align—203
Coordinates may be used with other options, but they must be in the first two positions of the options list. Coordinates are overridden by other options that specify location.
When addtext is used with a multiple graph, the text is applied to the whole graph, not to each individual graph.
Examples
freeze(g1) gdp.line
g1.addtext(t) "Fig 1: Monthly GDP (78m1-95m12)"
places the text “Fig1: Monthly GDP (78m1-95m12)” centered above the graph G1.
g1.addtext(.2, .2, X) "Seasonally Adjusted"
places the text “Seasonally Adjusted” in a box within the graph, slightly indented from the upper left corner.
g1.addtext(t, x, textcolor(red), fillcolor(128,128,128), framecolor(black)) "Civilian\rUnemployment (First\\Last)"
adds the text “Civilian Unemployment (First\Last)” where there is a return between the “Civilian” and “Unemployment”. The text is colored red, and is enclosed in a gray box with a black frame.
Cross-references
See legend (p. 332) and textdefault (p. 508). See also “Graph” (p. 161) for a summary of the graph object command language.
align |
Graph Proc |
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Align placement of multiple graphs. |
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Syntax |
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Graph Proc: |
graph_name.align(n,h,v) |
Options
You must specify three numbers (each separated by a comma) in parentheses in the following order: the first number n is the number of columns in which to place the graphs, the second number h is the horizontal space between graphs, and the third number v is the vertical space between graphs. Spacing is specified in virtual inches.
204—Appendix B. Command Reference
Examples
mygraph.align(3,1.5,1)
aligns MYGRAPH with graphs placed in three columns, horizontal spacing of 1.5 virtual inches, and vertical spacing of 1 virtual inch.
var var1.ls 1 4 m1 gdp
freeze(impgra) var1.impulse(m,24) gdp @ gdp m1
impgra.align(2,1,1)
estimates a VAR, freezes the impulse response functions as multiple graphs, and realigns the graphs. By default, the graphs are stacked in one column, and the realignment places the graphs in two columns.
Cross-references
For a detailed discussion of customizing graphs, see Chapter 14, “Graphs, Tables, and Text Objects”, on page 413 of the User’s Guide.
See also graph (p. 316).
alpha |
Object Declaration |
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Declare an alpha series object.
The alpha command creates and optionally initializes an alpha series or modifies an existing series.
Syntax
Command: |
alpha ser_name |
Command: alpha ser_name=formula
The alpha command should be followed by either the name of a new series, or an explicit or implicit expression for generating a series. If you create a series and do not initialize it, the series will be filled with the blank string “”.
Examples
alpha x = "initial value"
creates a series named X filled with NAs.
Once an alpha is declared, you need not include the alpha keyword prior to entering the formula (alternatively, you may use genr (p. 308)). The following example generates an
append—205
alpha series named VAL that takes value “Low” if either INC is less than or equal to 5000 or EDU is less than 13, and “High” otherwise:
alpha val
val = @recode(inc<=5000 or edu<13, "High", "Low")
If FIRST and LAST are alpha series containing first and last names, respectively, the commands:
alpha name = first + " " + last
genr name = name + " " + last
create an alpha series containing the full names.
Cross-references
See “Alpha Series” on page 151 of the User’s Guide for additional discussion. “Alpha” (p. 154) provides details on the alpha object.
See also genr (p. 308).
append
Logl Proc | Model Proc | Sspace Proc | System Proc | Valmap Proc | Var Proc
Append a specification line to a logl, model, sspace, system, valmap, or var.
Syntax
Object Proc: |
object_name.append text |
Var Proc: |
var_name.append(options) text |
Type the text to be added after the append keyword. For vars, you must specify the restrictions type option.
Options for Vars
One of the following options is required when using append as a var proc:
svar |
Text for identifying restrictions for structural VAR. |
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coint |
Text for restrictions on the cointegration relations and/ |
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or adjustment coefficients. |
Examples
model macro2
macro2.merge eq_m1
macro2.merge eq_gdp
206—Appendix B. Command Reference
macro2.append assign @all f
macro1.append @trace gdp
macro2.solve
The first line declares a model object. The second and third lines merge existing equations into the model. The fourth and fifth line appends an assign statement and a trace of GDP to the model. The last line solves the model.
system macro1
macro1.append cons=c(1)+c(2)*gdp+c(3)*cons(-1)
macro1.append inv=c(4)+c(5)*tb3+c(6)*d(gdp)
macro1.append gdp=cons+inv+gov
macro1.append inst tb3 gov cons(-1) gdp(-1)
macro1.gmm
show macro1.results
The first line declares a system. The next three lines append the specification of each endogenous variable in the system. The fifth line appends the list of instruments to be used in estimation. The last two lines estimate the model by GMM and display the estimation results.
vector(2) svec0=0
sspace1.append @mprior svec0
appends a line in the state space object SSPACE1 instructing EViews to use the zero vector SVEC0 as initial values for the state vector.
Cross-references
See “System Estimation Methods” on page 694, and “Models” on page 775, and “Value Maps” on page 161 of the User’s Guide for details. See also cleartext (p. 242).
arch |
Command || Equation Method |
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Estimate generalized autoregressive conditional heteroskedasticity (GARCH) models.
Syntax
Command: arch(p,q,options) y [x1 x2 x3] [@ p1 p2 [@ t1 t2]]
Command: arch(p,q,options) y=expression [@ p1 p2 [@ t1 t2]]
Equation Method: eq_name.arch(p,q,options) y [x1 x2 x3] [@ p1 p2 [@ t1 t2]]
Equation Method: eq_name.arch(p,q,options) y=expression [@ p1 p2 [@ t1 t2]]
arch—207
The ARCH command or method estimates a model with p ARCH terms and q GARCH terms. Note the order of the arguments in which the ARCH and GARCH terms are entered, which gives precedence to the ARCH term.
The maximum value for p or q is 9; values above will be set to 9. The minimum value for p is 1. The minimum value for q is 0. If either p or q is not specified, EViews will assume a corresponding order of 1. Thus, a GARCH(1, 1) is assumed by default.
After the “ARCH” keyword, specify the dependent variable followed by a list of regressors in the mean equation.
By default, no exogenous variables (except for the intercept) are included in the conditional variance equation. If you wish to include variance regressors, list them after the mean equation using an “@”-sign to separate the mean from the variance equation.
When estimating component ARCH models, you may specify exogenous variance regressors for the permanent and transitory components. After the mean equation regressors, first list the regressors for the permanent component, followed by an “@”-sign, then the regressors for the transitory component. A constant term is always included as a permanent component regressor.
Options
egarch |
Exponential GARCH. |
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parch[=arg] Power ARCH. If the optional arg is provided, the power parameter will be set to that value, otherwise the power parameter will be estimated.
cgarch |
Component (permanent and transitory) ARCH. |
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asy=integer Number of asymmetric terms in the Power ARCH or (default=1) EGARCH model. The maximum number of terms
allowed is 9.
thrsh=integer Number of threshold terms for GARCH and Component (default=0) models. The maximum number of terms allowed is 9.
For Component models, “thrsh” must take a value of 0 or 1.
archm=arg ARCH-M (ARCH in mean) specification with the conditional standard deviation (“archm=sd”), the conditional variance (“archm=var”), or the log of the conditional variance (“archm= log”) entered as a regressor in the mean equation.
208—Appendix B. Command Reference
tdist [=number] |
Estimate the model assuming that the residuals follow a |
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conditional Student’s t-distribution (the default is the |
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conditional normal distribution). Providing the optional |
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number greater than two will fix the degrees of freedom |
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to that value. If the argument is not provided, the |
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degrees of freedom will be estimated. |
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ged [=number] |
Estimate the model assuming that the residuals follow a |
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conditional GED (the default is the conditional normal |
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distribution). Providing a positive value for the optional |
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argument will fix the GED parameter. If the argument is |
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not provided, the parameter will be estimated. |
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h |
Bollerslev-Wooldridge robust quasi-maximum likeli- |
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hood (QML) covariance/standard errors. Not available |
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when using the “tdist” or “ged” options. |
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z |
Turn of backcasting for both initial MA innovations and |
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initial variances. |
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b |
Use Berndt-Hall-Hall-Hausman (BHHH) as maximiza- |
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tion algorithm. The default is Marquardt. |
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m=integer |
Set maximum number of iterations. |
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c=scalar |
Set convergence criterion. The criterion is based upon |
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the maximum of the percentage changes in the scaled |
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coefficients. |
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s |
Use the current coefficient values in “C” as starting val- |
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ues (see also param (p. 404)). |
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s=number |
Specify a number between zero and one to determine |
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starting values as a fraction of preliminary LS estimates |
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(out of range values are set to “s=1”). |
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showopts / |
[Do / do not] display the starting coefficient values and |
-showopts |
estimation options in the estimation output. |
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deriv=keyword |
Set derivative method. The argument keyword should |
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be a one-letter string (“f” or “a” corresponding to fast or |
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accurate numeric derivatives, respectively). |
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p |
Print estimation results. |
arch—209
Saved results
Most of the results saved for the ls command are also available after ARCH estimation; see ls (p. 345) for details.
Examples
arch(4, 0, m=1000, h) sp500 c
estimates an ARCH(4) model with a mean equation consisting of the series SP500 regressed on a constant. The procedure will perform up to 1000 iterations, and will report Bollerslev-Wooldridge robust QML standard errors upon completion.
The commands:
c = 0.1
equation arc1.arch(thrsh=1, s, mean=var) @pch(nys) c ar(1)
estimate a TARCH(1, 1)-in-mean specification with the mean equation relating the percent change of NYS to a constant, an AR term of order 1, and a conditional variance (GARCH) term. The first line sets the default coefficient vector to 0.1, and the “s” option uses these values as coefficient starting values.
The command:
arch(1, 2, asy=0, parch=1.5, ged=1.2) dlog(ibm)=c(1)+c(2)* dlog(sp500) @ r
estimates a symmetric Power ARCH(2, 1) (autoregressive GARCH of order 2, and moving average ARCH of order 1) model with GED errors. The power of model is fixed at 1.5 and the GED parameter is fixed at 1.2. The mean equation consists of the first log difference of IBM regressed on a constant and the first log difference of SP500. The conditional variance equation includes an exogenous regressor R.
Following estimation, we may save the estimated conditional variance as a series named GARCH1.
arc1.makegarch garch1
Cross-references
See Chapter 20, “ARCH and GARCH Estimation”, on page 599 of the User’s Guide for a discussion of ARCH models. See also garch (p. 308) and makegarch (p. 352).