"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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References suggested for multivariate analysis of several similar time series

I have a time series dataset that reports the hourly page views and social media shares of online news stories. What I hope to obtain is the relationship between the two variables. I would imagine ...
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10 views

VAR model and forecasting

I worked on obtaining a stable vector autoregression (VAR) model for my dataset consisting of 3 different dependent variables. Then I tried to forecast each of the variables. As I looked at the ...
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24 views

Interaction in time series analysis

I have three different physiological variables--heart rate, respiratory rate and blood oxygen saturation, each as a time series. I am trying to study the interaction between the variables as they ...
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6 views

Comparing IRFs derived from Bayesian VARs with other extraneous information

I was wondering if anyone here could help me with the following: I estimate a standard Bayesian VAR with Normal-Inverse Wishart priors. I identify some policy shock in it, and then derive the IRF for ...
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15 views

Outlier treatment in Vector Autoregression (VAR) Model using vars package in r

I have the same problem as the following post, but I have more samples and the index of the outlier is known. Outlier treatment in Vector Autoregression (VAR) Model I tried deleting the outliers; ...
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2answers
47 views

How to analyze multiple variable time series - suggest references

I have multiple environmental time series variables (for example: temperature, dissolved oxygen, conductivity, depth) measured every few minutes for several months. The variables are measured at ...
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11 views

Postestimation results after VAR analysis show autocorrelation in residuals

I'm performing a VAR analysis on news effects and S&P500 returns. Now, I specified the number of lags (5) according to Schwarze's Bayesian Information Criterion (SBIC) and ran some postestimation ...
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21 views

Should I log transform my volatility variable?

I'm wondering if my volatility factor is specified correctly. My data consists of log returns on the S&P 500 index, a measure of news sentiment, and a newscount variable (# of articles published ...
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22 views

What is the difference between vector error correction model and UVECM?

What is the difference between vector error correction model and UVECM? Which one is better to use for a 30 years time series data (to study long term and short term relationship) which has some ...
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11 views

Initial Parameters for VARMA models?

I want to estimate parameters of VARMA model using maximum likelihood estimation using real data. The problem I face with is that I don't know how to set the initial values for the parameters. I ...
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32 views

Autocorrelation tests for time series VAR models

I have a VAR model in which I regress the monthly unemployment rate on itself lagged one month, the monthly GDP percent growth lagged by two months and two dummy variables. I am trying to test for the ...
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1answer
27 views

VAR model with zero coefficients

ll, I'm working with a bivariate time series $(X_{t},Y_{t})$. Looking at the two time series separately, $X_{t}$ appears to be white noise. This is supported by looking at the empirical ACF and PACF ...
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58 views

Understanding vec2var conversion in R

I'm using Bernhard Pfaff's packages {urca} and {vars} to analyze 3 time series. Each is I(1) and cointegrated with $r =2$ cointegrating relationships. The vec2var() command should make the ...
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34 views

how to do conditional forecasting with cointegration model?

I'm confused about multistep forecasting from VECM model for 2 cointegrated series. The model is pretty simple, in error-correction form: $$ \Delta x_{t+1} = \alpha_1 (y_t - \beta x_t -\beta_0) ...
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14 views

Construct matrix of stacked variables in VAR regression

I am trying to NOT use packages for the estimation of models in order to have a deeper understanding of how things work. Currently, I am trying to estimate a VAR(1) (vector autoregression of first ...
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47 views

SVAR Model with Short run restrictions

I am currently working on implementing SVAR model in an economic analysis. I have 10 variables in my analysis and currently struggling to incorporate the short run ...
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22 views

Estimating the parameters of a model, which method should I use?

I am trying to estimate a system of macroeconomic (simultaneous) equations, and I've learned about the 'existence' of various methods including Structural Equation Models, Simultaneous Equations ...
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30 views

Long Run VAR alpha and beta significance levels

I am using a VAR with 2 variables and 4 lags. I am combining the coefficients of these variables to get an overall alpha and beta value for in the form $Y = \alpha + \beta X$. In order to get the long ...
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38 views

VAR / VEC in levels or difference depending on Cointegration

Thanks in advance. I have four I(1) variables I'm trying to model by VAR/VEC. I know that it is only okay to model non-stationary variables in levels only if they are cointegrated. What I would ...
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43 views

Cointegration difficulties using Stata

I am working on a time series analysis with 52 quarterly data concerning a variety of possible determinants of CO$_2$ emissions by transport (CO$_2$ taxes, GDP, load factor, transport volume). This ...
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12 views

1-day VaR scaling for higher horizons

I have been reading about VaR scaling in "Market Risk Analysis" by Carol Alexander. It talks about square root of time (horizon) scaling is valid only if returns distribution is normal. For stable ...
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33 views

What is the best approach for a set of data that is irregular and uneven

I have a dataset with 975 observations from 112 different categories. The timespan of this dataset is 18 years. However, the data is unevenly spaced and even acquired: While some categories have only ...
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80 views

Alternating signs of significant estimates in VAR model

I have 6 variables and 125 observations, which I am modelling using a VAR model, in which I put all variables in as edogenous, as all relationships interest me (the bidirectionality). I have carried ...
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1answer
209 views

Significance of an impulse response function

I've read several paper that all compare different cumulative IRF of the same VAR equation for statistically significant difference. The IRF they use are simply the sum of the coefficients of the VMA ...
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1answer
332 views

Optimal lag length in VECM using vars R package

I have some series that are cointegrated, so I know that I should fit a VECM model. Nevertheless I found no guidance in finding the optimal lag length, say lagLength. I am using vars R package. ...
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56 views

Computing a multi-sample (i.e., pooled) Akaike Information Criterion

I have a set of multivariate time series observations that I am trying to model using VAR processes, using AIC to choose the best model. However, instead of determining the best model order for each ...
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102 views

VAR model with time series of different frequencies

In case I want to see the effect of two or more endogenous time series on each other, I use a VAR model. But how do I proceed if one data set is monthly, and the other one daily?
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1answer
86 views

Examples of time series models on other than economic data

All our text book examples are based on macro economic problems, but there must be many applications of time series models on other data, such as for example windspeed, average heartbeat, gas turbine ...
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1answer
338 views

Normalizing to zero mean and unit variance before regression

I'm new to regression (vector autoregression), and recently encountered the following issue: If I use raw dependent and independent variables to do the regression, the $R^2$, DW-d test and standard ...
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1answer
99 views

Nearly constant time series

I want to analyse temporal interactions of some time series by means of the Box-Jenkins approach to find out which time series are predictors of another one (with the help of prewhitening and ...
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140 views

Vector autoregression with exogenous variables

Im dealing with a VAR model where I also want to include exogenous variables. Based on my sampling, the exogenous variables in $t$ are independent from my other variables in $t$, but highly dependent ...
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3answers
228 views

Stationarity in multivariate time series

I am working with a multivariate time series and using VAR (Vector Autoregression) model for forecasting. My question is What does stationarity actually means in a multivariate framework. 1) I know ...
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70 views

Sum of lagged coefficients (VAR)

I have come upon a paper where a VAR analysis is performed. Using 3 endogenous variables and some exogenous (control) variables, the results of the VAR analysis are shown in a table. For the ...
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3answers
2k views

What are disadvantages of state-space models and Kalman Filter for time-series modelling?

Given all good properties of state-space models and KF, I wonder - what are disadvantages of state-space modelling and using Kalman Filter (or EKF, UKF or particle filter) for estimation? Over let's ...
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64 views

Contemporaneous effect in VAR / VEC models

Other than Granger causality, I would also like to test contemporaneous effects. So with a VAR (vector autoregression) model I use a simple t-test to test if the cross correlation of the level ...
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51 views

Implementing a vector autoregressive model where only one variable is of interest

I have a dataset of 100 different time series and I am trying to forecast only one of them. However I think that the 99 other time series influence the one that I am interested in so I use a VAR ...
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67 views

Estimating a VAR model with variable coefficients

I want to estimate a VAR model based on the Dufour and Engle paper "Time and the Price Impact of a Trade" (2000). There, the parameter $ b_{i} $ of the endogenous variable $ x_{i} $ is dependent on ...
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1answer
93 views

Diagnostic for VAR model. non normal

I have some problem about my model. my model is based on VAR. (vector auto-.) well, I've tested ARCH test, BG test(autocorrelation p) and jarque.bera.test. Model is stable. Also I got good result for ...
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41 views

VEC model with lags restrictions

I am trying to estimate a VEC model imposing zero restrictions manually as in the restrict() function for a VAR model. I do not know how to introduce different lags (for example lags 1 to 3 and lag 7) ...
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26 views

Test if a variable is a good predictor of a transition to a state

I have a dataset of the wealth of 10 different countries in the world since 1800, one data point per year. Let's say I have noticed that when the wealth of a country goes above $1,000,000, this ...
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83 views

Multivariate Data into VAR model in the vars package

I have been trying to use the vars package in r for my multivariate time series data. I am having some problems. Do I have to do something to my multivariate data before using the vars package? Can ...
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146 views

Non Stationary series, VAR Model

Hi I am working with a multivariate time series data consisting of 1) Demand Data 2)Sales Data 3) Rainfall Data , all available from 2010-2013,at monthly level. Approach: I am trying to estimate the ...
1
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1answer
148 views

Outlier treatment in Vector Autoregression (VAR) Model

Data: Multivariate Time Series, Series 1) Demand of a product 2) Rainfall data both available at monthly level from 2010-2013. Approach: I am trying to estimate the effect of rainfall on demand of ...
3
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1answer
583 views

How to estimate vector autoregression & impulse response function with panel data

I am working on vector auto-regression (VARs) and impulse response function (IRFs) estimation based on panel data with 33 individuals over 77 quarters. How should this type of situation be analyzed? ...
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75 views

Can I get an univariate ARMA(2,1) representation from a bivariate VAR process?

Suppose the VAR is on (x,y) and I want to get an ARMA(2,1) expresion for x, how can i do that? For example, $\left[ \begin{array}{l} x_t\\ y_t \end{array} \right] = \left[ \begin{array}{l} ...
3
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1answer
98 views

Estimate a model by minimising the sum of the one-, two, … and h-step ahead forecasts?

When fitting (stationary) time series models, such as ARIMA models, the standard approach is to minimise the one-step ahead forecasting error, which is equivalent to performing maximum likelihood ...
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265 views

How to make panel VAR analysis in Eviews?

I want to perform panel VAR analysis in Eviews but I am not sure which is the correct option as there isn't any built in option in the software. Could you please advice what are the exact steps for ...
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171 views

Time-varying VAR model using Kalman filter and then impulse response function

I wish to build a Time-Varying VAR model in state space form using Kalman filter and then I want to build impulse response function for the model. Can anybody guide me any clear example with codes. I ...
1
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1answer
147 views

Fit VAR model with unknown order in Matlab

I have a multivariate observed time series $Y_t$ and I want to find the best fitting VAR process for it. I have the econometric toolbox in Matlab and can use 'vgxvarx' if I pre-specify an order for ...
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198 views

Panel VAR analysis

I am need to perform panel VAR analysis in eviews and so far I did the Panel Unit root test and the Granger causality test. I am not sure how exactly to proceed as in Eviews there is no built in ...