let's say X and Y. They are correlated. I can obtain forcast for X and for Y separatly (I'm using VAR model) and confidence intervals for them. Then I would like to make a ...
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0answers
42 views

IRF bootstrap confidence intervals exceed the 95% confidence bands, any thoughts why?

The image below shows the results from point estimate of IRFs and bootstrap confidence intervals constructed via recursive estimation of VECMs in a rollowing window VECM modelling. I am getting ...
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0answers
75 views

Cointegration - stationary time series in R

I have 2 time series that are stationary (according to ADF, PP, KPSS tests) so I cannot use the Johansen cointegration test. Can you recommend what model should I use in this case? I will try to ...
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0answers
23 views

Vector Autoregression: Difference between first-differencing, logs and logs of levels?

I want to use a vector autoregression in order to estimate an impulse response analysis, but I am not sure what the differences between first-differencing, logs and logs of levels are. Is there any ...
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17 views

Impulse Response Functions for LVSTAR

I am currently struggeling with a LVSTAR (logistic smooth transition vector autoregression model) with 2 states. That model is given by $$ \boldsymbol{y_t} = F(z_t)\boldsymbol{\Pi_1' x_t} + ...
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84 views

Extract shock size from impulse response function in a VAR system

I have the results from a standard VAR model with Monte Carlo simulated confidence bands. I have the graphs for the impulse responses as well and I know that the shock size is one standard deviation. ...