fixedtest—299
solve oprob1
The first line estimates an ordered probit of Y on a constant, X, and Z. The second line makes a model from the estimated equation with a name OPROB1. The third line solves the model and computes the fitted probabilities that each observation falls in each category.
Cross-references
To perform dynamic forecasting, use forecast (p. 300). See makemodel (p. 358) and solve (p. 475) for forecasting from systems of equations or ordered equations.
See Chapter 18, “Forecasting from an Equation”, on page 541 of the User’s Guide for a discussion of forecasting in EViews and Chapter 21, “Discrete and Limited Dependent Variable Models”, on page 619 of the User’s Guide for forecasting from binary, censored, truncated, and count models. See “Forecasting” on page 754 of the User’s Guide for a discussion of forecasting from sspace models.
fixedtest |
Equation View | Pool View |
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|
Test joint significance of the fixed effects estimates.
Tests the hypothesis that the estimated fixed effects are jointly significant using F and LR test statistics. If the estimated specification involves two-way fixed effects, three separate tests will be performed; one for each set of effects, and one for the joint effects.
Only valid for panel or pool regression equations estimated with fixed effects. Not currently available for specifications estimated using instrumental variables.
Syntax
Object View: |
eq_name.fixedtest(options) |
Options
p |
Print output from the test. |
|
|
Examples
equation eq1.ls(cx=f) sales c adver lsales
eq1.fixedtest
estimates a specification with cross-section fixed effects and tests whether the fixed effects are jointly significant.
300—Appendix B. Command Reference
Cross-references
See also testadd (p. 500), testdrop (p. 503), ranhaus (p. 413), and wald (p. 530).
forecast |
Command || Equation Proc | Sspace Proc |
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|
Computes (n-period ahead) dynamic forecasts of an estimated equation or forecasts of the signals and states for an estimated state space.
forecast computes the forecast for all observations in a specified sample. In some settings, you may instruct forecast to compare the forecasted data to actual data, and to compute summary statistics.
Syntax
Command: |
forecast(options) yhat [y_se] |
Equation Proc: |
eq_name.forecast(options) yhat [y_se] |
ARCH Proc: |
eq_name.forecast(options) yhat [y_se y_var] |
Sspace Proc: |
ss_name.forecast(options) keyword1 names1 [keyword2 names2] |
|
[keyword3 names3] ... |
When used with an equation, you should enter a name for the forecast series and, optionally, a name for the series containing the standard errors and, for ARCH specifications, a name for the conditional variance series. Forecast standard errors are currently not available for binary or censored models. forecast is not available for models estimated using ordered methods.
When used with a sspace, you should enter a type-keyword followed by a list of names for the target series or a wildcard expression, and if desired, additional type-keyword and target pairs. The following are valid keywords: “@STATE”, “@STATESE”, “@SIGNAL”, “@SIGNALSE”. The first two keywords instruct EViews to forecast the state series and the values of the state standard error series. The latter two keywords instruct EViews to forecast the signal series and the values of the signal standard error series.
If a list is used to identify the targets in sspace forecasting, the number of target series must match the number of names implied by the keyword. Note that wildcard expressions may not be used for forecasting signal variables that contain expressions. In addition, the “*” wildcard expression may not be used for forecasting signal variables since this would overwrite the original data.
forecast—301
Options
Options for Equation forecasting
dIn models with implicit dependent variables, forecast the entire expression rather than the normalized variable.
uSubstitute expressions for all auto-updating series in the equation.
gGraph the forecasts together with the ±2 standard error bands.
e |
Produce the forecast evaluation table. |
|
|
i |
Compute the forecasts of the index. Only for binary, |
|
censored and count models. |
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|
sIgnore ARMA terms and use only the structural part of the equation to compute the forecasts.
nIgnore coefficient uncertainty in computing forecast standard error.
f = arg |
Out-of-forecast-sample fill behavior: “actual” (fill obser- |
(default= |
vations outside the forecast sample with actual values |
“actual”) |
for the fitted variable), “na” (fill observations outside |
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the forecast sample with missing values). |
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|
p |
Print results. |
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Options for Sspace forecasting
i = arg |
State initialization options: “o” (one-step), “e” (dif- |
(default=”o”) |
fuse), “u” (user-specified), “s” (smoothed). |
|
|
m = arg |
Basic forecasting method: “n” (n-step ahead forecast- |
(default=“d”) |
ing), “s” (smoothed forecasting), “d” (dynamic fore- |
|
casting. |
|
|
mprior = |
Name of state initialization (use if option “i=u” is |
vector_name |
specified). |
|
|
n = arg |
Number of n-step forecast periods (only relevant if n- |
(default=1) |
step forecasting is specified using the method option). |
|
|
vprior = |
Name of state covariance initialization (use if option |
sym_name |
“i=u” is specified). |
|
|
302—Appendix B. Command Reference
Examples
The following lines:
smpl 1970q1 1990q4
equation eq1.ls con c con(-1) inc smpl 1991q1 1995q4
eq1.fit con_s eq1.forecast con_d plot con_s con_d
estimate a linear regression over the period 1970Q1–1990Q4, compute static and dynamic forecasts for the period 1991Q1–1995Q4, and plot the two forecasts in a single graph.
equation eq1.ls m1 gdp ar(1) ma(1) eq1.forecast m1_bj bj_se eq1.forecast(s) m1_s s_se
plot bj_se s_se
estimates an ARMA(1,1) model, computes the forecasts and standard errors with and without the ARMA terms, and plots the two forecast standard errors.
The following command performs n-step forecasting of the signals and states from a sspace object:
ss1.forecast(m=n,n=4) @state * @signal y1f y2f
Here, we save the state forecasts in the names specified in the sspace object, and we save the two signal forecasts in the series Y1F and Y2F.
Cross-references
To perform static forecasting with equation objects see fit (p. 297). For multiple equation forecasting, see makemodel (p. 358), and solve (p. 475).
For more information on equation forecasting in EViews, see Chapter 18, “Forecasting from an Equation”, on page 541 of the User’s Guide. State space forecasting is described in Chapter 25, “State Space Models and the Kalman Filter”, on page 751 of the User’s Guide. For additional discussion of wildcards, see Appendix B, “Wildcards”, on page 943 of the
User’s Guide.