Questions tagged [vector-autoregression]

Vector Auto-Regression, a multivariate time-series model / method. Under VAR, each univariate time-series is a linear combination of its own previous values and the previous values of the other series.

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How to interpret the IRF Graph generated by the vars::irf( ) function

My question is: What is the unit used in the Y axis of an IRF graph? Or, in other words, how to interpret an IRF graph from a VAR (vector auto-regressive) model? I estimated a VAR model in R, through ...
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Should I scale my dataset before applyting VAR?

I need to implement a VAR (vector autoregressive) model using a dataset of multiple financial indices. The dataset is unscaled. Should I scale it first or the VAR is not sensitive to scaling? In case ...
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Difference between Granger causality and VAR(1)?

For my VAR(1) I get that the causal variable in each equation is statistically significant at 10%. But for Granger causality at 10% I only get that 1 variable granger causes the other and not the ...
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VAR restrictions on Exogenous Variables

Technically it should be possible to restrict the coefficient matrix of the exogenous variables by setting the desired restriction = 0, am I right there? If so, does anyone know how to implement that ...
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Vec operator and covariance matrix

You have a matrix containing $T$ observations of each of $K$ random variables \begin{align} U = \begin{bmatrix} u_{11} & \dots & u_{1T} \\ \vdots & \ddots & \vdots ...
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Interpreting Impulse Response Function

I am trying to estimate a forward guidance shock on the expected path of the future federal funds rates and industrial production of manufacturing and construction. I built my SVAR model using Smith ...
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Error message in R when applying irf function to a SVAR-model [closed]

I estimated a structural autoregressive model with the following R commands: var=VAR(dat, p=2) A=matrix(c(1,0,NA,1),byrow=TRUE,nrow=2) B=matrix(c(NA,0,0,NA),byrow=TRUE,nrow=2) svar=SVAR(x, Amat = A, ...
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Vector autoregression with p = 2, lagged coefficients seem to “cancel out” (one is positive, one is negative with ~equal magnitude)

I'm analyzing some time series data with vector autoregression (AKA VAR). I'm using the implementation in R's vars package, if that matters, but I believe it is ...
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Granger causality test on residuals

I have a time-series of three variables: $x_0, x_1, x_2$. I have a theory that claims $x_0$ granger causes both $x_1$ and $x_2$. On the other side I want to check if $x_1$ causes $x_2$, $x_2$ causes $...
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Insignificant impulse response functions

Anyone with time series analysis experience? I am building a VAR model with 6 endogenous variables and 3500 observations for each variable (I have used daily frequencies in my time series model). I ...
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Limitations of VAR Model

I am trying to find out more about the limitations of using the linear regression model VAR() for finding granger causality between position data (CFTC) and price data. Mostly the strong assumptions ...
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R forecasting: fable package, Multivariate decompostion model

I am using fable and fabletool package and I really like the new workflow structure. It is easy to understand and apply in many different forecast scenarios. I am now trying Vector auto regression ...
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Choosing the right lag order for the LjungBox() test in VAR()

I have several VAR Models with mostly AIC as lag criterion. For the Ljung-Box test I read about the rule of thumb of choosing h=min(10,T/5) with T=number ...
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How do I transform first-differenced impulse response functions back into levels?

I am estimating a structural VAR where all my variables are I(1). I took the log differences of each variable and generated the impulse response functions. Is there a way to convert the impulse ...
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Granger causality in non stationary VAR(1) process

I have VAR(1) process which is not stationary (roots are outside of the unit circle). If I deemed it to be stationary, what can I say about Granger causality?
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VAR() or dynlm() or lm()

Can anybody tell me something about the difference between the functions VAR() and dynlm()? I thought I could do my VAR with OLS ...
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VAR(p) Model in R with HAC estimator

I'm running a VAR model in R and found with several tests (arch.test, serial.test) that my model still contains ...
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VAR(p) Model with difflog data

I'm doing a regression analysis with a VAR model in R. My data is price and position data in the commodity market, since i want to find out (Granger) causality between them. I took the difflog for ...
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VAR or VECM for I(0) e I(1)

I have 5 series, four nonstationary series and one stationary series.I tested each pair using Engle-Granger (x1,x2),(x1,x3),(x1,x4); (x2,x3) (x2, x4); (x3,x4) and found cointegration only in x2->x4,...
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Modelling VAR or VECM [duplicate]

I have 5 series, I did ADF on my series and found that 3 series have unit roots(x1, x2, and x3) and 2 don't(x4 and x5). Still using the level variables I did Engle e Granger test and Johansen test( ...
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Maximum Likelihood Estimation of a VAR(p) Process

In Lütkepohl's New Introduction to Multiple Time Series Analysis Chapter 3, we try to estimate the model parameters via Maximum Likelihood Estimation. We assume that $u=(u_1',\dots,u_T')'\sim \mathcal{...
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EViews: VAR dynamic forecasting data

In Eviews, I tried VAR out-of-sample dynamic forecast. But after doing forecast process, there are all same data left. For example, I used 1998q1~2008q4 GDP growth rate, exchange rate, etc... to ...
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VAR (Vector Auto Regression) with exogenous variables

Is it possible to set seasonality as dummy variables in a VAR model? Which I use those dummies as my exogenous variable?. Is it also advisable to input other variables that are a good measure of my ...
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Deriving counterfactuals for an SVAR model within the vars package

I have the following function from the svars package, which generates counterfactuals for variables within a Structural VAR model. I'm hoping to find a solution ...
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Visualizing the results of Granger causality test

I have tested for Granger causality in my data and have the $p$-value related information such as, $Granger Granger causality H0: general do not Granger-cause special data: VAR object sims.2 F-Test =...
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Granger Causality doesn't agree with VAR

I have 6 econometric price series (2 of which are of particular interest here - let's call them X1 and X2), for which I have tried to run the Granger Causality tests and cross-checked it with VAR. ...
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Intuition of Condition of (weak) Stationarity in multivariate case

This may be a duplicate. I am trying to get an intuitive idea about the condition for stationarity. I think I got a fair idea of stationarity as a concept (from this and other sources on internet) but ...
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Seasonality and ECM/VECM - Correct seasonality before estimating VECM

I know that for univariate framework, a typical process to deal with seasonality is : detect correct (for instance, withdraw seasonal factors) forecast re-seasonalize the forecasted series (for ...
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an alternative to GMM?

Dear StackExchange Users, I am currently working on my PhD thesis and one of the chapter deals endogeneity in the data. I have N the number of individual (here country) = 11 and T the time dimension = ...
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How should interpret the irf derived from a VAR in differences?

For example, I have two series: price and sales and they are non-stationary. I took the first difference of them to make them stationary. I then use the differenced sales and price to set up a VAR(p) ...
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Can I use breaking series as a regressor in a VAR model?

Context: I have two series, price and sales. Sales is mean-reverting stationary but price is stationary only after controlling for an intercept break. I want to set up a 2-equation VAR model and the ...
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SVAR , impulse responce and size effect

Sir I want to test system equations with simultaneous effect , where I have peer group firms having spillovers from peers to firm and firm to peerfirms simultaneously. Let say , I want to test ...
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Correlation matrix from VAR(1) model

I have implemented a simple VAR(1) model with gaussian noise and no bias to generate two dimensional data. When computinng the empirical covariance matrice (for lag 0) of of this signal, it is always ...
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VAR model variable selection

I'm required to use two time series models in my exam project. I want to use a stock price of an energy company, and then explain it first using ARIMA, and then adding other variables and using VAR. ...
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Heterogeneous panel Granger causality for cointegrated data based on VECM?

I am currently doing a project on the link between transportation and economic growth. Particularly, am interested in the potential heterogeneoua nature of causality relations across regions. However, ...
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How do you interpret the instantaneous causality test? [duplicate]

I have used the causality function to test a VAR(3)-model for Granger causality. For one variable the following results were computed: The test found that H0 could ...
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How to assess the impact of an exogenous on endogenous variables in VAR

I fitted a VAR model that includes an exogenous variable, and I am interested in assessing the impact of the exogenous variable on the endogenous variable. As far as I know, IRFs (impulse response ...
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Granger causality test result depends on whether boot is used or not. Which result should one take?

I estimated a VAR model and used the Granger-causality test, to interpret the model. I used the causality function to check for possible Granger-causality, but I ...
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How to formally write a VAR model including an exogenous variable?

I fitted a Vector Auto-Regressive models of order 2 VAR(2) with two variables "V1" and "V2" plus an exogenous one "EX". How can I formally write the equations of the ...
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Can I interpret the signs of coefficients in a VAR model?

I estimated a VAR-model. I checked the time series for stationarity and after estimating the model the residuals and all is fine. I know that it makes no sense to directly interpret the coefficients ...
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Choosing Cross-Sections for Panel Data

I'd like to ask, how do I pick how many and which cross-sections to use for Panel Data analysis? I want to look for cointegration among some variables and run panel VAR/VECM on annual data from 1990-...
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how to understand the significance of dynamic multiplier?

In below figure, it is written that the results of the dynamic multiplier are significant. How would we reach that kind of result by looking at dm tables?
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Causal assumptions behind SVAR with sign-restrictions identification

Which causal assumptions are (explicitly or implicitly) made in identification of a structural vector autoregression (SVAR) model with sign-restrictions (see e.g. Uhlig 2005)? By “causal assumption” ...
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How to treat data for Bayesian VARs

I am working on a project where I need to implement a time varying structural VAR. From my understanding this uses the Kalman filter and is basically a Bayesian tool. From frequentist time series ...
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Time series I(0) and I(1) - how do I use it in a model?

I am analyzing two time series with the goal to run a VAR-Model. Now I ran a KPSS-test to test if the time series were stationary. The result is now that one time series is I(0) while the other one is ...
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Time series I(0) and I(1) - how to model it?

I am analyzing two time series with the goal to run a VAR-Model. Now I ran a KPSS-test to test if the time series were stationary. The result is now that one time series is I(0) while the other one is ...
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VAR or VECM Model?

If I have 4 variables in a model and I want to check for causality between the two variables say X and Y. These two variables are not cointegrated based on Engle-Granger Residual test for ...
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Wold's decomposition and Gaussian distribution in infinite dimensional Hilbert space

It is well known that the Wold's decomposition allows that every covariance-stationary time series $ Y_{{t}}$ can be written as the sum of two time series one deterministic $\eta _{t}$ and one ...
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Understanding the results of VARX and solving issue with VARXpred using MTS library in R

Here are the first 4 lines of my data Resource Pas-Climate-Flow.csv: My dependent variable is Flow (it’s binomial but I could ...
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How to Approach VAR with Stationary Variables

I've got 3 variables and all of them seems to be non-stationary but when I take first differences they are stationary. My aim is to create a VAR with three of them and I'm not sure if I put them as ...

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