Questions tagged [vector-auto-regression]
Vector autoregression is a stochastic process model used to capture the linear interdependencies among multiple time series.
32 questions
0 votes
0 answers
7 views
Relating Parameters of an Autoregressive Model Back to Physical Parameters of its Equivalent Differential Equation
Noting first that autoregressive models are for stationary signals and it is assumed an AR fit will not produce accurate results: For the differential equation $m*\frac{d^2x}{dt^2}+b*\frac{dx}{dt}+k*x(...
1 vote
0 answers
41 views
Can I simply apply non-linear transformations to a VAR model like this? How do I know whether it's stable/unstable?
I am writing a dissertation and for one part I want to validate an estimator I derived given different systems. In particular, the most basic system is a VAR(1) model: $$ X_t = AX_{t-1} + \epsilon_t $$...
1 vote
2 answers
79 views
Covariance of a multivariate autoregression
Consider an autoregressive process of the form, $$ x_{t+1} = Ax_t + \xi_t, $$ where $\xi_t$ is sampled from ${N}(0,\Sigma)$, then I have two formulas from the stationary covariance that do not agree. ...
2 votes
1 answer
234 views
How to show that an AR(1) process with Gaussian errors is ergodic?
(Repost of this thread, as I believe that the answer may be wrong.) An $\text{AR}(1)$ model with Gaussian errors takes the form below: $$x_t = \rho x_{t-1} + \epsilon_t$$ where $\epsilon_t$ are IID ...
2 votes
1 answer
86 views
Least squares regression with stable and non-negative constraint
I am trying to fit an auto-regressive model to a time-series where I have some constraints. We have the first order model, $$ X_{t+1} = AX_t + \xi_t, $$ which I can pose as a least-squares ...
0 votes
1 answer
55 views
Non-negative autoregressive model
I am trying to fit a linear 1-order autoregressive model to some multivariate time-series data. The model I am using is of the form $$x_t = Ax_{t-1}+\xi_{t-1}$$ and I am solving it in R using the mAr ...
1 vote
1 answer
38 views
Autoregressive Gaussian process -- limiting distribution?
Let $x_0 = 0$ and suppose that $x_{t + 1} \mid x_t \sim N((1-\alpha) x_t, \alpha^2)$. That is, $$ x_{t + 1} = (1- \alpha) x_t + \alpha w_{t+1}, \quad \mbox{for}~t \geq 1, $$ where $w_{t}$ are ...
1 vote
0 answers
23 views
How can I decompose this VAR equation?
I have this VAR equation $\begin{pmatrix} s_t\\ f_t \end{pmatrix} = \frac{\begin{pmatrix} 1-0.4L & 0.3L\\ -0.6L & 1-0.1L \end{pmatrix}}{(1-0.1L)(1-0.4L)+0.18L^2} \begin{pmatrix} 0.5\\0.7\end{...
1 vote
0 answers
129 views
Is there such a thing as a geometric series of a non-constant?
I'm an applied social scientist with an interest in time series analysis. I have a question about the behavior of a 'geometric series' of a non-constant, so to speak. If we had a geometric series like ...
1 vote
0 answers
105 views
Questions on coin tossing and AR(1), martingale & Markov chain
After $t$ tosses of a fair coin, let $H(t)$ be the number of heads observed so far; $T(t)$ be the number of tails observed so far; $X(t)=H(t)-T(t)$. Which one of $H(t)$, $T(t)$ and $X(t)$ are a) AR(1)....
1 vote
0 answers
36 views
Generating synthetic multivariate time series with stable VAR model
I am trying to generate stable multivariate time series (MTS) using a VAR model. Here I don't try to fit a VAR model on existing data, but to create the data from a VAR process by manually setting the ...
3 votes
0 answers
46 views
What's the point on using logarithms before differencing a time series in ARIMA? [closed]
I would like to know what's normally the point on ARIMA models, before differencing our time series in order to get stationarity, of applying a logarithm to our series. Does this helps our time series ...
0 votes
1 answer
82 views
Two correlated AR(1) series [closed]
I have two AR(1) series that are correlated. $$X_{t,1} = \rho_1X_{t-1,1} + e_{t,1}$$ $$X_{t,2} = \rho_2X_{t-1,2} + e_{t,2}$$ and $corr(X_{t,1}, X_{t,2}) = \rho.$ I want to generate at each time $t$ a ...
1 vote
0 answers
113 views
Wold's decomposition and Gaussian distribution in infinite dimensional Hilbert space
It is well known that the Wold's decomposition allows that every covariance-stationary time series $ Y_{{t}}$ can be written as the sum of two time series one deterministic $\eta _{t}$ and one ...
0 votes
0 answers
88 views
Simulate autoregressive model
I am trying to simulate an autoregressive model such that $\mathbf{W}^t = \mathbf{W}^{t-1}\mathbf{M}$ where $\mathbf{M} \in \mathbb{R}^{k \times k}$ and $\mathbf{W} \in \mathbb{R}^{p \times k}$ where $...