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1

What techniques have you tried? If none, you could start with looking into Granger Causality @wiki by, perhaps, using this Bivariate Granger Causality R tool You have to be quite careful in how you interpret the results, as there are constraints on what the possible factors of influence can be (when testing for Granger Causality).


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The mean equation specification for ARIMAX(8,0,0)(5,0,1)[7] (as in the R code above): $$ (1 - \phi_1L^1 - \ldots - \phi_8L^8)(1-\Phi_1L^7 - \Phi_2L^{14} - \ldots - \Phi_5L^{35})y_t = \beta x_t + (1 + \Theta_1L^7)\varepsilon_t $$ where $x_t$ is the holiday dummy variable. Equivalent ARIMA fit in Matlab (+ GARCH and forecasting): % specify seasonal ...


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You can use Matlab too, that, in my humble opinion, is simpler than R from a syntax point of view. The model you need for is run by the Matlab function arima that can be used with seasonality option to do what you have to do. Here you can find an example and a brief explanation of the model. Type ctrl + F and search for: "Specify a seasonal ARIMA model" ...


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I have the same problem as you. Up to my knowledge, there is no package allowing to combine seasonal ARIMA process with GARCH effects.


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It is a classical misunderstanding, your model is right, you always have a acf equal to one at lag zero (and not one) since if there is no lag acf = covariance(x , x_lag 0) / variance x = variance x / variance x = 1. So you need to pay attention to the x axis , some software displays ACF starting at lag zero and some others from 1 (which make better ...


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I would suggest you to forecast the series using different models and to determine which one is the best accordingly loss functions such as RMSE, MAPE.. or using the Mincer-Zarnowitz regression . You could also compare one-step forecast versus dynamic forecast. Another way is to compute VaR and observe the model having the lowest failure rate. AIC/BIC ...


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Did you try rmgarch package of R ? http://cran.r-project.org/web/packages/rmgarch/index.html http://unstarched.net/r-examples/rmgarch/mgarch-comparison-using-the-hong-li-misspecification-test/


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It sounds to me like you have a Markov model that is not "lumped", it's just that certain transitions don't provide you with any payout. I would model the true transition probabilities. Now, let's ask what the probability of getting a one is, assuming that we won't stay at zero, i.e. $P(X_1=1 | X_0=0, X_1 \not = 0)$. We recall that $$P(A|B)=P(A,B) / P(B)$$ ...


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What you should do: read a general introduction to time series analysis before you apply these methods otherwise you will misinterpret the results. time series are assumed to be covariance stationary. This is in short that their mean is the same for all points in time and that the covariance between two observations only depends on the lag. the "I" in ...


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JFreeeChart provides a TimeSeries data structures with some basic functionality but not a lot of analytics. I have started writing my own and I may open source it if there is sufficient interest. Moving averages, deviations, correlations, returns and most of the easy stuff are already implemented, but I think it needs a few more interesting features before I ...


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Revenue data for non-public companies are available only at a very low frequency, based on financial reporting requirements. It would be impossible to have a long enough period to estimate the normal return in the first place, let alone detect the effect that an event on one single day will have on the annual revenues. Also, it would be very difficult to ...



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