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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Is stationarity of variables neccessary condition for Bayesian VAR?

I am trying to run a BVAR on 5 variables. Four out of five are non-stationary. So shall I do the first difference of the non-stationarity variables or take them in level for running the BVAR? And what ...
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Time series data with seasonality using model VAR and VARMA [duplicate]

I have a time series with seasonal economic data, and other time series to see if this variable help predict my time series, but the VAR model is not a model for seasonality. What options do I have ...
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Johansen procedure shows cointegration r=1, but ect is not significant?

I have 6 variables, all of them I(1). I tested for cointegration and got a significant result for r=1, so I decided to estimate a VECM. The problem is now that the ECTs of the VECM are not significant....
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Eigenvalues of Johansen Trace Test

I'm currently taking a course in time series and have been struggling with understanding the Johansen trace test. Specifically, the calculation of the eigenvalues for the Likelihood ratio statistic. ...
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VAR model with AR(p) and ARMA(p,q) data?

I want to estimate a VAR-model with 6 variables, all of them are stationary. But when I analyse the time series by examining ACF, PACF and auto.arima in R. ...
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What model for volatility spillover effect in R? [closed]

I am doing research to study the volatility spill[over] effect. I have time-series data of Indonesian stock price (Jakarta composite index or JKSE), an exchange rate (IDR/USD), and oil price (BRENT). ...
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VAR coefficients

Say I have a VAR(p) model without any noise (a multidimensional AR model without noise). How would I go about calculating the coefficients that are MSE optimal? Is there an extension to the Yule ...
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Johansen Cointegration Test at Levels or First Differences in a VAR Model

I have multiple variables that I am trying to perform a VAR model with, but all my variables are non-stationary at levels as they fail the Augmented Dickey Fuller test. Having taken the first ...
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Impulse Response Functions for VAR

I have a brief question for my research, which I hope someone may answer fast :-) Considering an impulse response function is a 1 standard deviation with some effect Is 2 standard deviation just the ...
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1 Stationary time series, 1 non stationary: do I need to transform BOTH, OR can I use VAR with 1 transformed and 1 stationary variable?

I am doing a time series forecast using VAR. I have 2 time series, "orders" and "calls" The orders time series is stationary The calls time series is non-stationary Let's say I use ...
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One step ahead forecasting - VAR

Hi I am exploring VAR functions from vars packages Ive a model fit using following command model_fit <- VAR(diff_raw_prices, p = 5, type = "none") Now ...
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What happens if one of my series in the VAR is not stationary?

I have a VAR model that comprehends 6 series. Only one of them is non-stationary even after taking the 1st difference. Do I need to take the 2nd difference? My concern is to approximate too much the ...
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VEC model to VAR model transformation in R

I need to transform my VEC model (function VECM() from tsDyn package) into VAR model to be able to estimate Granger causalities. In the first step I need to estimate VEC model. In the second step I ...
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Endogeneity problem regression

Let us consider the following system made up of $2$ equations: \begin{align*} a^0_{11}y_t&=a^1_{11}y_{t-1}+a^1_{12}c_{t-1}+\epsilon^y_{t}\\ a^0_{22}c_t&=a^0_{21}y_t+a^1_{21}y_{t-1}+a^1_{22}c_{...
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Interpreting Impulse Response Function after first differences of logarithm transformation

I created an impulse response function from a VAR model. I used data transformed by taking the first difference of logarithms. I am now in trouble with giving a substantive interpretation of the scale ...
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What happens to a VAR when I standarize variables?

Suppose you have 6 or 7-time series that are stationary. But my question is what does happen to a VAR when I standardize all the variables of the VAR This makes that the mean is 0 and the standard ...
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Exclusion restrictions as instrumental variables in VAR

I'd like to know if my intuition behind exclusion restrictions that we place to identify the structural vector autoregressive (VAR) models is correct. This is a question not only for econometricians ...
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VAR with trend-stationary variables

I'm currently trying to estimate a VAR with 3 variables - consumption, investment and a credit spread. I have inspected the variables and run ADF tests to determine that they are in-fact trend-...
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Removing/separating impact of variable on time series data

I have a time series with a trend and seasonality. What I would like to achieve is remove impact of variable X on Y, to learn how Y would look like if there were no impact of X. Y is internal metric, ...
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VAR with categorical variables

I'm currently trying to fit a vector autoregression model to my data set with 4 numerical variables and 1 categorical variable. To the best of my knowledge, the way to do this is by one-hot encoding ...
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Hosking test vs Granger Causality for VAR - to use or not to use?

Let's say that we have 2 stationary time series: X and Y. Granger's causality test outputs no granger causality between them, however Hosking cross-correlation test proves there is a relationship ...
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In a time series forecasting, should we apply differencing on entire dataset if one or two features are non stationary?

I'm working on a time series forecasting model using VAR (Vector Autoregression). I have 6 features, out of which 2 features are not stationary. If I apply first-order differencing on those features, ...
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Correlated error restrictions and OLS

I have a VAR model of the form $$ Y_t = \beta Y_{t-1} + \varepsilon_t $$ Where $Y_t$ and $\varepsilon_t$ are $n\times 1$ vectors, and $\beta$ is an $n \times n$ matrix. The residuals $\varepsilon_{t,i}...
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Interpretation of VAR model: about impulse function and lag of p

For example, I have three time series, Y,X1,X2. After using time series cross validation and utilizing BIC/AIC to determine the best p as the lag of the VAR model, in which I got p = 1 to estimate the ...
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Prediction with VAR where a variable has no recent data

I have 10 variables for which I do one-step ahead forecast using a VAR model. The model was trained on historical data for the variables. Almost all the variables receive new data points daily, so ...
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Why is Granger test implication different from significances of coefficients?

I am running a simple Granger causality test on a VAR(4) model. I obtain the following coefficients for it ...
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Time series forecasting with exogenous variables with VAR and ARIMAX/SARIMAX [closed]

I have a multivariate time series forecasting exercise with sales data for past one year at daily level along with exogenous variables as number of buyers, price and promotions all at daily level. Say ...
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What would be a continuous-time version of a VAR process?

It is often said that a AR(1) process can be viewed as a discretized version of the continuous-time Ornstein-Uhlenbeck process. Can we really claim this to be valid considering that the Ornstein-...
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Is Impulse Response a Steady State concept in VAR analysis?

I am reasoning on the concept of steady state in VAR models and Impulse Response Analysis. If I have a Structural VAR model as: $y_t = \Pi_0 + \Pi_1y_{t-1} + A\varepsilon_t $ assume that the ...
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MLE on Structural VAR

I have a simple model that I wish to fit using data. The model is of the form below. \begin{gather} y_t = -\lambda r_t + \theta a_t + \varepsilon_1 \\ \\ \pi_t = \pi_{t-1} + w y_t + \varepsilon_2 \\ \\...
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Getting 'system is computationally singular' error even after drop variables with high autocorrelation in Panel Var

I'm getting system is computationally singular error even after drop variables with high autocorrelation in a Panel Var regression. I'm using public crop insurance ...
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Where to find textbook with Toda Yamamoto causality test example?

I'm trying to create a Toda Yamamoto analysis on a VAR system, for Granger causality test, so far i only find a book where is mentioned: Levendis, J. D. (2018). Time Series Econometrics: Learning ...
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Difference between ML models and conventional time series model such as VAR in multivariate time series forecasting

I am learning to use ML model to do the time series forecasting / prediction in multivariate. But I have confusion about them. For example, I have a monthly dataset UCI AirQuality. I would like to ...
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Seasonal differencing a when the sample size is small when estimating a VAR

When estimating a VAR the series must be level and seasonally stationary. However I have only 48 data points. I first made the series level stationary based on the ADF test and performed HEGY test for ...
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Interpreting why a VAR produces lower error than VARMA?

I trained various VARMA models on the same dataset consisting of different number of AR and MA terms, from $VARMA(0,1)$ and $VARMA(1,0)$ to $VARMA(6,6)$ and all the combinations in-between. After ...
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Implementing VAR predictions manually

I have trained a VAR model via statsmodels library in Python, and I am trying to implement the one-step ahead prediction manually without using the package. Based ...
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How to fit VAR after differencing (Error:NA in y)?

I want to run VAR model by using vars::VAR. Since my X1 was not stationary, I differenced it by diff(data$X1), then got a data ...
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Paval VAR impulse response for exogen variable

Using pvargmm function in R, I am trying to estimate impulse response for the oirf function, but the oirf does impulse response only for endogen variables, ignoring the exogen variables. I need to ...
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I have a VAR model, can I use the R-square values to explain how good the model explains the dependent variable and if yes, how will it be done

I have a VAR model, can I use the R-square values to explain how good the model explains the dependent variable (explanatory power of the model) and if yes, how will the values of the R-square be ...
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Differencing data with missing values?

I have a non-stationary dataset that I would like to model using a VAR model. I need to difference it to make it stationary, however my dataset contains a lot of NaN's at random points, so using ...
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Regression coefficients between vector-valued variables

Sorry for the basic question, but I'm a bit confused on the notation. Let say we have a random vector of $m$ predictors variables ${\bf X} = (X_1,\ldots,X_m)^{\intercal}$ that are used to predict $n$ ...
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Optimal lag length in VAR/VECM: IC or Residual test?

I read so many answers in here that I should use IC(information criteria) to determine the optimal lag length in VAR/VECM. But also it is important to check the residual of VAR/VECM has no-...
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How to resolve problem of autocorrelation in VAR model?

I am trying to define var model with 6 endogenous variables and 4 exogenous variables. Note that all my variables have been made stationary by taking difference My lag selection criteria give me the ...
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Granger Causality and F statistic

I am trying to educate myself in Granger Causality reading the classic literature. From what I have understood the idea is quite simple: first, to test if $X_t$ Granger causes $Y_t$ we define two ...
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How to get the coefficients of determination (R Squared) from factor loadings in FAVAR?

I estimated a FAVAR (Factor Augmented VAR) model for forecasting purposes. The FAVAR gave a very low RMSE value compared to VAR. However, I am unable to interpret the factors through factor loadings. ...
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Is correlation matrix of residuals , a method to check for autocorrelation of residuals in the model?

I am modelling and forecasting using a factor augmented VAR. I got the following residual matrix after fitting the model. My question is how to interpret it and does it indicate any autocorrelations ...
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Difference between Dynamic Factor Model and Factor Augmented VAR?

One simple question: What is the difference between the dynamic factor model and factor augmented vector autoregressive (FAVAR) model?
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How to structure this multi-dimensional data for AR modelling?

I have a time-series dataset for each month for the past three years which represent quoted prices for the same product but with different delivery month. For example, Jul-19 is a dataset consisting ...
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Detecting autocorrelation of residuals using ACF and PACF plots

How to identify autocorrelation of residuals in the fitted VAR model. I have provided the ACF and PACF plots below. There are some significant lags in the PACF plot. Does it mean that my model has ...
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Model for panel VAR with a lot missing values

I have the following panel data with two variables: and There are around 1,000 individuals and 16 time periods. The variable is only available in times 4, 8, 12, and 16. I have the following panel ...
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