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Questions tagged [var]

Vector Auto-Regression, 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 status.

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9 views

What's the advantage of Toda-Yamamoto's Granger Causality procedure?

There are already quite a few questions here on the Toda-Yamamoto (TY) approach to Granger Causality, i.e., the blog posts by Dave Giles (2011 and 2014). What I would like to clarify is just whether I ...
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Optimal lag-selection in VAR-model in R

Having troubles with the lag specification of a VAR-model. The purpose of the model is to measure orthogonal impulse/response function of oil price shocks on macroeconomic variables, such as GDP-...
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25 views

Simulate cointegrated prices and VAR model [on hold]

I am trying to simulate cointegrated stock prices and use a VAR Model to make forecasts. This is the code I wrote so far: ...
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bootstrapping standard errors for a VAR model

I am using resampling with replacement from the residuals are a VAR model in order to estimate standard errors of the parameters of the model. The distribution of the parameters for the model do not ...
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18 views

VECM: Normalized cointegrated vectors

I try to understand the cointegration vector of VECM by the example below: a Johansen cointegration test of three variables. The test results indicate there are 2 cointegration relationship (r<=2)...
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1answer
20 views

How to analyse an impulse response function with more than 2 variables?

I am running an impulse response function in R, using the package vars. My data has 3 variables, the inflation (Brazilian CPI, or IPCA), the exchange rate and the output gap. My goal is to calculate ...
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How does one interpret an element of the “transfer matrix” used to calculate frequency domain granger causality (via VAR models)? [migrated]

I am attempting to gain a better mathematical understanding for how autoregressive models can be used to infer frequency-domain granger causality. All freq. domain measures of causality that utilize ...
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27 views

Impulse Response Functions R: Transitory Shocks for Non-Stationary Data

I am working on generating Impulse Response Functions via the VECM and VAR models, an hence have data that is non-stationary in levels, stationary in first differences and cointegrated. My IRFs ...
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1answer
18 views

VECM with first differences? [closed]

Is it ok to take first differences of data which is non stationary in levels but stationary in first differences (and cointegrated), and input these differenced variables into the VECM? Or does this ...
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1answer
37 views

VAR for Non-Stationary Data? [duplicate]

I have property return variables and economic variables in natural log form, which are non-stationary in level and stationary in first differences, but are not cointegrated. To my understanding, this ...
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1answer
41 views

Levels or First Differences, VECM or VAR for Ultimate Impulse Response Functions?

My final goal is to generate Impulse Response Functions in R. I have variables that are non stationary when I set k = 5 in a Unit Root test, and they are cointegrated which to my understanding ...
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47 views

Examining Cointegration Before or After Deseasonalizing

I am trying to build a VAR model with two time series, one of which is I(0) and one of which is I(1). I originally tested these for cointegration with a Johansen Test and found them cointegrated with ...
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32 views

Impulse responses - Mean, Median or Point estimate?

Im thinking about what is the most reasonable way to plot impulse responses in a simple OLS VAR model independent of the identification strategy. ${Y}_t = A_1{Y}_{t-1}+ U_t$ I have learned that the ...
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19 views

Forecast interval for VAR model predictions

I have been looking at VAR models for doing multivariate time series analysis. What would one need to do to also estimate forecast intervals for the predictions from a VAR model?
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7 views

orthogonal Impulse response function for VAR model

As a beginner, If I have a VAR(1) model with only two variables (means system of two equations) how can we estimate orthogonal impulse reponse function step-by-step. Is there any article or document ...
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10 views

Does forecast error variance decomposition in which the response variable predominately explains itself imply the model is incorrectly specified?

So I have set up a six variable VAR model in the hope of explain natural gas prices and performed forecast error variance decomposition, however the response variable (natural gas prices) explains ...
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Determine if Impulse responses are different

I have several VAR models which give me Impulse responses. I used a simple wild Bootstrap algorithm to estimate confidence intervals. Now I´m thinking about how to compare these Impulse responses. Of ...
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39 views

How do I forecast quarterly public expenses based on annual budgets and potentially other variables?

I have some time series data from 2008 and forward (see below) on quarterly public expenses and annual public budgets. I would like to forecast the last two quarters of 2018 as precisely as possible, ...
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22 views

VECM in cointegration analysis: How to chose the form of the deterministic terms from dataplots

Please have a look at the plot of 5 different time series $Y_t=(y_{1,t},y_{2,t},y_{3,t},y_{4,t},y_{5,t})$. I want to use R's urca package to perform cointegration ...
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1answer
69 views

How to interpret the the results of augmented Dickey-Fuller test to make conclusions about the order of integration

I am following Pfaff 2011 chapter 3 and 5.1 to find the order of integration of a time series $y_t$ by augmented Dickey-Fuller (ADF). Basically what we do here is testing whether $y$ has a unit root ...
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(VAR/VECM) Difference between fitted value and predicted value using in-sample data

My understanding of the mechanism of generating fitted values of VAR or VECM is much like lm() (perhaps since VAR/VECM use linear regression to estimate coefficients?), where the data is just used to ...
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30 views

How to estimate a SVAR model with contemporaneous restrictions that are different to the lagged restrictions

I am trying to do an SVAR model from the paper "What drives natural gas prices - an SVAR approach" (link below) which has different constraints for the lagged variables (The $A^{*}_{i}$ matrices) than ...
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19 views

How to obtain Covariance-variance matrix for VECM

I am trying to conduct a causality analysis with a VECM, and I am looking for ways to extract the Covariance-Variance matrix and the correlation matrix from the fitted VECM using the already existing ...
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19 views

VAR obtained from vec2var() and and regular VAR giving different IRF and OIRF

I am currently trying to generate the orthogonal impulse response functions (OIRF) of a VECM with two variables. Both variables are I(1) and there is definitely a cointegration at all levels as tested ...
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21 views

Unstable panel VAR / PVAR

Quick question on PVARs. I am using Stata's user-written pvar package. After running the unit root tests using xtunitroot, I ...
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1answer
26 views

Stationary VAR( 1) process : complex eigenvalues

For a stationary Vector autoregressive process of order 1, eigenvalues of A should be smaller than one. However, I am getting some eigenvalues as a complex number after the estimation. however, the ...
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20 views

Impulse Responses Generation in Vector AutoRegression in EViews

I have all the positive time series data of 6 variables. On these six variables, I have applied VAR model and want to generate impulse response functions. But impulse response functions are not ...
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VECM: Why does Speed of adjustment/Error Correction Term have to be a matrix?

From what I understand, Loading matrix, or alpha, is the same as Error Correction Matrix, which also refers to the speed of adjustment. However, if the speed of adjustment measures how many percentage ...
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38 views

Level VAR stability for VECM estimation

I am trying to estimate VECM model for I(1) and cointegrated data. First, I try to find the optimal number of lags by using VARselect by following the below steps: level data is given to VARselect, ...
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14 views

Granger Causality: Sum of errors vs. determinant

I have been measuring Granger Causality between pairs of vectors processes (i.e. 2 vectors consisting of multiple time-series variables). Most of the equations I find in references utilize a ...
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24 views

How to apply VAR model for a I(1) and other I(0) variable? Objective is to forecast [duplicate]

I am modeling liquidity variable, real money growth with real asset price returns. The former is I(1) and the latter is I(0). The objective is to see the predictive power and forecast. However, can we ...
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36 views

How to interpret impulse response analysis in VAR when using standardized variables?

How to interpret impulse response analysis when using standardized variables (ie., subtracting the mean and divide by standard deviation) in vector autoregression analysis? The reason why I ...
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22 views

How to choose drivers for forecasts based on vector autoregression

as mentioned in the title my question is how to choose from a large set of time series the best Driver for a forecast based on vector autoregression. I am sure that this question is very general. I ...
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Vector Autoregression with Trials

I want to use VAR model on my data. In addition to typical two dimensions (variables, time steps), I have a third dimension, namely the repetitions of the same experiment. Can you recommend a ...
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1answer
28 views

How to generate a VAR(1) model? [closed]

I already written the code but something went wrong and I don't know why... here is the code. ...
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266 views

Open source code for factor-augmented VAR (FAVAR) model

I am looking for an open source package (R, Python, Julia) that has an implemented FAVAR (factor-augmented VAR) class for time-series prediction problem. I've already tried to use several solutions ...
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25 views

Removing Influence of Other Time Series in Multivariate TS Analysis

I have some non-periodic time series that are all correlated. In the absence of the others, each time series would consist of a set of responses to events. I don't know the duration or shape of each ...
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1answer
32 views

Omitting certain time periods in VAR

I am using a vector autoregression with a monthly lag, and wish to not include certain months, as they are outliers in my analysis and may distort findings. Is estimating such a VAR (using OLS, then ...
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1answer
32 views

Can VARMA handle non-linear data?

I know that traditional ARIMA models cannot handle non-linear data but I was unable to find any place where it states whether if VARMA can handle non-linear data or only linear. Please clarify this ...
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28 views

Is there any benefit of using GLS when the regressors are identical

I am reading Greene, Econometric Analysis, 7th Addition, I am seeking a point of clarrification. "The case of identical regressors is quite common [think a VAR mode].... In this special case, ...
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1answer
69 views

Estimating a VAR using OLS vs GLS

I have read in several places that I can estimate a VAR model equation by equation using OLS instead of using GLS, if I have the same explanatory variables. Do I need to make any assumptions about ...
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51 views

Estimating a VAR model via OLS

I am looking at Vilasuso (2001), who says that when using least-squares to estimate causality in mean, there is significant size distortion if the conditional variances are correlated. My question ...
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Heteroscedasticity in VAR Residuals and Least Squares

If I have a VAR model, think of the simple case with two variables $y_1$ and $y_2$, Vilasuso (2001): says that if the conditional variances of $y_1$ and $y_2$ are correlated, significant size ...
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27 views

Orthogonalised Impulse Response Functions in Stata

This might be a really basic question for some of you but I have been looking up how to interpret impulse responses but most of the answers that were presented did not quantify the responses but ...
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59 views

VARX model on non -stationary timeseries

I am going through the lecture notes on VARX by Dr Tsay Pg 11-22 Link Plot of endogenous and exogenous series shows that these are not stationary. Pg 15 shows lag 2 VAR model is fit at level. Not ...
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1answer
98 views

Deriving the Cointegrating Equation in a VECM model

I am teaching myself econometrics and I am having trouble understanding how the cointegrating equation in VECM is derived. Lets say we have two variables, Consumption and Income. As I understand it, ...
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1answer
27 views

Inference in cointegated VAR model

I am estimating the following VAR model: \begin{equation*} x_t = k + A_1 x_{t-1} + A_2 x_{t-2} + \dots + A_p x_{t-p} + \epsilon_t, \end{equation*} where $x_t$ is a vector of variables and notation is ...
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1answer
248 views

AIC, BIC values keep changing with lag.max in VAR model

I'm using a VARselect function from vars package in R to select order for my model. My data set has 2 time series with 21 data points. When I give ...
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42 views

Do vector autoregressive models require stationarity?

Some say yes and some no (note I am ignoring here the issue of cointegration). Say there is no cointegration.