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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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....
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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:
$$\...
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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 ...
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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 ...
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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....
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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 ...
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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 ...
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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 <...
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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 ...
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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. ...
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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 ...
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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 ...
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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 ...
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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 ...
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Asymmetric VAR Models
I know that VARs employ the same number of lags for each variable in the model. However, what if we were dealing with a scenario where each variable in the VAR model has a unique number of lags. Thus, ...
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Interpretation of impulse response analysis - Cholesky decomposition output in R
I am doing an impulse response analysis involving 3 time series A, B, and C in R.
Following Lutkepohl approach, I used the log and diff functions to make them stationary.
After creating the VAR model, ...
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Likelihood function of VAR-MGARCH-BEKK model?
I am doing my dissertation on the spillover effect between countries' markets and looking to use VAR-MGARCH model to do it. For example how would a change/shock of US market index affect Thailand ...
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Can I estimate a panel VARX as a SUR?
If my panel vector autoregression with exogenous variables (VARX) is unrestricted, and the same variables appear on every equation. Can I estimate it as a panel SUR?
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OLS panel vector autoregression with exogenous variables
Are there any R, Stata, or Python packages that allow the estimation of panel VARX models with options to specify fixed-effects and clustered standard errors?
I'm quite desperate at this point and I ...
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Johansen cointegration, VAR, VECM
I do have a question regarding Johansen's cointegration, VAR, and VECM model estimation. I would like to analyze the relationship between two variables using these methods. My dataset consists of 4 ...
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Identification of Non-Gaussian State Space Model
The following paper details necessary assumptions in order to have a non-gaussian state-space model be identifiable (see A1-A5); 'A General Linear Non-Gaussian State-Space Model: Identifiability, ...
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Should the RMSE of an unrestricted VAR model decrease as compared to a restricted Autoregression model when there is Granger Causality
I have 2 time series, say for instance, T1 and T2. T1 granger causes T2 at lag 2. Should this mean that if I make a VAR model with these two time series, and an autoregression model with just T2, the ...
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Use centered variables or include an intercept in time series analysis?
I have read that analogous to univariate AR(p) models, there are two possibilities to allow for a non-zero mean with VAR(p) models:
a) either use centered variables in the model:
Φ(B)(Xt − μ) = Zt
b) ...
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Should the RMSE of the unrestricted (VAR) model for a time series that is being Granger caused by another be lesser than its restricted counterpart?
I have a couple of time series, say, T1 and T2. I have established (using the grangercausalitytest library of Statsmodels in ...
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Multiple Linear Regression Predictions with Macroeconomic Indicators
We are given some commodity (steel, copper etc.) price predictions made by the following steps:
Finding the correlations between the commodity price data and macroeconomic indicators
Selecting a set ...
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Bayesian VAR: Derivation of predictive distribution for reduced form VAR
I have a standard reduced form VAR of type without intercept: $y_t = A_{1}y_{t−1} + \ldots + A_py_{t−p} + e_t$, $e_t \sim N(0,Σ)$.
I need to derive the predictive posterior distribution $p(y_{T+h}|y_{...
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Name for the following auto-regressive type data generating model
Suppose $X$ is a $d$-dimensional random vector. The coordinates follow an auto-regressive structure:
$$
X_{1} \sim N(\mu_1,\sigma^2_1), \qquad X_{j}|X_{< j} \sim N(a^T_j X_{<j},~ \sigma^2_j), \...
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Impulse Response in a VAR model - all endogenous variables
I am trying to understand the implications of an IRF. Specifically in a VAR system.
Here is documentation I looked at:
https://www.statsmodels.org/stable/vector_ar.html#impulse-response-analysis
https:...
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impulse response values VAR statsmodels
I am trying to understand how the values of the irf plots are estimated
I read following page: https://www.statsmodels.org/stable/vector_ar.html
But I don't understand how the values of the impulse ...
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Why are the variables in a VAR model considered endogenous?
Does it have to do with the interdependence of one equation on the lagged values of its own as well as the other equations?
If I remember correctly, in simultaneous equations, cross-causality is also ...
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Detrending these time series
I have the following time series. Its clearly not a simple linear trend. I want to explore the relationship among these variables using a VAR, or even a time-varying VAR. The biggest issue in my data ...
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Eigenvalues of VAR(1) coefficient matrix
Suppose we have a VAR(1) model:
$$
\mathbf{X}_t = \boldsymbol{\Phi} \mathbf{X}_{t-1} + \mathbf{Z}_t, \hspace{10mm} \mathbf{Z}_t \sim WN(0,\Sigma)
$$
If we can keep plugging that equation into itself, ...
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Can I compare forecasting performance of rolling window VAR and usual forecast of model with ARIMA errors?
Can I compare forecasting performance of rolling window VAR and usual forecast of model with ARIMA errors?
Or maybe there is exist better way to compare forecasting performance?
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Statsmodels VAR plot_acorr() amount of plots
I am working on a statsmodels VAR model to forecast some values and want to analyze the created model. In the examples and in some books I read about calculating the autocorrelation of the residuals ...
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Can I compare the forecasting performance of two models VECM and VAR
Can I compare the forecasting performance of two models VECM and VAR with the same dataset, if in the case of VECM I have some of the variables I(1) at the level, and others are stationary?
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Structural break near the end of training sample
I have multivariate time series (VAR), and i found a break near the end (84.7%).
I wanted to use a dummy variable in this situation, but even if I choose 90% training set, the dummy only trains on 100 ...
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Transform Second Difference Predictions from VAR model back to levels
I am currently working with a VAR model in R using the second difference of some variables (it only becomes stationary after differencing twice). So far I'm trying to see if the model fits one of the ...
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Find the conditional distribution from joint normal distribution with vec operators
I have two random matrices one on the top of the other:
$ \begin{bmatrix}\boldsymbol{B_1} \\ \boldsymbol{B_2} \end{bmatrix}$.
and they are both of dimension $k \times N$. I have that:
$ vec\begin{...
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How to model structural break in ARIMAX/VAR/ARDL
I tried to use a QLR test for structural breaks for a variable that I am forecasting, and I found a break, which is very accurate to geopolitical events in 2022.
Because of this, my significance of ...
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ARIMA, VAR and State Space Model (SSM) forecasting comparison
I am trying to compare the asset price forecasting abilities of SSMs with ARIMA and VAR models. To keep it brief, this is the plan that I am following:
Collect multivariate data
Perform ADF ...
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Granger-Causality test result interpretation
I have fitted a VAR model to first-differenced financial data, and conducted a Granger-Causality test on stationary data. The results can be seen below:
The above results show that only one variable ...
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Prediction intervals - "VAR in levels" vs "VAR in differences"
The prediction intervals are much wider on my "VAR in differences" model than in my "VAR in levels" model. Any ideas of why this might be the case? I know there is a strong ...
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Trade-offs when building VAR models
I am trying to build my first VAR model, consisting of three time series, for forecasting and have gotten quite far. I have made all the tests and comparing models indcluding different lags, different ...
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Unit root test and cointegration
maybe someone can help me with my data.
I analyse how macroeconomic indicators affect stock index. For this analysis I prefer VAR model.In my case data of all variables are non-stationary - I have ...
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VARMAX model in r | Fit VARMA model including exogenous variable [closed]
Working on VARX model and I want to include MA term here but I have not found any package in R to build VARMAX model. MTS package can be used to fit VARMA model but I want to include exogenous ...
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Can a VAR(d) process be strictly stationary?
If a stochastic process is generated by a vector autoregressive process of order $d$, can it be strictly stationary. I know that under the stability condition, that this is weakly stationary process.
$...
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What discrete vector timeseries modeling (e.g. autoregression) methods support "continuity" requirements?
This question is motivated by the need to do vector-valued discrete time series forecasting with some guarantees of "continuity" (or rather, a discretized analog of continuity expressed in ...
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Can we use vector as the variable in an autoregressive model?
I have a vector variable with 50 time periods. I believe the vector variable changes over time but also has a static component. I am looking for a model similar to an AR(1) model that can provide me ...
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