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What is the difference between VAR, Dynamic Regressive, and ARMAX models? An ARMAX model has the form $$ y_t = \beta x_t + \varphi_1 y_{t-1} + \dotsc + \varphi_p y_{t-p} + \varepsilon_t + \theta_1 \varepsilon_{t-1} + \dotsc + \theta_q \varepsilon_{t-q} $$ (one could also have more than one exogenous variable and or lags of exogenous variables in the above equation ) The dependent variable is a univariate time series
spss - How can I transfer an ARMAX model in Excel in order to forecast . . . I am currently trying to set up an Excel based tool, that alows to predict future values based on an ARMAX model, previously set up in SPSS The Excel tool contains the coeffienients, calculated by SPSS, the data used for building the model (dependent variable(y) explanatory variables(x)) and the test period
Estimating an ARMAX model using an I (2) exogenous variable Is it valid to estimate an ARMAX model using I(1) and I(2) variables, which are made stationary after first and second differencing, respectively? For instance, I have an I(1) stock price variable, which I difference once to make it stationary Additionally, I have a technical indicator that is I(2), which I difference twice to achieve
Estimation of $\\mathrm{ARMAX}(1,1,2)$ using panel data $\begingroup$ $(2)$ does not seem to be either ARMAX or ARDL, because it contains neither moving average nor autoregressive terms Or if it does, this is not obvious from the representation that you are using $\endgroup$
Writing the likelihood and conditional variance in a ARMAX model or . . . source 2 ARMAX pag 5 source 3 Dynamic Regression likelihood In case some exogenous regressors are added to a time-series, then the likelihood must be computed conditionally on t-1 and covariates (that are indeed assumed to be exogenous!)