"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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Testing for structural break in the covariance

I estimated a bivariate VAR(p) model and assume that there exist two covariance regime $\Sigma_1$ for the period 1 to $T_B$ and $\Sigma_2$ for the period $T_B+1$ to $T$. I am now interest in testing ...
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6 views

Relation Between Lag length of ADF test and Lag of VAR After that

Suppose I have three variable and they are difference stationary at different lags. How should I decide the no of optimum lags in the VAR of the three variable? I understand the lag length criteria of ...
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22 views

What is the difference between “lag order” and “maximum lags”

The R Vars package has a Vector Auto Regression function called var. The arguments include (among other things) "p" defined as the "Integer for the lag order" and "lag.max," which is defined as ...
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2answers
28 views

How can I forecast interrelated hierarchies?

I need to model demand for server components. Server 1 & Server 2 both use Hard Drive B, Server 1 uses Network Card A, and Server 2 uses Network Card C. ...
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25 views

How to quantify the impact of a variable in a VAR model equation?

Given a VAR model (the equations that make the model, coefficient significance and the adjusted $R^2$ value for each equation), is there a way to calculate the impact of a variable over the other?
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25 views

Linear VAR impulse responses - sensitivity of confidence interval bands to shock size

My main question is: is the statistical significance of an impulse response in a linear VAR dependent on the size of the shock? Or put alternatively, how do the upper/lower confidence interval bands ...
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15 views

Estimate VAR model from data about lags

Does anybody have any idea how i would write the var model based on this table? What coefficients should be included? Any hint will be much appreciated. Thank you!
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1answer
20 views

Why Granger causality test gives same result for restricted and unrestricted VAR models

I applied granger causality test 1st in unrestricted 2 dimensional VAR(1) model and then restricted model (t>2). Both are giving the same result (the result of unrestricted VAR model). Actually ...
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15 views

All of the series used in a model must be stationary at the same order of differencing

While practicing VAR analysis, all of the series used in the model must be stationary at the same order of differencing. Is this correct? For example, let $X$~$I(1)$ and $Y$~$I(2)$. Can I use these ...
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1answer
24 views

Does VECM use the stationary series or the originals ones?

I have some cointegrated series and I decided to build a VECM model. (I differentiated them twice in order to get stationary series and that led me to believe that they might be cointegrated - I ...
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1answer
31 views

Finding optimal lag order for an exogenous regressor in a VAR model

I can't use VARselect as it gives lags in a VAR model which considers all the variables to be endogenous. In my case, one of the variables is exogenous and affects dependent variable with a certain ...
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1answer
43 views

VAR Model- Impulse Response Function Y-axis

I read different papers which try to analyse the relationship between oil and macroeconomics with the help of a VAR model. The results are explained in graphics which show the impulse response ...
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17 views

Bayesian VAR, IRFs and unit roots

I estimated VAR using Bayesian inference. Then I calculated roots of the characteristic function of this VAR. The biggest root was greater than one. Also I tried to make all series stationary before ...
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2answers
67 views

What is the best model for time series data with independent and dependent variables

I have two different variables across a time series over a couple thousand time steps. I want to predict the values of the dependent variable (y) based values of the independent variable (x) in the ...
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14 views

Augmented Dickey-Fuller test with time trend term

I have time series variable X, Y and Z. I need to analyse these variables by using a VAR model. Before running a regression I did augmented Dickey-Fuller test and found that one variable is stationary ...
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1answer
48 views

Lag length selection for a VAR model

The model I am working on has 4 time series (X, X1, X2, X3). Lag lengths are 5, 1, 4 and 6, respectively. X1, X2 and X3 are stationary at level and X is stationary at second difference. I am applying ...
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34 views

Inverting a VAR - looking for a good resource

I am having trouble with something that should be pretty basic. I need to invert a VAR (vector autoregression). Everything I have read just brushes past the actual inversion process, taking for ...
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50 views

How to explain to laypeople that in a VAR model some variable explaines its own variance?

Background: I observed that people not familiar with vector autoregressive (VAR) models often struggle with the interpretation of a forecast error variance decomposition. I am frequently asked, why a ...
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1answer
69 views

Interpretation of VAR and causality

I have two time series(X1 and X2) each having 900 records. I wanted to establish relationship between them and put it in ...
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1answer
32 views

Jarque-Bera test mandatory for VECM and VAR?

What tests do I need to perform for VECM and VAR to be considered robust? I know LM test for residual autocorrelation is mandatory, but what about Jarque-Bera test? Is that necessary?And what should I ...
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1answer
110 views

Time series Analysis - VAR or VECM

I have 4 time series. One of them is stationary and rest of them are not. I need to find relation between them. I will use AIC to decide lag length. Should I use VAR or VECM to find relation between ...
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20 views

Contemporaneous regression in R (VAR model)

I have two time series and I want to check the relation between them. I am using a VAR(3) model. I would also like to include the contemporary variable, something like this: Can we do this ...
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67 views

VECM model output - where is the long run relationship?

So I'm getting the following EViews output, but where on earth is the long run relationship? Do I have to estimate it separately using OLS? If you have to estimate it yourself via OLS, I've already ...
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26 views

lag number in VAR

I am trying to determine the optimal lag number in 2-equation VAR as follows: 1. choose lag based on information criteria 2. estimate the model using # of lags determined above and test for ...
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55 views

Vector autoregressive model selection process and relationship with cointegration

Let's say you're looking at two securities that trade closely with one another and you suspect you can somehow trade the spread. How can you use VAR models to estimate the relationship between the ...
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51 views

orthogonalized impulse response's contradictory forms in a VAR(p) model

I have so far discovered three different ways of utilizing the Cholesky decomposition for calculating the OIRFs of a VAR(k). The different methods seem contradictory so I would like some input on ...
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1answer
59 views

What can be inferred from “covariance matrix of residuals” and “correlation matrix of residuals” after VAR?

I have this VAR: summary(VAR(V6CADModelSt45obs1D.df[,c(5,3,2,6,1,4)], p=5, type="none", ic="SC")) The following is the result of this VAR: ...
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26 views

Out of sample VAR in R [closed]

I am looking to write code in R for out-of-sample forecasting with a VAR model. My data is quarterly from 1985:Q2 to 2013:Q4. I use an initial sample of 1985:Q1 - 1994:Q4 and expanding samples ...
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29 views

Calculating Marginal Data Density for VAR Model

I am currently estimating Bayesian vector autoregressive (BVAR) models and I would like to do model comparison with Bayes factors. I have read about the Gelfand-Day method, the Geweke (1999) modified ...
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2answers
290 views

How to calculate the impulse response function of a VAR(1)? (With example)

How to calculate: 1) Simple IRF 2) Orthological IRF (Y2 -> Y1) Of an unrestricted VAR(1) model: $Y_{1, t} = A_{11}Y_{1, t-1} + A_{12} Y_{2, t-1} + e_{1,t}$ , $Y_{2, t} = A_{21}Y_{1, t-1} + A_{22} ...
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1answer
58 views

How can we determine the sign of Granger causality in a >2 dimensional VAR?

As the title suggests, I'm trying to test for the sign of Granger causality in a large VAR. For exposition, consider the following three-dimensional VAR: \begin{align} \vec y_t=\vec ...
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39 views

Flat impulse response function

I'm doing a VAR at the moment involving 3 variables. CPI, interest rate and unemployment. Im getting strange results for my orthogonal impulse response function in that all of the impulses on ...
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1answer
112 views

How many lags should I include in the VAR-model

When building a VAR-model with six variables I had the following situation: after building a VAR(1) the overall portmanteau test says that the residuals are ok (p=0.85, p_adjusted=0.22). But when I ...
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44 views

SVAR with Taylor Rule

I am trying to replicate this paper (Stock, James H., and Mark W. Watson. 2001. "Vector Autoregressions." Journal of Economic Perspectives, 15(4): 101-115.) I am having trouble with the SVAR. They ...
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101 views

determinstic trend in VAR-models

I'm asking myself the following question. I want to build a VAR-Model with 6 time series A, B, C, D, E and F. I analysed every series univariate and I found out that A, D, E and F are stationary and B ...
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63 views

What is the logic behind using Adstock VS VAR style lag analysis for marketing mix models?

I'd like to discern why the adstock transformation is the default method to introduce lagged influence of prior time points i marketing mix models over a standard linear method as in VAR? I understand ...
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1answer
82 views

VAR Impulse response with dummies

I have dummy variables (DV) which measure policy reforms (e.g. Independence of the judiciary, barriers-to-entry in a market etc.). These can be either “0,1” or, say, “0,1,2,….. upper”. Say I have a ...
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42 views

Forecasting with use of PCA variables as independent and one ternary dependent variable in R

I'm having trouble in taking a direction of my research project. I have independent variables that are commonly used as economic indicators and I want to include variables/indicators that are not ...
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1answer
43 views

Vector Autoregression

I have a general question on VAR-methodology. In the case of asymmetric modelling I employ FGLS to exploit off diagonal covariance between residuals due to non-unique regressors between equations. Ok, ...
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1answer
35 views

Identities in a VAR model

I am working on a VAR model where one of the equations is an identity. For example: $$ \begin{cases} A_t = \alpha_{11} + \alpha_{12} A_{t-1} + \alpha_{13} B_{t-1} + \alpha_{14} C_t + ...
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19 views

Help forming a VAR model

Can anyone help me for a very basic VAR model for regressing Inflation (CPI first difference) on energy prices and money supply. any suggestions be appreciated.
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21 views

VARMA with t-student innovations

I'm wondering if there is a possibility to estimate VARMA model with t-student innovations in R. I found package MTS, but all models here seem to be estimated assuming multivariate normal ...
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180 views

Vector Autoregression, how to interpret Impulse Response Function (IRF)

I have an IRF that shows the GDP shock to GDP. Let's say I have a 5-year forecast of GDP. If there is an immediate 1% decrease in GDP today, can I adjust the original 5-year forecast by using the ...
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23 views

VAR model selection for forecasting one variable

Suppose I have a VAR model for variables $x_1$ through $x_K$. I will use the model to forecast $x_1$ a few steps ahead and will do this iteratively rather than directly. I am not interested in ...
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1answer
94 views

Forecasting with a VAR estimated by GLS versus OLS

Suppose I have a VAR model with different regressors in different equations (this could be due to restricting some coefficients of a full VAR($p$) model to zero or having some different exogenous ...
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1answer
117 views

Estimating VAR by GLS versus OLS: efficiency

Suppose I have a VAR model with different regressors in different equations (this could be due to restricting some coefficients of a full VAR($p$) model to zero or having some different exogenous ...
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0answers
18 views

Why do orthogonal complements come into play in the Granger representation?

Consider the Granger representation of a VAR model. (See : here). Can anyone explain me how in this representation Equation 1, page 4 the orthogonal complements of $\alpha$ and $\beta$ come into ...
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1answer
71 views

How do you simulate two correlated AR(p) time series?

I would be interested in the mathematical framework plus code in R if possible. Basically I want to find out the parameters of the two AR(p) models if I already specificed a certain cross-correlation ...
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27 views

Connection between discrete VAR(1) model and simple discrete Markov Chain

I have studied both Markov chains and Vector Autoregressive Models, and I am interested in the connections between the following models: Markov Chain: $$X_{t+1}=T*X_{t}$$ Where X is a vector ...
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119 views

lag length selection in Vector Error Correction Models

I am doing a VECM analysis in R using vars R package. My problem is to find the lag length of the VECM model to be specified. I a previous post I was suggested to use the VARSelect function. However I ...