8
votes
auto.arima forecasting same value continuously for future part in r
auto.arima() fits a regression on your Fourier terms and models residuals using ARIMA. In your case, the residuals are modeled as ARIMA(1,1,2). This is not a "flat ...
- 131k
7
votes
Accepted
Test for the significance of the effect of an intervention in a time series
What you are referring to is called a test for structural change/break or a changepoint model.
As you have a known change date, you can simply add an interaction in the model, and use a standard t-...
- 3,184
6
votes
Accepted
How is `Arima` from `forecast`/`stats` package with external regressors (dynamic regression) evaluated?
Neither 1 nor 2, though 2 is closer to the truth. Regressor coefficients and AR + MA coefficients are fit simultaneously by maximum likelihood estimation. Indeed, OLS cannot be used there for the ...
- 69.5k
5
votes
kalman filter multiple observations per time step
Most of the time, implementing a Kalman filter with multiple observations falls under the data fusion or sensor fusion umbrella. In general, there is no single way to approach the problem. For a ...
- 1,035
3
votes
Lagged variables in multilevel models
This type of models is also known with the name transition models; for example, see Chapter 10 of Diggle, Heagery, Liang and Zeger.
An issue with these models is that the vector of regression ...
- 21.5k
3
votes
Accepted
Linearisation of Kalman filter
Yes this is correct and you are allowed to do this. To learn more about this subject I suggest you read up on AR models with time varying coefficients.
I wrote a paper about robust estimation of ...
- 133
3
votes
Choice between static and dynamic panel regression
My analysis concerns a macroeconomic study and as often happens in these cases (I would not be wrong but they are commonly called "macro-panel" or "wide-panel").
I have never ...
- 6,454
3
votes
Seasonal Arima with binary exogenous variables
I took your 350 observations into AUTOBOX piece of software specifically designed for time series analysis . After incorporating the deterministic input series AD the following acf was computed
A (...
- 30k
2
votes
How to repoduce the fitted values from a forecast::Arima in R by hand?
Since posting the question I found this other question which helped me arrive at an answer: Arima with xreg, rebuilding the fitted values by hand
...
- 121
2
votes
Fixed Effects Problem when regressing GDP per capita growth on lagged GDP per capita
Consider a simple version of the model without regressors, which is enough to highlight the problem with fixed effects:
\begin{equation}
y_{im}=\alpha_i+\rho y_{i,m-1}+\eta_{im}\quad(m=1,\ldots,M;\;i=...
- 34.8k
2
votes
What's the definition of "Dynamic Regression Models"?
No problem but I'd definitely try to get my hands on some kind of textbook or lecture notes. The following is not a formal definition but here goes my attempt: To me, a DRM is any model which is ...
- 2,875
2
votes
Accepted
Short Run vs Long Run Effect in Dynamic Panel Regressions
We care about dynamic models (time series models, cointegrating regressions, ADL models, etc.) because we want to model the dependent variable's memory of itself. Put another way, we want our model to ...
- 30.7k
1
vote
Accepted
Differences between Dynamic Regression Model and Intervention Model?
Both models apply the transfer function, 𝑣(𝐵), to a variable. And they both take into account noise as 𝑁𝑡. Hence, they are both transfer function-noise models.
However, while the standard dynamic ...
- 26
1
vote
Accepted
Short-term gas demand forecasting
First off, you have already implemented a lot of precisely those models one would recommend in this situation, congratulations!
One possibility for daily data would indeed be Prophet, as dimitry ...
- 131k
1
vote
Short-term gas demand forecasting
I've solved similar forecasting problems using Prophet. There are R and Python implementations, with many replicable examples on that webpage. It handles multiple seasonalities, holidays, and ...
- 38.3k
1
vote
Accepted
Is there a way to tell when a time-series can no longer be predicted by the same model?
There is an R package called changepoint.forecast available on Github here. It implements online changepoint models which look at the forecast residuals and check ...
- 1,309
1
vote
Accepted
Implications of insignificant dccalpha and dccbeta for DCC-model used for co-movement analysis
There are at least a couple of things that can go wrong:
The model's assumptions may be violated, indicating the model is not rich enough to account for patterns in the data.
Some of the model's ...
- 69.5k
1
vote
Interpretation of dccalpha and dccbeta in DCC-GARCH model
As I have said in "DCC GARCH - specifying ARCH and GARCH parameter matrices in Stata", I do not see how DCC could generate spillovers in the sense that $\sigma_{i,t}^2$ be a function of $\...
- 69.5k
1
vote
Accepted
Covariance in system with lagged reverse causality
If you assume zero covariance between $\epsilon^1_t$ and $\epsilon^2_t$ as well as between $y_{t-1}$ and $\epsilon^2_t$, then $\beta$ is identified by a regression of $y_t$ on $x_t$ and $y_{t-1}$. ...
- 2,395
1
vote
Auto.arima coefficients with exogeneous variables
Your equation would have been right if you used $\Delta n_t$ instead of $n_t$. It's the differenced model, like ARMA of $\Delta n_t$:
$$\Delta n_t = -0.8366*\Delta n_{t-1} - 0.3928*\Delta n_{t-2} + ...
- 62.3k
1
vote
Accepted
Transfer Function Clarification
Following the broad guidelines laid out in The theory behind fitting an ARIMAX model I introduced your 36 monthly values and 12 time series into AUTOBOX. After the AUTOBOX modelling process http://www....
- 30k
1
vote
Handling daily time series data for better accuracy
Your question " going forward how can i improve model performance to get better accuracy on test data" . My answer "Build a better model that separates signal from noise" by using data-driven model ...
- 30k
1
vote
Cox-Snell residuals for Cox model with time varying coefficient
Because the tt(disease) means you fit the model with a time-dependent covariate disease*t, R would split one obs into several by the unique event time in the whole ...
- 11
1
vote
Dynamic panel data
An approachable introduction is:
Nathaniel Beck and Jonathan N. Katz. 2011. "Modeling Dynamics in Time-Series–Cross-Section Political Economy Data." Annual Review of Political Science doi:10.1146/...
1
vote
What is meant by a "stochastic constant"?
$\alpha_t$ takes the role of the "constant" in the observation equation:
$$Y_t = \alpha_t + \beta_t X_t + \varepsilon_t$$
That is, it's the part that doesn't depend on $X_t$.
However, it is not ...
- 5,950
1
vote
How to deal with timeseries regressors of different lengths in Dynamic Regression Model
No, you can't plug zero instead of the missing observation. There are many ways to deal with missing data, especially if it's missing at random (MAR). You could impute the missing values, or simply ...
- 62.3k
1
vote
Logistic Regression with Lags
Depends on the nature of the question. Adjusting for an observed lag has advantages such as allowing for real-time prediction and estimating more easily interpretable effects. One confusing bit is ...
- 64.8k
1
vote
Accepted
[R]: DOLS - number of leads and lags
Here is an example of what I typically do, in code form easy running:
...
- 126
1
vote
Accepted
Stationary of exogenous variables in Dynamic Regression with SARIMA errors
You need the dependent variable and the independent variable to have the same order of integration, otherwise they would diverge from each other asymptotically, invalidating both the intuitive or ...
- 69.5k
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