Stata 15 help for tssmooth shwinters

[TS] tssmooth shwinters -- Holt-Winters seasonal smoothing

Syntax

tssmooth shwinters [type] newvar = exp [if] [in] [, options]

options Description ------------------------------------------------------------------------- Main replace replace newvar if it already exists parms(#a #b #g) use #a, #b, and #g as smoothing parameters samp0(#) use # observations to obtain initial values for recursions s0(#cons #1t) use #cons and #lt as initial values for recursions forecast(#) use # periods for the out-of-sample forecast period(#) use # period of the seasonality additive use additive seasonal Holt-Winters method

Options sn0_0(varname) use initial seasonal values in varname sn0_v(newvar) store estimated initial values for seasonal terms in newvar snt_v(newvar) store final year's estimated seasonal terms in newvar normalize normalize seasonal values altstarts use alternative method for computing the starting values

Maximization maximize_options control the maximization process; seldom used from(#a #b #g) use #a, #b, and #g as starting values for the parameters ------------------------------------------------------------------------- You must tsset your data before using tssmooth shwinters; see [TS] tsset. exp may contain time-series operators; see tsvarlist.

Menu

Statistics > Time series > Smoothers/univariate forecasters > Holt-Winters seasonal smoothing

Description

tssmooth shwinters performs the seasonal Holt-Winters method on a user-specified expression, which is usually just a variable name, and generates a new variable containing the forecasted series.

Options

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

replace replaces newvar if it already exists.

parms(#a #b #g), 0 < #a < 1, 0 < #b < 1, and 0 < #g < 1, specifies the parameters. If parms() is not specified, the values are chosen by an iterative process to minimize the in-sample sum-of-squared prediction errors.

If you experience difficulty converging (many iterations and "not concave" messages), try using from() to provide better starting values.

samp0(#) and s0(#cons #lt) have to do with how the initial values #cons and #lt for the recursion are obtained.

s0(#cons #lt) specifies the initial values to be used.

samp0(#) specifies that the initial values be obtained using the first # observations of the sample. This calculation is described under Methods and formulas in [TS] tssmooth shwinters and depends on whether the altstart and additive options are also specified.

If neither option is specified, the first half of the sample is used to obtain initial values.

forecast(#) specifies the number of periods for the out-of-sample prediction; 0 < # < 500. The default is forecast(0), which is equivalent to not performing an out-of-sample forecast.

period(#) specifies the period of the seasonality. If period() is not specified, the seasonality is obtained from the tsset options daily, weekly, ..., yearly. If you did not specify one of those options when you tsset the data, you must specify the period() option. For instance, if your data are quarterly and you did not specify tsset's quarterly option, you must now specify period(4).

By default, seasonal values are calculated, but you may specify the initial seasonal values to be used via the sn0_0(varname) option. The first period() observations of varname are to contain the initial seasonal values.

additive uses the additive seasonal Holt-Winters method instead of the default multiplicative seasonal Holt-Winters method.

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

sn0_0(varname) specifies the initial seasonal values to use. varname must contain a complete year's worth of seasonal values, beginning with the first observation in the estimation sample. For example, if you have monthly data, the first 12 observations of varname must contain nonmissing data. sn0_0() cannot be used with sn0_v().

sn0_v(newvar) stores in newvar the initial seasonal values after they have been estimated. sn0_v() cannot be used with sn0_0().

snt_v(newvar) stores in newvar the seasonal values for the final year's worth of data.

normalize specifies that the seasonal values be normalized. In the multiplicative model, they are normalized to sum to one. In the additive model, the seasonal values are normalized to sum to zero.

altstarts uses an alternative method to compute the starting values for the constant, the linear, and the seasonal terms. The default and the alternative methods are described in Methods and formulas in [TS] tssmooth shwinters. altstarts may not be specified with s0().

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options controls the process for solving for the optimal alpha, beta, and gamma when the parms() option is not specified.

maximize_options: nodifficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), and nonrtolerance; see [R] maximize. These options are seldom used.

from(#a #b #g), 0 < #a < 1, 0 < #b < 1, and 0 < #g < 1, specifies starting values from which the optimal values of alpha, beta, and gamma will be obtained. If from() is not specified, from(.5 .5 .5) is used.

Examples

Setup . webuse turksales

Perform Holt-Winters seasonal smoothing on sales . tssmooth shwinters shw1=sales

Same as above, but perform out-of-sample forecast using 4 periods . tssmooth shwinters shw2=sales, forecast(4)

Same as above, but use additive seasonal Holt-Winters method . tssmooth shwinters shw3=sales, forecast(4) additive

Same as above, but normalize seasonal values . tssmooth shwinters shw4=sales, forecast(4) additive normalize

Same as above, but store final year's estimated seasonal terms in seas . tssmooth shwinters shw5=sales, forecast(4) additive normalize snt_v(seas)

Stored results

tssmooth shwinters stores the following in r():

Scalars r(N) number of observations r(alpha) alpha smoothing parameter r(beta) beta smoothing parameter r(gamma) gamma smoothing parameter r(prss) penalized sum-of-squared errors r(rss) sum-of-squared errors r(rmse) root mean squared error r(N_pre) number of seasons used in calculating starting values r(s2_0) initial value for linear term r(s1_0) initial value for constant term r(linear) final value of linear term r(constant) final value of constant term r(period) period, if filter is seasonal

Macros r(method) shwinters, additive or shwinters, multiplicative r(normalize) normalize, if specified r(exp) expression specified r(timevar) time variable specified in tsset r(panelvar) panel variable specified in tsset


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