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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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Approximation in Impulse response calulation

I am dealing with the calculation of the impulse response functions in a VAR Model and I'm not sure I got it right. What I understand: The orthogonal Impulse Response function is a $MA(\infty)$ ...
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what does it mean to run a time series model in levels?

I have seen the phrases running a var in levels and running a var in difference in very high frequency, either on this site or elsewhere. I understand running a var in difference basically means to ...
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Why do we need a VECM specification if the I(1) processes are cointegrated?

I happened to question the rationale of employing VECM, since some empirical studies as in Basu (2017) employed a VAR model to obtain impulse-response analysis. As far as I know, one should consider ...
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8 views

VAR order in Cointegration Test

I am studying Johansen's Test using Tsay's book (Multivariate Time Series Analysis). The book has given some sample results of function ca.jo in r package ...
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How can I recover full dimensional VAR model coefficients after fitting a VAR model to a dimensionality reduced (via PCA) dataset?

I am using PCA to reduce dimensionality prior to fitting a multivariate time-series dataset to a VAR (vector autoregressive) model. Is there any way to convert a PCA-derived VAR model to a full ...
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31 views

Are trend-stationary series I(0)?

I have time-series of different interest rates. Graphs of all series show existence of trend. For some of these series ADF-test with constant rejects null hypothesis. For others, null hypothesis is ...
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How to determine the bandwidth parameter? Newey-West

How to determine the bandwidth parameter? Following from the below paragraph is it easy to understand how Newey and West determine the bandwidth? "The heteroskedasticity consistent estimator (HCE) ...
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Simulate stationary VAR(p)

I would like to simulate a stationary VAR(p) coefficient matrix. However, I only found the following (inefficient) solution: Simulate a coefficient matrix (n x n*p) drawing each coefficient from a ...
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29 views

Residual variance-covariance matrix in vector autoregression

It's my understanding that the general form of a variance-covariance matrix has variance terms on the diagonal and covariance terms on the off-diagonal. I have seen in multiple references (for ...
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In Cointegration, adf test or for VAR models. What makes difference in having trend + Constant, Constant alone, and non

In Cointegration, adf test or for VAR models. What makes difference in having trend + Constant, Constant alone, and non. in below link http://www.econ.uiuc.edu/~econ508/R/e-ta8_R.html It said ...
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29 views

How to perform Granger-causality?

I have a question regarding Granger-causality. I want to test if 1) [y2 and y3] do not Granger-cause y1 and test if 2) [y2] does Granger-cause y1. The equation is as follows: y1-3, t = ...
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non-stationary time series for VAR model forecasting

I'm working with a VAR model to do forecast involving two non-stationary time series (quarterly frequency). The literature indicates to verify if there is cointegration and, otherwise, to use the ...
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Selecting lag length for VAR Model. Differences or Levels?

I'm currently testing for optimal VAR lag length using the information criteria. I found that my variables are non-stationary (i.e. they have to be first differenced). When I identify the number of ...
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Panel VAR / Panel VECM

I have an unbalanced panel with N=800 and T=72 (quarterly frequency). After conducting some unit root tests, all reject the null that all panels are not stationary (the alternative is that at least ...
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Generating multivariate heteroskedastic data

I am trying to estimate a VAR model with heteroskedastic error terms. $e_{it}$ is given by $η_{i,t} √h_{ii,t}$, where $η_{i,t}$ is iid, N(0,1). I am trying to get $e_{it}$ Does anyone have any ...
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Correcting for ARCH effect in VAR and impulse response results

I find significant ARCH effect in my series when running a VAR analysis $Y_t=(y_{1,t};y_{2,t};y_{3,t};y_{4,t};y_{5,t})^\top$ I have two questions: Does the ARCH effect impact the impulse response ...
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Importing and transforming new time series quarterly data into Stata

Date Crude Oil Production (Thousands barrels/day) Economic Activity 1980 Jan 62348.011 34.913651 Data runs up to ...
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(multiple) fractional outcomes & autoregression

Let me start with a broad description of the problem and I will then describe my approach (that might be totally inappropiate). The big goal is to predict the distribution of population of a given age ...
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If I recover VAR model coefficients from PCA-derived coefficients, do I need to ensure that the model has zero cross-correlation in the residuals?

I am investigating how to appropriately combine PCA with VAR modeling. I am using PCA to reduce the number of vars I fit to a VAR model, and am attempting to recover the non-dim. reduced coefficients ...
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36 views

Implement VAR model in R with HAC corrected standard errors [closed]

I have fitted a VAR model in R (with function VAR) and would like to use HAC corrected standard errors. How is that possible?
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1answer
102 views

Interpretation of the Impulse Response Function - VAR Estimation

I have some issues while discussing and interpreting this impulse response function (the graphics analysis). What do they mean and represent economically? What can the conclusions be? Basically ...
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35 views

How would PCA run on multivariate time-series data affect phase relationships across variables?

I am running PCA on a multivariate time-series dataset using observations across time (i.e. w/out time as an explicit variable) as the design matrix. Given this setup, I've found that it is difficult ...
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34 views

Jarque-Bera Test for Normal Distrbances in a VAR

I did the test of he null hypothesis of Normal disturbances and found that it is rejected for Dlop and Dunp. Does this mean that I have a problem with my model specification? Or how can I rectify this ...
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38 views

Granger Causality Interpretation using Stata

I am trying to characterise temporal sequence of influences in a VAR and wanted to use the Granger Causality. Based on these results, am I right to say that the change in oil prices (Dlop) do not ...
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19 views

Cross Correlation

Given that my variables exhibit non-stationary (i.e lrgdp and lop) and I intend on estimating a VAR model, would it make sense to correlate them in their first differences instead (dlrgdp and dlop) ...
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VAR Model using Stata

I'm relatively new to the VAR model and have been using Sean Becketti's 'Introduction to Time Series Using State' as reference and wanted to check if I am on the right track. As of now, I have 5 ...
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VAR, test for normality, autocorrelation and heteroskedasticity- should I use stationary first differences for these tests?

I am checking thhe long-term relationship between unemployment and labor force participation rate. I have a integration order I(1) and I want to run VAR. As far as I understand I need to use first ...
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Rescaling Linear Impulse Response Functions, Innovations, and Confidence Bands

all. I am using a VAR model to do a bit of analysis. I obtain cumulative (linear) orthogonalized impulse response functions (COIRF). Because I am conducting similar analysis across different time ...
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31 views

Can VAR models capture all frequency interactions reliably and to the same extent?

I would like to assess how a set of ~50-100 time-series variables causally interact with each other. To do this, I am fitting a VAR model and using cross-validation to estimate model-order. After the ...
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42 views

VAR with stationary and non-stationary in R

I'm working on using Granger causality of some variables and I have 4 stationary time series (X1, X2, X3, X4) and one that is not (X5). I've seen here that If (A) then first-difference each of the ...
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19 views

VAR/VEC models: checking stationarity during cross-validation

I am attempting to derive a single multivariate/vector autoregressive (VAR) model from a large dataset (6-minutes sampled at 250Hz in total w/ 50 vars) using cross-validation (CV) to optimize model-...
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26 views

Heteroscedasticity and non-normaility in VAR(3) model

After specifying my VAR model, I run several diagnostic tests. While the serial.test indicates that there is no Autocorrelation, ...
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1answer
181 views

Jarque-Bera test for Normality

Which test should I consider if by JB-test result I have heteroscedasticity and by the result of two others no. $JB JB-Test (multivariate) data: Residuals of ...
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59 views

VAR estimation-How to interpret the results?

I have these results when I estimate a VAR with two variables:Growth and Debt and p=2.How to interpret the result for each equation? Thank you. VAR Estimation Results: ...
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1answer
48 views

VAR model for a stationary and a nonstationary series

I have a stationary and a non stationary time series. For estimating a VAR model, both time series should be differenced or only the second?
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Why would I regress a vector on itself in a VAR?

I'm just working through an econometric paper by Bernanke / Blinder http://drphilipshaw.com/Protected/The%20Federal%20Funds%20Rate%20and%20the%20Channels%20of%20Monetary%20Transmission.pdf where in ...
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Estimating lag order in Granger causality test

I have a weekly revenue from selling products, named Chicken and Egg. I am trying to understand whether purchasing Chicken Granger-causes customers to buy Egg or vice versa. I don't have a Ph.D. in ...
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Simulating size distrotion with a VAR(1) model

Firstly, I am not asking for code, I would like the intution of how I would do this. I am testing for size distortion. I have estimated and VAR(1) model and I have the parameters. I want to ...
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1answer
52 views

Impulse Responses with 68% confidence interval

In my field some papers are published with questionable econometric methodology. The impulse responses are presented with 68% confidence bands only and the conclusion is that the effect is ...
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1answer
38 views

How to perform 1 step ahead forecasts with a VAR function [closed]

Say I am given the parameters of a VAR (2) function with two variables. How would I use this information to perform a 1 step ahead forecast? Example of what I would have is... $A_t= 1.5 - 0.5A_{t-1} ...
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118 views

Impulse response: Interpreting shock and response for log-variables

I have a question related to the interpretation of Impulse Response Function (IRF) functions. Assume we do have two time-series that have been both log-transformed and are stationary. When applying a ...
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Using a bi- variate Vector Auto-regressive model on 2 assets

I have two assets in a time series with serial auto correlation. I want to find the relationship between them. That is, if Y1 goes to up 10% Y2 should move up 5%. I would also like to know, if Y1 is ...
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41 views

Fitting a multivariate AR(1) with covariates?

I have time series data where my main question of interest is making inference on predictive covariates, and accounting for the correlation (one observation each day) is just a nuisance issue. The ...
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1answer
41 views

Uncorrelated errors with the regressor in a reduced form VAR

I have a reduced form VAR $$\begin{equation} y_t = c_o + A y_{t-1} + \epsilon_t \end{equation}$$ Where, $y_t \in \mathbb{R}^2$, $A$ is a $2$X$2$ matrix and $$\begin{equation} E(\epsilon_t \...
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Interpreting the parameters of VAR model

If I have 26 parameters (coefficients) estimated in my VAR model, how can I interpret them?Is possible? Or I should consider only the results of IRF and Granger - causality test? Thank you very much.
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How to interprete Granger test results?

The results of my Granger causality test in R are below. VARp is my VAR model and I have two endogenous variables. From the results, I have only instantaneous ...
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36 views

VAR(p) Model Covariances and Moment Equation

I'm currently going through the book Analysis of Financial Time Series by Ruey S. Tsay and reached the following statement (The book can be found here, with VAR(1) included in the preview): Where: $...
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1answer
54 views

Relation between Vector Auto-regressive models and correlation matrix

I am generating a multivariate time series using Vector Autoregressive Models- $$X(t) = AX(t-1) + \epsilon$$ where $X \in R^{n \times 1}$, $A \in R^{n \times n}$ and $\epsilon \in R^{n \times 1}$ is a ...
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Pro and cons on multivariate time series approaches

So I am working on a project where I want to forecast how a team will perform in their next match in a number of specific categories (goals scored, time spent in certain parts of the field, passes, ...