Stata 15 help for arima_postestimation

[TS] arima postestimation -- Postestimation tools for arima

Postestimation commands

The following postestimation commands are of special interest after arima:

Command Description ------------------------------------------------------------------------- estat acplot estimate autocorrelations and autocovariances estat aroots check stability condition of estimates irf create and analyze IRFs psdensity estimate the spectral density -------------------------------------------------------------------------

The following standard postestimation commands are also available:

Command Description ------------------------------------------------------------------------- estat ic Akaike's and Schwarz's Bayesian information criteria (AIC and BIC) estat summarize summary statistics for the estimation sample estat vce variance-covariance matrix of the estimators (VCE) estimates cataloging estimation results forecast dynamic forecasts and simulations lincom point estimates, standard errors, testing, and inference for linear combinations of coefficients lrtest likelihood-ratio test margins marginal means, predictive margins, marginal effects, and average marginal effects marginsplot graph the results from margins (profile plots, interaction plots, etc.) nlcom point estimates, standard errors, testing, and inference for nonlinear combinations of coefficients predict predictions, residuals, influence statistics, and other diagnostic measures predictnl point estimates, standard errors, testing, and inference for generalized predictions test Wald tests of simple and composite linear hypotheses testnl Wald tests of nonlinear hypotheses -------------------------------------------------------------------------

Syntax for predict

predict [type] newvar [if] [in] [, statistic options]

statistic Description ------------------------------------------------------------------------- Main xb predicted values for mean equation -- the differenced series; the default stdp standard error of the linear prediction y predicted values for the mean equation in y -- the undifferenced series mse mean squared error of the predicted values residuals residuals or predicted innovations yresiduals residuals or predicted innovations in y, reversing any time-series operators ------------------------------------------------------------------------- These statistics are available both in and out of sample; type predict ... if e(sample) ... if wanted only for the estimation sample. Predictions are not available for conditional ARIMA models fit to panel data.

options Description ------------------------------------------------------------------------- Options dynamic(time_constant) how to handle lags of y_t t0(time_constant) set starting point for the recursions to time_constant structural calculate considering the structural component only ------------------------------------------------------------------------- time_constant is a # or a time literal, such as td(1jan1995) or tq(1995q1); see Conveniently typing SIF values in [D] datetime.

Menu for predict

Statistics > Postestimation

Description for predict

predict creates a new variable containing predictions such as expected values and mean squared errors. All predictions are available as static one-step-ahead predictions or as dynamic multistep predictions, and you can control when dynamic predictions begin.

Options for predict

+------+ ----+ Main +-------------------------------------------------------------

xb, the default, calculates the predictions from the model. If D.depvar is the dependent variable, these predictions are of D.depvar and not of depvar itself.

stdp calculates the standard error of the linear prediction xb. stdp does not include the variation arising from the disturbance equation; use mse to calculate standard errors and confidence bands around the predicted values.

y specifies that predictions of depvar be made, even if the model was specified in terms of, say, D.depvar.

mse calculates the MSE of the predictions.

residuals calculates the residuals. If no other options are specified, these are the predicted innovations; that is, they include the ARMA component. If structural is specified, these are the residuals from the structural equation; see structural below.

yresiduals calculates the residuals in terms of depvar, even if the model was specified in terms of, say, D.depvar. As with residuals, the yresiduals are computed from the model, including any ARMA component. If structural is specified, any ARMA component is ignored, and yresiduals are the residuals from the structural equation; see structural below.

+---------+ ----+ Options +----------------------------------------------------------

dynamic(time_constant) specifies how lags of y_t in the model are to be handled. If dynamic() is not specified, actual values are used everywhere that lagged values of y_t appear in the model to produce one-step-ahead forecasts.

dynamic(time_constant) produces dynamic (also known as recursive) forecasts. time_constant specifies when the forecast is to switch from one step ahead to dynamic. In dynamic forecasts, references to y_t evaluate to the prediction of y_t for all periods at or after time_constant; they evaluate to the actual value of y_t for all prior periods.

For example, dynamic(10) would calculate predictions in which any reference to y_t with t < 10 evaluates to the actual value of y_t and any reference of y_t with t>10 evaluates to the prediction of y_t. This means that one-step-ahead predictions are calculated for t < 10 and dynamic predictions thereafter. Depending on the lag structure of the model, the dynamic predictions might still refer to some actual values of y_t.

You may also specify dynamic(.) to have predict automatically switch from one-step-ahead to dynamic predictions at p + q, where p is the maximum AR lag and q is the maximum MA lag.

t0(time_constant) specifies the starting point for the recursions to compute the predicted statistics; disturbances are assumed to be 0 for t < t0(). The default is to set t0() to the minimum t observed in the estimation sample, meaning that observations before that are assumed to have disturbances of 0.

t0() is irrelevant if structural is specified because then all observations are assumed to have disturbances of 0.

t0(5) would begin recursions at t=5. If the data were quarterly, you might instead type t0(tq(1961q2)) to obtain the same result.

The ARMA component of ARIMA models is recursive and depends on the starting point of the predictions. This includes one-step-ahead predictions.

structural specifies that the calculation be made considering the structural component only, ignoring the ARMA terms, producing the steady-state equilibrium predictions.

Syntax for margins

margins [marginlist] [, options]

margins [marginlist] , predict(statistic ...) [predict(statistic ...) ...] [options]

statistic Description ------------------------------------------------------------------------- xb predicted values for mean equation -- the differenced series; the default y predicted values for the mean equation in y -- the undifferenced series stdp not allowed with margins mse not allowed with margins residuals not allowed with margins yresiduals not allowed with margins -------------------------------------------------------------------------

Statistics not allowed with margins are functions of stochastic quantities other than e(b).

For the full syntax, see [R] margins.

Menu for margins

Statistics > Postestimation

Description for margins

margins estimates margins of response for expected values.

Examples

Setup . webuse wpi1

Fit ARIMA model with additive seasonal effects . arima D.ln_wpi, ar(1) ma(1 4)

Compute predictions for D.ln_wpi . predict xb

Consider structural component only -- ignore ARMA terms -- when making predictions . predict xbs, structural

Compute predictions for ln_wpi, reversing any time-series operators applied in estimation . predict y, y

Compute predictions for ln_wpi, using lagged forecasted values for predictions after 1970q1 instead of lagged actual values . predict yd, y dynamic(tq(1970q1))

Graph time-series line plot . tsline y yd


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