Syntax - Stata

2cluster kmeans and kmedians— Kmeans and kmedians cluster analysis Options Main k(#) is required and indicates that # groups are to be formed by the cluster analysis.

  Syntax

Syntax Description - Stata

2substr()— Extract substring Diagnostics In substr(s, b, l) and substr(s, b), if b describes a position before the beginning of the string or after the end, "" is returned.

  Descriptions, Stata, Syntax, Syntax description

Title stata.com generate — Create or change …

Title stata.com generate — Create or change contents of variable SyntaxMenuDescriptionOptions Remarks and examplesMethods and formulasReferencesAlso see Syntax Create new variable generate type newvar

  Change, Content, Variable, Create, Stata, Generate, Generate create or change, Generate create or change contents of variable

SyntaxDescriptionRemarks and examplesAlso see

macro— Macro definition and manipulation 3 Macro extended functions related to matrices rownamesjcolnamesjrowfullnamesjcolfullnames matname roweqjcoleq

  Syntaxdescriptionremarks and examplesalso see, Syntaxdescriptionremarks, Examplesalso

Title stata.com graph box — Box plots

graph box— Box plots 3 Menu Graphics > Box plot Syntax graph box yvars if in weight, options graph hbox yvars if in weight, options where yvars is a varlist options Description

  Plot, Box plot

Title stata

area options — Options for specifying the look of special areas 3 Also see [G-2] graph dot — Dot charts (summary statistics)

  Title, Stata, Title stata

www.stata.com

2cluster dendrogram— Dendrograms for hierarchical cluster analysis The height of the vertical lines and the range of the (dis)similarity axis give visual clues about the

Title stata.com putexcel — Export results to an Excel …

putexcel— Export results to an Excel file 3 export options Description Main overwritefmt overwrite existing cell formatting when exporting new content

  Putexcel

destring — Convert string variables to numeric ... - …

Title stata.com destring — Convert string variables to numeric variables and vice versa SyntaxMenuDescription Options for destringOptions for tostringRemarks and …

  Title, Variable, Stata, Numeric, String, Convert, Title stata, Destring convert string variables to numeric, Destring

Title stata.com gettoken — Low-level parsing

Title stata.com gettoken — Low-level parsing SyntaxDescriptionOptionsRemarks and examplesAlso see Syntax gettoken emname1 emname2: emname3, parse("pchars") quotes qed(lmacname) match(lmacname) bind

  Title, Stata, Title stata

GENERALIZED AUTOREGRESSIVE CONDITIONAL …

Autoregressive Conditional Heteroskedastic), is introduced, allowing for a much more flexible lag structure. The extension of the ARCH process to the GARCH process bears much resemblance to the extension of the standard time series AR process to the general ARMA process and, as is argued below,

  Generalized, Conditional, Autoregressive, Generalized autoregressive conditional, Autoregressive conditional

18 GARCH Models - University of Washington

ARCH is an acronym meaning AutoRegressive Conditional Heteroscedas-ticity. In ARCH models the conditional variance has a structure very similar to the structure of the conditional expectation in an AR model. We flrst study the ARCH(1) model, which is the simplest GARCH model and similar to an AR(1) model.

  University, Washington, University of washington, Garch, Conditional, Autoregressive, Autoregressive conditional

XLNet: Generalized Autoregressive Pretraining for Language ...

conditional distribution. Since an AR language model is only trained to encode a uni-directional con-text (either forward or backward), it is not effective at modeling deep bidirectional contexts. On the ... the autoregressive objective also provides a natural way to …

  Conditional, Autoregressive, Xlnet

Econometric Modelling of Markov-Switching Vector ...

the process might be time-invariant conditional on an unobservable regime variable s t which indicates the regime prevailing at time t.Let M denote the number of feasible regimes, so that s t 2f 1 ... In Markov-switching vector autoregressive (MS-VAR) models – the subject of this study – it is assumed that the regime s t

  Conditional, Autoregressive

Labels to Street Scene Labels to Facade BW to Color - arXiv

and, indeed, images. The image-conditional models have tackled image prediction from a normal map [55], future frame prediction [40], product photo generation [59], and image generation from sparse annotations [31,48] (c.f. [47] for an autoregressive approach to the same problem). Sev-eral other papers have also used GANs for image-to-image

  Conditional, Autoregressive

Conditional Image Synthesis with Auxiliary Classifier GANs

et al.,2016)). Autoregressive models dispense with latent variables and directly model the conditional distribution over pixels (van den Oord et al.,2016a;b). These models produce convincing samples but are costly to sample from and do not provide a latent representation. Invertible den-sity estimators transform latent variables directly using a

  Conditional, Autoregressive

Vector Autoregressive Models for Multivariate Time Series

Vector Autoregressive Models for Multivariate Time Series 11.1 Introduction The vector autoregression (VAR) model is one of the most successful, flexi-ble, and easy to use models for the analysis of multivariate time series. It is a natural extension of the univariate autoregressive model to dynamic mul-tivariate time series.

  Autoregressive

Title stata.com arima — ARIMA, ARMAX, and other dynamic ...

arima— ARIMA, ARMAX, and other dynamic regression models 3. arima D.y, ar(1/2) ma(1/3) is equivalent to. arima y, arima(2,1,3) The latter is easier to write for simple ARMAX and ARIMA models, but if gaps in the AR or MA lags are to be modeled, or if different operators are to be applied to independent variables, the

  Model, Dynamics, Other, Regression, Ramax, And other dynamic regression models