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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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Can one determine the number of forecast/prediction steps in a VAR on a priori grounds?

Context of my question: I am running a vector autoregression (VAR) model using two time-series of equal length (n ~ 750 data points). The lag was chosen based on the Bayes information criterion (BIC) ...
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Why can we use RMSE as a proxy for the effect size of a VAR or Granger Causality?

My question here follows a previous question by me here: Is there a meaningful option to compute something like an effect size for Granger Causality?. In this previous question I asked for options to ...
Philipp's user avatar
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How does the pvarfeols function from the R panelvar package handle fixed effects?

I have been using the pvarfeols function (Fixed Effects Estimator for PVAR Model) from the R panelvar package. It seems like the ...
catin's user avatar
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Why does the forecast for some series degrade when using a VARMA model comparing to independent ARMA models?

I am working with multiple time series that I suspect are correlated, and I have assumed that using a VARMA model would at least not degrade the forecasts of each series, if not improve them. However, ...
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MSE of VAR impulse responses in R

I am using the vars library in R. How do I calculate the MSE of the impulse responses I generate with the irf function? The <...
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Can I impose an inflation floor for a VAR model?

I am trying to forecast inflation using a VAR. After Granger causality and carefully analyzing impulse response functions, I have selected three variables to include in the VAR. My issue is that while ...
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How to implement ordering for VAR impulse response functions in statsmodels (python)

I'm trying to implement an impulse response function for a VAR system. However, I'm not sure how to implement the variable ordering. Does this correspond to the order of the columns in the data frame? ...
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Is my approach to compute Granger causality valid?

I have two time-series, let us call them A (colored in red) and B (colored in blue). There are ~770 data points per time-series. Note: Both time-series are in fact not the recorded raw signals, but ...
Philipp's user avatar
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Interpretation and Analysis of a Multivariate Threshold Autoregressive Model

I'm looking to study the asymmetric affect a market rate, like the Fed Funds rate has on an interest rate. In other words, I would like to study the response of interest rate adjustments in different ...
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Why does the serial.test function from the vars package in R yield contradictory results for type='BG', 'ES', and 'PT.asymptotic'?

I fitted a VAR model, individually all the residuals do not have autocorrelation, but if I use the serial.test I get different results: Type 'ES' Edgerton and Shukur (1999) Test p.value=0.99 Type 'BG'...
abraham granados carmona's user avatar
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Time series model specification

I want to run a regression analysis in R to explain the variation of my DV g_law_tot which is the growth rate of total budget from t-1 to t. I have yearly data from 1994 to 2023. I have some political,...
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Impulse response function for discontinous time series

I have monthly time series on forecasts (for the months of August, September, October, November, December, and January.) The data is only available for these months and doesn't exist for other months. ...
alex's user avatar
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Estimating VECM with Exogenous variable

I'm currently working on a project that requires estimating a Vector Error Correction Model (VECM), potentially extending to a structural VECM, that incorporates at least one exogenous variable. The ...
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Depicting a Single Equation Error Correction Model in Matrix / Vector notation?

I am trying to express the following single equation error correction model in matrix / vector notation. The original model is: $ \Delta Y = y_{t-1} \delta_0 + \sum_{i=1}^{k}z_{i,t-1} \delta_k + \...
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Estimating VAR of differences of potentially cointegrated variables

What are the possible issues, if any, of estimating a standard two-dimensional $VAR(p)$ of $I(0)$ variables that are first differences of $I(1)$ variables whose potential cointegrating relationship ...
Pavel Filip's user avatar
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Decomposition of VAR(1) coefficient matrix

Consider the VAR(1) process $X_t = \Phi X_{t-1} + \epsilon_t.$ Is there a generally accepted decomposition for the coefficient matrix $\Phi$ that would decrease the degrees of freedom? My initial ...
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Forecast error variance decomposition

Nagayasu uses forecast error variance decomposition (FEVD) to prove a bidirectional causal relationship between endogenous stationary variables in a two-dimensional vector autoregression (VAR). What ...
Pavel Filip's user avatar
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VAR regression between I(1) and I(0)

I am considering two time series and I would like to to a VAR regression between them. The ADF test rejected stationarity in only one of them, so the time series would be I(0) and I(1). I understand ...
dleal's user avatar
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How to count the number of observations in a VAR and when to apply equation-by-equation estimation?

I am dealing with VARs in which the dimension of the systems, k, can become quite big. My question is how are the number of observations counted in a VAR? The reason I am asking is the following ...
Sukre_Estaver's user avatar
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Intercept or trend in a VAR model for stationary percentage changes [closed]

I am estimating a VAR model in R, using the vars package. There is an argument in VAR() function called ...
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Selection of best VARX model using VAR() in R

I have 9 variables (all stationary) grouped into five different datasets (each set has 4 common variables and one different). How can I evaluate which is the best VARX model? I'm using ...
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FAVAR model, stationarity and Toda & Yamamoto

To overcome the problems of non-starionarity and cointegration between variables, Toda and Yamamoto (1995) suggested to estimate a VAR with a number of lags sufficient to avoid the problem of ...
Ricter's user avatar
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VARMAX does not improve over ARIMA

i'm trying to forecast inflation using the macroeconomic variables i understand to be determinants of inflation (e.g. inflation expectations, exchange rates e.t.c) however, what ive noticed is that ...
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VMA to VECM representation

I'm doing research at the moment and for estimation purposes I need to convert a VMA (equation 9.2.2 in the attached picture) to a VECM model (9.2.1) in picture; does anyone know how to make this ...
user406838's user avatar
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Outputs of Granger causality and FEVD are opposite of each other

This is regarding cointegration or VAR analysis of bivariate systems. The Granger causality will either say that instrument 1 (say fund) and instrument 2 (say index) are either not Granger causing ...
significance seeker's user avatar
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Impulse Response of a dummy Variable

I am writing a paper about electoral periods and its effect in the exchange rate. I estimated a VAR model where the electoral period dummy its included in the var. I am trying to measure the impulse ...
marioavila's user avatar
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2 answers
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Autocorrelation in the vector case

I obtained two sets of data from a Monte Carlo simulation of polymer movement. One is a list of $r^2_{end-to-end}$, ...
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Can I compute a VAR Model and then work on only one OLS equation?

Good morning, I'm trying to estimate a VAR model between six variables and one of them is the price of copper. What I'm interested in is only the equation of the copper prices and i'm running a VAR ...
Ricter's user avatar
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Can I transform two log variables into one using multiplication in VECM time series model?

I performed a Vector error correction model using Tourist Arrivals as a dependent Variable and Real GDP, Public Road Transport operated distance and Public Railway Transport operated distance as ...
Phil Smith's user avatar
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Using PLS to determine relative contributions of different drivers to the variability of a source?

I have a several drivers that are influencing the variability of a source. The drivers are not independent of one another, so I cannot use linear regression. Instead, I used Partial Least Squares (PLS)...
Billiam's user avatar
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Why is mean of innovations restricted to zero in definition of VAR process?

The VAR process is defined as: $$\begin{align} \mathbf{y}_t = A_1\mathbf{y}_{t-1} + \dots + A_d\mathbf{y}_{t-d} + \boldsymbol{\epsilon}_t, \quad t \in \mathbb{Z} \end{align}$$ where $\boldsymbol{\...
Dylan Dijk's user avatar
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How to choose the best one model from ARIMAX, ARCH/GARCH and VAR?

Now I have 3 models to find what economic factors have an effect on gold price: ARIMAX model ARCH/GARCH model VAR model What is the tool to find the best model? In linear regression, we can ...
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Forecasting cash inflow using network load usage - timeseries vs GAM (or similar)

I have two datasets of daily cash_inflows & network consumption (load) for past 4 years. Network load consumption is the single source of income. There will be some lag between load consumption &...
Math Lover's user avatar
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Efficient way to compute covariance matrix of Vector Autoregressive Process of order 1 (VAR)

For a VAR process $$ X_t = A_1 X_{t-1} + \epsilon_t $$ The covariance of $X_t$ can be computed in the following way: $$ \text{vec}(\Sigma) = (I -(A \otimes A))^{-1} \text{vec}(\Sigma_{\epsilon}) $$ ...
Dylan Dijk's user avatar
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Normality assumption n=48 [closed]

Regarding normality assumptions for model evaluation I have been told by my supervisor that it is not needed in the case of analyzing but is needed in forecasting only. i am looking for an explanation....
Am Ahmed's user avatar
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Interpreting the VECM: which variable corrects towards which one?

I am trying to understand the vector-autoregressive error correction model, but I am having a hard time understanding the error correction part. Imagine that we have a VAR(1) model of 2 dimensions: $$\...
Rstrobaek's user avatar
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BVAR model: Draws and Burn-In?

This is a very basic question. I am trying to understand how a BVAR model works. One thing I dont get is why we are using a burn-in period and what we are making "draws" from. I simply can ...
Johanna W's user avatar
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Difficulties with estimation and strange fitted values for BVAR (BVAR R package)

I'm using the BVAR package in R to estimate a Bayesian vector autoregression involving the following monthly variables: US Capacity utilization, US Total Employees, US PCE index, and 5,10,20,30 year ...
Diego De Vivero's user avatar
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VECM model unable to predict approximately but able to learn the pattern

In the below image VECM model has learnt the pattern but did not predict properly there is a difference in actual and prediction Have used the below dataset to predict the meanpressure:- https://www....
Shree's user avatar
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In VAR model, can I include not-granger-causing variables in impulse response anaysis?

In a VAR model, I have 6 endogenous variables(X: dependent, others: independent) Having run Granger Causality test, I found that only 2 independent variables granger cause X. Can I include other 3 ...
GPark's user avatar
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Autocorrelation of residuals in my VAR model

I am running a VAR model to predict the flow of consumer loans (dependent variable). I have three independent variables (consumption of durables goods, employment rate and households GFCF). Each ...
user398549's user avatar
2 votes
0 answers
44 views

Common way to forecast multivariate time series where components are restricted by an inequality?

Let's say we have a multivariate time series that we would like to forecast future values of: $Z_t = (X_t, Y_t)$ where $X_t$ and $Y_t$ are real-valued time series and constrained by the inequality $...
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Anomaly detection in Multivariate timeseries

I am working on an algorithm which will detect the anomalies in multivariate timeseries. Suppose there is a time series My algorithm will compute two equations: lower_equation_y and upper_equation_y. ...
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Dynamic adjustment equations coefficients using VECM from statsmodels

I am looking to replicate a study that was conducted on running a VECM to assess the short- and long-term impacts of media on sales and brand health metrics (consideration & awareness). Using <...
Timothy Mcwilliams's user avatar
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119 views

VECM predict gives forecasting results that lag behind actual data

I am using Python's statsmodels.tsa.vector_ar.vecm.VECM to estimate VECM models and generate pseudo out-of-sample forecasts with the .predict() function to compare with actual data. For example, I ...
Hanqing Ye's user avatar
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Creating a VAR model - data with seasonalities

I am in process of creating a forecast using VAR model for pricing of a certain commodity. Some of my variables (such as price itself, as well as inflation, and taxation) don't have any seasonalities. ...
Thomas's user avatar
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VAR models: Effects of sparsity and magnitude disparities on VAR dynamics and possible solutions

In a VAR model involving two (or more) time series, if one series has sparse data with low counts, while the other series has lower sparsity and higher values, are there any statistical or technical ...
kk68's user avatar
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How to use VAR model for model forecasting

Good evening, everyone. I am a software engineer and I am studying the VAR model and its advantages and disadvantages. Specifically, my question is the following: is it possible to use VAR to predict ...
Alessandro Pio Budetti's user avatar
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BEKKS package in R. Are you supposed to feed residual of a mean equation model into the BEKK.fit or the return series?

I am trying to use the BEKKs Package in R. For context, my plan is to fit a VAR model of index prices to obtain the residuals. Then feed the residual into the BEKK.fit function. However, I am not sure ...
long nguyen's user avatar
2 votes
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Higher moments of Vector Auto-Regressive (VAR) process

If we have a VAR process: $\begin{align} \mathbf{y}_t = A_1\mathbf{y}_{t-1} + \dots + A_d\mathbf{y}_{t-d} + \boldsymbol{\epsilon}_t, \quad t \in \mathbb{Z} \end{align}$ With the stability condition ...
Dylan Dijk's user avatar

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