Questions tagged [autoregressive]
The autoregressive (AR) model is a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values.
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Could we impose to a SARIMA model that the sum of predict values equals a given value (in R)?
I work on forecasting in R using a SARIMA model with monthly time series data. However, for the last year, I don't have the details by months but I have the annual value.
I want to predict 2023 data ...
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Dickey-Fuller test statistical significance
I recently read about the Dickey-Fuller test.
Firstly about the transformation from
$$
y_t=\rho y_{t-1}+\epsilon_t
$$
to:
$$
y_t-y_{t-1}=(\rho-1) y_{t-1}+\epsilon_t
$$
I assumed it is to get the ...
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AR(p) model in R not fitting data [closed]
I have a set of data that I am trying to model with a simple AR(p) model. I've run a Dickey Fuller test on unit root stationarity and reject the null.
However, when I run a simple ar command in R I ...
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How to simulate this AR(1): Xt =2+0.8(Xt_1 −2)+εt series in R? [duplicate]
I want to simulate an AR(1) model defined as follows:
$$X_t = 2 + 0.8(X_{t-1} −2) + \varepsilon t$$
but I do not know how to interpret the constant $2$ in this term.
How do I simulate this in R?
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Unit root stationarity and modelling AR(p) process
I'm reading through Introduction to Econometrics by Gary Koop. I'm a little confused on the process for modelling AR(p) processes. Hopefully someone can help clarify things for me. Let me set out my ...
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Covariance stationarity for an AR(1) with squared terms?
I have a simple, but surprisingly mind-numbing, problem. I am familiar with determining stationarity for an AR(p) process: look at the roots from the characteristic equation. What if we had higher-...
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Autoregressive cross-lagged models
I'm working on a research project with an autoregressive cross-lagged model with two measures three time points. The paths from $t_1$ to $t_2$ were significant, but $t_2$ to $t_3$ were all not ...
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Characteristic Polynomials for AR(p) Processes with intercepts
If we have AR(1) process with no intercept like:
$$
x_t=\phi x_{t-1}+w_t,
$$
it has a unit root when $|\phi|=1$.
If we have an AR(p) process with no constant
$$
x_t=\phi_1 x_{t-1}+\phi_2 x_{t-2}+\...
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Manual MLE of AR(1) yields a weird initial value $y_0$
I am playing with a manual implementation of the maximum likelihood estimator (MLE) of the parameters in an AR(1) model
$$
y_t = c + \varphi_1 y_{t-1} + \varepsilon_t
$$
with $\text{Var}(\varepsilon_t)...
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Alternative method to deriving autocorrelation function of stationary AR(2) process [duplicate]
I have read this question/answer:
Autocorrelation of a stationary AR(2) process
How can we derive this using Expectation.
Let $Y_t = \phi_0 +\phi_1 Y_{t-1} + \phi_2 Y_{t-2}+\epsilon_t$
I found the ...
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When to include random-effects in zero-inflation model component?
Is it appropriate to specify random-effects (RE) in zero-inflation (ZI) component of the model? My intuition is that whatever RE is appropriate for main component should be appropriate for ZI ...
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In repeated measures, how to distinguish regression to the mean from a negative lagged effect?
I have repeated measures for a quantitative variable "cry" for N = 52 participants (how much you cry at a given time), there are 30 repeated measures. The values range from 0 (not at all) to ...
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Difference between zero-inflated model and zero-altered model
Could someone explain what assumptions I am making (perhaps implicitly) when I specify family = nbinom2() versus ...
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Brockwell/Davis seem to say more persistence implies better predictability---do I have a counterexample?
Brockwell/Davis, Introduction to Time Series and Forecasting, p. 40, write (notation slightly adapted; please refer to screenshot below)
The best linear predictor $l(Y_{T})=aY_{T}+b$ for a stationary ...
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Time series regression on mixed frequency overlapping data
I have an hourly univariate time series. I am trying to see if the next hour, day, week etc changes are forecastable from the past changes. The ACF and PACF of the data both look similar and show some ...
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