"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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10 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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32 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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0answers
13 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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9 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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16 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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32 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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0answers
7 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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24 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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58 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
95 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
126 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. ...
3
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0answers
45 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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0answers
78 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
82 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
292 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 ...
2
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1answer
80 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 ...
3
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0answers
74 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
164 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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50 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
1k 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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32 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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34 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 ...
2
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0answers
53 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
66 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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0answers
33 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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25 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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71 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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91 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 ...
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1answer
111 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
419 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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0answers
55 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
87 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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0answers
250 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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141 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 ...
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1answer
117 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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187 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 ...
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0answers
58 views

Vector Autoregression with restrictions

I've got a problem when solving a VAR with restricted coefficients and some zero-restrictions on the covariance matrix. For example, I've got a VAR $$ y_t = z + A y_{t-1} + \Sigma \epsilon_t, \quad ...
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0answers
238 views

Normality test and heteroskedasticity test violated in panel data

I have a panel data with 140 observations included from 280 and I want to perform a VAR analysis in Eviews. My data seems to fail the normality and the heteroskedasticity test and I am not sure if ...
2
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0answers
58 views

Asymptotic vs. bootstrap test statistic, size and power properties

I am running a VAR based Granger non-causality tests. I've obtained asymptotic and bootstrap $p$ values for Wald joint test of 0 restriction on a set of lagged variables. It appears that bootstrap ...
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73 views

VAR with dummies, how to analyze dummy effect?

I'm currently trying to perform a VAR model analysis. I have the following variables, all on a one-minute scale, with 900 entries. Put in eViews as endogenous: Y1: Amount of tweets per minute Y2: TV ...
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1answer
713 views

Time series: one I(1) and one I(0) variable, should I use VAR/VEC, test for cointegration?

Like the title says, I've got two time series, one is stationary to begin with and thus has no unit root, the other time serie is stationary after one-time differencing. I want to create a model out ...
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0answers
50 views

asymmetric vector autoregression

When fitting a vector auto-regressive model to a few time series, all the lags up to a certain pre-specified number will be retained in the model. This is true even if the coefficients for those lags ...
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1answer
81 views

Insignificant VAR coefficients

I am not quite familiar with vector autoregression (VAR). I am thinking of using VAR/IRF (impulse response functions) to illustrate the relations between some time series variables. However, most of ...
9
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2answers
269 views

How to model month to month effects in daily time series data?

I have two time series of daily data. One is sign-ups and the other terminations of subscriptions. I'd like to predict the ...
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1answer
37 views

Imputation variance and explained variance (in vector autoregression)

I have a question concerning the coefficients of VAR models used on multiple imputed data (high missigness in some variables: up to 40%). In particular I would like to know how the coefficients are ...
0
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1answer
125 views

Cointegration of a VAR(1) process

I am using a Johansen procedure to test for cointegration a vectorial 4-dim vector (timeserie). First I tested for differential stationarity of each individual vector, all of those have a unit root ...
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0answers
49 views

How to approximate a Stochastic volatility process with Markov Chain

It is easy to use a Markov Chain to approximate an AR(1) process --- Tauchen (1986), Tauchen and Hussey (1991). For a simple stochastic volatility process (discrete), which in it's very basic form is ...
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118 views

Predict using VAR

If I arrive at an equation from VAR model; I know i can use it to predict at some relative period ahead of time by using predict(p1ct, n.ahead = 5 ci = 0.95); I ...
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1answer
126 views

Program Impulse Response Functions for VAR

I'm trying to program impulse response functions for a VAR model using Cholesky decomposition. The thing is I do not completely understand how I should do this when I read in the literature. Suppose I ...
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1answer
470 views

Positive & negative shocks in a VAR model and impulse response function in R

I have two general questions about impulse response functions in R using the package vars. Take a look at this code: ...