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SPSS Programming and Data Management: A Guide for SPSS and SAS Users, Second Edition documents the wealth of functionality beneath the SPSS user interface. It includes detailed examples of command syntax, the macro facility, scripting, and the output management system. The accompanying CD-ROM includes the command and data files used in the book. The book also contains a chapter for SAS users, showing equivalent SPSS code for many common data management tasks. With knowledge gained from this book, you will be able to use the many tools available within SPSS to import data from almost any source, clean it, transform it, merge it with other data, and get it into the condition required to produce reliable models and informative results.

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Question: What does Exogenous Variable Means?

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Exogenous (or exogeneous) (from the Greek words "exo" and "gen", meaning "outside" and "production") refers to an action or object coming from outside a system. It is the opposite of endogenous, something generated from within the system.

• In an economic model, an exogenous change is one that comes from outside the model and is unexplained by the model. For example, in the simple supply and demand model, a change in consumer tastes or preferences is unexplained by the model and also leads to endogenous changes in demand that lead to changes in the equilibrium price. Put another way, an exogenous change involves an alteration of a variable that is autonomous, i.e., unaffected by the workings of the model.

• In biology, "exogenous" refers to an action or object coming from the outside of a system. For example, an exogenous contrast agent in medical imaging refers to a liquid injected IV in the patient that enhances visibility of a pathology, such as a tumor.

• In biology, an exogenous factor is any material that is present and active in an individual organism or living cell but that originated outside of that organism, as opposed to an endogenous factor.
  
  • Exogenous factors in medicine include both pathogens and therapeutics.
    
  • DNA introduced to cells via transfection or viral infection (transduction) is an exogenous factor.
    
  • Carcinogens are exogenous factors.

• The word exogenous is also used in geology. Exogenous processes are all taking place at the outside of the Earth and all the other planets. Weathering, erosion, transportation and sedimentation are the main exogenous processes.

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Endogenous

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Exogenous variable:

A factor in a causal model or causal system whose value is independent from the states of other variables in the system; a factor whose value is determined by factors or variables outside the causal system under study. For example, rainfall is exogenous to the causal system constituting the process of farming and crop output. There are causal factors that determine the level of rainfall — so rainfall is endogenous to a weather model — but these factors are not themselves part of the causal model we use to explain the level of crop output. As with endogenous variables, the status of the variable is relative to the specification of a particular model and causal relations among the independent variables. An exogenous variable is by definition one whose value is wholly causally independent from other variables in the system. So the category of “ exogenous” variable is contrasted to those of “ purely endogenous” and “ partially endogenous” variables. A variable can be made endogenous by incorporating additional factors and causal relations into the model. There are causal and statistical interpretations of exogeneity. The causal interpretation is primary, and defines exogeneity in terms of the factor’s causal independence from the other variables included in the model. The statistical or econometric concept emphasizes non- correlation between the exogenous variable and the other independent variables included in the model. If xj is exogenous to a matrix of independent variables X ( excluding xj), then if we perform a regression of xj against X ( excluding xj), we should expect coefficients of 0 for each variable in X ( excluding xj). Normal regression models assume that all the independent variables are exogenous.

Engle, R. F., D. F. Hendry, and J. F. Richard. 1983. Exogeneity. Econometrica 51: 277- 304.

Pearl, Judea. 2000. Causality: models, reasoning, and inference. Cambridge: Cambridge University Press.

Woodward, James. 1995. Causation and Explanation in Econometrics. In On the Reliability of Economic Models: Essays in the Philosophy of Economics, edited by D. Little. Bostn: Kluwer Academic Publishers.

Encyclopedia of Social Science Research Methods, edited by Michael Lewis- Beck ( University of Iowa), Alan Bryman ( Loughborough University), and Tim Futing Liao. Sage Publications.

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General notion

gllamm stands for Generalized Linear Latent And Mixed Models. The primary aims of this software is to provide a maximum likelihood framework for models with unobserved components, such as multilevel models, certain latent variable models, panel data models, or models with common factors.

The official website of the software is http://www.gllamm.org maintained by one of the authors, Sophia Rabe-Hesketh. She and other authors of gllamm, Anders Skrondal and Andrew Pickles, published a book "Generalized Latent Variable Modelling".

gllamm is implemented as a system of Stata modules. To install it on your Stata, type

[此贴子已经被作者于2006-5-5 7:09:23编辑过]

This manual describes a Stata program gllamm that can estimate Generalized Linear Latent and Mixed Models (GLLAMMs). GLLAMMs are a class of multilevel latent variable models for (multivariate) responses of mixed type including continuous responses, counts, duration/survival data, dichotomous, ordered and unordered categorical responses and rankings. The latent variables (common factors or random effects) can be assumed to be discrete or to have a multivariate normal distribution. Examples of models in this class are multilevel generalized linear models or generalized linear mixed models, multilevel factor or latent trait models, item response models, latent class models and multilevel structural equation models. The program can be downloaded from http://www.gllamm.org.

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Papers using gllamm

2006

Aitkin, I. and Scott, J. (2006). The effect of missing data on covariates in survival analysis. Submitted for publication.

Andreß, H. J. (2006). Zur Entwicklung von Lebensstandard und Deprivation in Deutschland von 1996 bis 2003. Vierteljahrshefte zur Wirtschaftsforschung, 75 (1).

Bertelli, A. M. nm(2006). Motivation Crowding and the Federal Civil Servant: Evidence from the U.S. Treasury. International Public Management Journal 9 (1), 1-21.

Bottai, M., Salvati, N. and Orsini, N. (2006). Multilevel models for analyzing people抯 daily movement behavior, Journal of Geographical Systems.

Bramon, E., Dempster, E., Frangou, S., McDonald, C., Schoenberg, P., MacCabe, J. H., Walshe, M., Sham, P., Collier, D. and Murray, R. M. (2006). Is there an association between the COMT gene and P300 endophenotypes? European Psychiatry 21 (1), 70-73.

Fieldhouse, E., Pickles, A., Shyrance, N., Johnson, J. and Kingsley, P. (2006). Modeling multiparty elections, preference classes and strategic voting. Working paper, The University of Manchester, UK.

Grilli, L. and Rampichini, C. (2006). A review of random effects modelling using gllamm in Stata. In Software Reviews of Multilevel Analysis Packages. Centre for Multilevel Modelling. University of Bristol, UK.

Jæger, M. M. (2006). Welfare regimes and attitudes towards redistribution: The regime hypothesis revisited. European Sociological Review 22 (2).

Jæger, M. M. (2006). What makes people support public responsibility for welfare provision: Self-interest or political ideology? Acta Sociologica 49 (3).

Klassen, A. C., Culticlo, F., Kulldorff, N., Alberg, A. J., Platz, E. A. and Neloms, S. T. (2006). Missing stage and grade in Maryland prostate cancer surveillance data, 1992-1997. American Journal of Preventive Medicine 30 (2), S77-S87.

Knotts, H. G. and Haspel, M. (2006). The impact of gentrification on voter turnout. Social Science Quarterly 87 (1), 110-121.

Marsden, P. V., Landon, B. E., Wilson, I. B., McInnes, K., Hirschhorn, L. R., Ding, L., and Cleary, P. D. (2006). The reliability of survey assessments of characteristics of medical clinics. Health Services Research 41 (1), 265-283.

Miranda, A. and Rabe-Hesketh, S. (2006). Maximum likelihood estimation of endogenous switching and sample selection models for binary, count, and ordinal variables. Submitted for publication.

Prentice, J. C. (2006). Neighborhood effects on primary care access in Los Angeles. Social Science & Medicine 63, 1291-1303.

Rabe-Hesketh, S. and Skrondal, A. (2006). Multilevel modeling of complex survey data. Journal of the Royal Statistical Society, Series A. In press.

Rabe-Hesketh, S. and Skrondal, A. (2006). Multilevel and latent variable modelling with composite links and exploded likelihoods. Submitted for publication.

Rabe-Hesketh, S., Skrondal, A. and Zheng, X. (2006). Multilevel structural equation modeling. In S.-Y. Lee (Ed.). Handbook on Structural Structural Equation Models. Amsterdam: Elsevier. Forthcoming.

Skrondal, A. and Rabe-Hesketh, S. (2006). Models for longitudinal data. In de Leeuw, J. (Ed.) Handbook for Multilevel Analysis. New York: Springer. Forthcoming.

Solis-Trapala, J. L. and Farewell, V. T. (2006). A note on robust inference from a conditional Poisson model. Biometrical Journal 48, 117-130.

Torsheim, T., Currie, C., Boyce, W., and Samdal, O. (2006). Country material distribution and adolescents' perceived health: multilevel study of adolescents in 27 countries. Journal of Epidemiology and Community Health 60 (2), 156-161.

看到真正的高手了，向你学习

[此贴子已经被作者于2006-5-6 3:33:49编辑过]

STATA学习手册[2000]第二版+

[此贴子已经被作者于2006-5-6 3:57:11编辑过]

Stata help for xt

 help xt, help iis, help tis dialogs: iis tsset ------------------------------------------------------------------------------- 
 Title 
 [XT] xt -- Introduction to xt commands 
 
 Syntax 
 xtcmd ... [, i(varname_i) t(varname_t) ...] 
 iis [varname_i] [, clear] 
 tis [varname_i] [, clear] 
 
 Description 
 The xt series of commands provide tools for analyzing cross-sectional time-series datasets: 
 xtdes Describe pattern of xt data xtsum Summarize xt data xttab Tabulate xt data xtdata Faster specification searches with xt data 
 xtline Line plots with xt data 
 xtreg Fixed-, between- and random-effects, and population-averaged linear models xtregar Fixed- and random-effects linear models with an AR(1) disturbance xtgls Panel-data models using GLS xtpcse OLS or Prais-Winsten models with panel-corrected standard errors xtrc Random coefficients models xtivreg Instrumental variables and two-stage least squares for panel-data models xtabond Arellano-Bond linear, dynamic panel data estimator 
 xttobit Random-effects tobit models xtintreg Random-effects interval data regression models 
 xtlogit Fixed-effects, random-effects, & population-averaged logit models xtprobit Random-effects and population-averaged probit models xtcloglog Random-effects and population-averaged cloglog models 
 xtpoisson Fixed-effects, random-effects, & population-averaged Poisson models xtnbreg Fixed-effects, random-effects, & population-averaged negative binomial models 
 xtgee Population-averaged panel-data models using GEE 
 Cross-sectional time-series (longitudinal) datasets are of the form x_it, where x_it is a vector of observations for unti i and time t. The particular commands (such as xtdes, xtsum, xtreg, etc.) are documented in their own help file entries. This entry deals with concepts common across commands. 
 iis is related to the i() option on the other xt commands. Command iis or option i() sets the name of the variable corresponding to the unit index i. 
 tis is similarly related to the t() option. Command tis or option t() sets the name of the variable corresponding to the time index t. tis without an argument displays the current name of the time variable. 
 Some xt commands use time-series operators in their internal calculations, and thus require that your data be tsset; see tsset. For instance, since xtabond uses time-series operators in its internal calculations, you must tsset your data before using it. The particular help file will indicate if tsset is required before using the for the command. For these commands, iis tis are neither sufficient nor recommended. 
 Note that specifying iis or tis will clear any previous tsset settings. Also, specifying tsset will override any settings specified by iss or tis. 
 Options 
 i(varname_i) specifies the variable name corresponding to index i in xit. This must be a single, numeric variable, although whether it takes on the values 1, 2, 3 or 1, 7, 9, or even -2, 2^1/2, pi, is irrelevant. (If the identifying variable is a string, use egen's group() function to make a numeric variable from it; see egen.) 
 For instance, if the cross-sectional time-series data are of persons in the years 1991-1994, each observation is a person in one of the years; there are four observations per person (assuming no missing data). varname_i is the name of the variable that uniquely identifies the persons. 
 You can specify the i() option the first time you estimate, or you can use the iis command to set the i() beforehand. Note that it is not necessary to specify i() if the data have been previously tsset or if iis has been previously specified -- in these cases, the group variable is taken from the previous setting. 
 t(varname_t) specifies the variable name corresponding to index t in xit. This must be a single, numeric variable, although whether it takes on the values 1, 2, 3 or 1, 7, 9, or even -2, 2^1/2, pi, is irrelevant. 
 For instance, if the cross-sectional time-series data are of persons in the years 1991-1994, each observation is a person in one of the years; there are four observations per person (assuming no missing data). varname_t is the name of the variable that uniquely identifies the persons. 
 You can specify the t() option the first time you estimate, or you can use the tis command to set the t() beforehand. Note that it is not necessary to specify t() if the data have been previously tsset or if tis has been previously specified -- in these cases, the group variable is taken from the previous setting. 
 clear removes the definition of i() or t(). For instance, typing tis, clear makes Stata forget the identity of the t() variable. 
 
 Remarks 
 Once i() and t() have been specified, either by option or by the iis and tis commands, they need not be specified again except to change the variable's identity. 
 iis and tis, without arguments, list the current name of the index variable. 
 
 Example 
 An xt dataset: 
 pid yr_visit fev age sex height smokes ---------------------------------------------- 1071 1991 1.21 25 1 69 0 1071 1992 1.52 26 1 69 0 1071 1993 1.32 28 1 68 0 1072 1991 1.33 18 1 71 1 1072 1992 1.18 20 1 71 1 1072 1993 1.19 21 1 71 0 
 The other xt commands need to know the identities of the variables identifying patient and time. You could type 
 . iis pid . tis yr_visit 
 
 Also see 
 Manual: [XT] xt 
 Online: xtabond, xtcloglog, xtdata, xtdes, xtfrontier, xtgee, xtgls, xthtaylor, xtintreg, xtivreg, xtlogit, xtnbreg, xtpcse, xtpoisson, xtprobit, xtrc, xtreg, xtregar, xtsum, xttab, xttobit; tsset 

Stata help for time

 help time ------------------------------------------------------------------------------- 
 Title 
 [TS] time series -- Introduction to time-series commands} 
 
 Description 
 Some Stata commands are written directly for performing time-series analyses. This entry provides an index to these commands. 
 Many other Stata commands allow time-series operators in expressions and varlists (e.g., regress, summarize, graph, list, ...). See varlist for a table of time-series operators. 
 Before using time-series analysis commands or time-series operators, you must declare your data to be time series and indicate the time variable. This is done using the tsset command; see tsset. 
 If your interest is in analyzing cross-sectional time-series (panel) datasets, see xt. 
 
 Data management tools and time-series operators 
 haver Load data from Haver Analytics database tsset Declare a dataset to be time-series data tsfill Fill in missing times with missing observations in time-series data tsappend Add observations to a time-series dataset tsreport Report time-series aspects of a dataset or estimation sample tsrevar Time-series operator programming command 
 
 Univariate time-series 
 Estimators 
 arima Autoregressive integrated moving-average models arch Autoregressive conditional heteroskedasticity (ARCH) family of estimators newey Regression with Newey-West standard errors prais Prais-Winsten regression and Cochrane-Orcutt regression 
 
 Time-series smoothers and filters 
 tssmooth ma Moving-average filter tssmooth dexponential Double-exponential smoothing tssmooth exponential Single-exponential smoothing tssmooth hwinters Holt-Winters nonseasonal smoothing tssmooth shwinters Holt-Winters seasonal smoothing tssmooth nl Nonlinear filter 
 
 Diagnostic tools 
 corrgram Tabulate and graph autocorrelations xcorr Cross-correlogram for bivariate time series cumsp Cumulative spectral distribution pergram Periodogram dfgls DF-GLS unit-root test dfuller Augmented Dickey-Fuller unit-root test pperron Phillips-Perron unit-roots test dwstat Durbin-Watson d statistic durbina Durbin's alternative test for serial correlation bgodfrey Breusch-Godfrey test for higher-order serial correlation archlm Engle's LM test for the presence of autoregressive conditional heteroskedasticity wntestb Bartlett's periodogram-based test for white noise wntestq Portmanteau (Q) test for white noise 
 
 Multivariate time series 
 Estimators 
 var Vector autoregression models svar Structural vector autoregression models varbasic Fit a simple VAR and graph impulse-response functions vec Vector error-correction models 
 Diagnostic tools 
 varlmar Obtain LM statistics for residual autocorrelation after var or svar varnorm Test for normally distributed disturbances after var or svar varsoc Obtain lag-order selection statistics for VARs and VECMs varstable Check the stability condition of VAR or SVAR estimates varwle Obtain Wald lag-exclusion statistics after var or svar veclmar Obtain LM statistics for residual autocorrelation after vec vecnorm Test for normally distributed disturbances after vec vecrank Estimate the cointegrating rank using Johansen's framework vecstable Check the stability condition of VECM estimates 
 
 Forecasting, inference, and interpretation 
 irf create Obtain impulse-response functions and FEVDs fcast compute Compute dynamic forecasts of dependent variables after var, svar, or vec vargranger Perform pairwise Granger causality tests after var or svar 
 
 Graphs and tables 
 irf graph Graph impulse-response functions and FEVDs irf cgraph Combine graphs of impulse-response functions and FEVDs irf ograph Graph overlaid impulse-response functions and FEVDs irf table Create tables of impulse-response functions and FEVDs irf ctable Combine tables of impulse-response functions and FEVDs fcast graph Graph forecasts of dependent variables computed by fcast compute 
 
 Results management tools 
 irf add Add IRF results from one IRF file to another irf describe Describe an IRF file irf drop Drop IRF results from the active IRF file irf rename Rename an IRF result in an IRF file irf set Set the active IRF file 
 
 Also see 
 Manual: [U] 12.5.4 Time-series formats, [U] 24.3 Time-series dates, [U] 26.13 Models with time-series data, [TS] intro, [TS] time series 
 Online: tdates, tfmt, tsset, varlist, xt; and list above 

Stata Web Books
Regression with Stata
by Xiao Chen, Philip B. Ender, Michael Mitchell & Christine Wells (in alphabetical order)

The aim of these materials is to help you increase your skills in using regression analysis with Stata. This web book does not teach regression, per se, but focuses on how to perform regression analyses using Stata. It is assumed that you have had at least a one quarter/semester course in regression (linear models) or a general statistical methods course that covers simple and multiple regression and have access to a regression textbook that explains the theoretical background of the materials covered in these chapters. These materials also assume you are familiar with using Stata, for example that you have taken the ATS Stata 1 & Stata 2 classes or have equivalent knowledge of Stata. If you are a member of the UCLA community and have questions about these materials, we welcome you to send questions via email to ATSstat@ucla.edu or to visit our consulting services .

Stata help for anova

 help anova dialog: anova also see: anova postestimation ------------------------------------------------------------------------------- 
 Title 
 [R] anova -- Analysis of variance and covariance 
 
 Syntax 
 anova varname [term [/] [term [/] ...]] [if] [in] [weight] [, options] 
 where term is of the form varname[{*||}varname[...]] 
 
 options description ------------------------------------------------------------------------- Model category(varlist) variables in terms that are categorical or class class(varlist) synonym for category(varlist) continuous(varlist) variables in terms that are continuous repeated(varlist) variables in terms that are repeated-measures variables partial use partial (or marginal) sums of squares sequential use sequential sums of squares noconstant suppress constant term 
 Adv. model bse(term) between-subjects error term in repeated-measures ANOVA bseunit(varname) variable representing lowest unit in the between-subjects error term grouping(varname) grouping variable for computing pooled covariance matrix 
 Reporting regress display the regression table [no]anova display or suppress the ANOVA table detail show mapping from values to level numbers for categorical variables ------------------------------------------------------------------------- by is allowed; see prefix. aweights and fweights are allowed; see weight. See anova postestimation for features available after estimation. 
 
 Description 
 The anova command fits analysis-of-variance (ANOVA) and analysis-of-covariance (ANCOVA) models for balanced and unbalanced designs, including designs with missing cells; for repeated-measures ANOVA; and for factorial, nested, or mixed designs. anova can also be used to produce regression estimates by those who have no interest in ANOVA and ANCOVA output. 
 If you want to fit one-way ANOVA models, you may find the oneway or loneway commands more convenient; see oneway and loneway. If you are interested in MANOVA or MANCOVA, see manova. 
 
 Options 
 +-------+ ----+ Model +------------------------------------------------------------ 
 category(varlist) indicates the names of the variables in the terms that are categorical or class variables. Stata ordinarily assumes that all variables are categorical variables, so, in most cases, this option need not be specified. If you specify this option, however, the variables referenced in the terms that are not listed in category() are assumed to be continuous. Also see the class() and continuous() options. 
 class(varlist) is a synonym for category(varlist). 
 continuous(varlist) indicates the names of the variables in the terms that are continuous. Stata ordinarily assumes that all variables are categorical variables. Also see the category() and class() options. 
 repeated(varlist) indicates the names of the categorical variables in the terms that are to be treated as repeated-measures variables in a repeated-measures ANOVA or ANCOVA. 
 partial presents the ANOVA table using partial (or marginal) sums of squares. This is the default. Also see the sequential option. 
 sequential presents the ANOVA table using sequential sums of squares. 
 noconstant suppresses the constant term (intercept) from the ANOVA or regression model. 
 +------------+ ----+ Adv. model +------------------------------------------------------- 
 bse(term) indicates the between-subjects error term in a repeated-measures ANOVA. This option is needed only in the rare case when the anova command cannot automatically determine the between-subjects error term. 
 bseunit(varname) indicates the variable representing the lowest unit in the between-subjects error term in a repeated-measures ANOVA. This option is rarely needed since the anova command automatically selects the first variable listed in the between-subjects error term as the default for this option. 
 grouping(varname) indicates a variable that determines which observations are grouped together in computing the covariance matrices that will be pooled together and used in a repeated-measures ANOVA. This option is rarely needed since the anova command automatically selects the combination of all variables except the first (or as specified in the bseunit() option) in the between-subjects error term as the default for grouping observations. 
 +-----------+ ----+ Reporting +-------------------------------------------------------- 
 regress presents the regression output corresponding to the specified model. Specifying regress implies the noanova option, so if you want both the regression output and ANOVA table, you must also specify the anova option. You need not specify the regress option at the time of estimation. You can obtain the underlying regression estimates at any time by typing anova, regress. 
 [no]anova indicates that the ANOVA table be or not be displayed. The anova command typically displays the ANOVA table, and in those cases, the noanova option suppresses the display. For instance, typing anova, detail noanova would show the detail output for the last ANOVA model while suppressing the ANOVA table itself. 
 If you specify the regress option, the ANOVA table is automatically suppressed. In that case, also specifying the anova option would show both the regression output and the ANOVA table. 
 detail presents a table showing the actual values of the categorical variables along with their mapping into level numbers. You do not have to specify this option at the time of estimation. You can obtain the output at any time by typing anova, detail. 
 
 Examples 
 . anova y a . anova y a b . anova y a b a*b . anova y a b c . anova y a b c a*b a*c b*c . anova y a b c a*b a*c b*c a*b*c 
 
 ANCOVA examples 
 . anova y a b x, continuous(x) . anova y a b x z, continuous(x z) . anova y a b x a*x a*x*x, continuous(x) . anova y a b a*b x a*x z a*z, continuous(x z) 
 
 Nested ANOVA examples 
 . anova output machine / operator|machine / . anova response t / c|t / d|c|t / p|d|c|t / 
 
 Split-plot ANOVA example 
 . anova y pr / cl|pr sk pr*sk / cl*sk|pr / gr|cl*sk|pr / 
 
 Repeated measures ANOVA examples 
 . anova score person drug, repeated(drug) . anova y sub / rep|sub region / region*sub / , repeated(region) . anova y cal / sub|cal shape cal*shape , repeated(shape) . anova y n / sub|n p n*p / p*sub|n d n*d / d*sub|n p*d n*p*d, rep(p d) 
 
 Also see 
 Manual: [R] anova 
 Online: anova_postestimation; encode, reshape, loneway, oneway, regress, manova 

Stata help for clogit

 help clogit dialog: clogit also see: clogit postestimation ------------------------------------------------------------------------------- 
 Title 
 [R] clogit -- Conditional (fixed-effects) logistic regression 
 
 Syntax 
 clogit depvar [indepvars] [if] [in] [weight] , group( varname) [options] 
 options description ------------------------------------------------------------------------- Model * group(varname) matched group variable offset(varname) include varname in model with coefficient constrained to 1 constraints(constraints) apply specified linear constraints 
 SE/Robust vce(vcetype) vcetype may be oim, robust, opg, bootstrap, or jackknife robust synonym for vce(robust) cluster(varname) adjust standard errors for intragroup correlation 
 Reporting level(#) set confidence level; default is level(95) or report odds ratios 
 Max options maximize_options control the maximization process; seldom used ------------------------------------------------------------------------- * group(varname) is required. bootstrap, by, jackknife, nestreg, rolling, statsby, stepwise, and xi are allowed; see prefix. fweights and iweights are allowed; see weight, but they are interpreted to apply to groups as a whole, not to individual observations. See clogit postestimation for features available after estimation. 
 
 Description 
 clogit fits what biostatisticians and epidemiologists call conditional logistic regression for matched case-control groups and what economists and other social scientists call fixed-effects logit for panel data. It also fits McFadden's choice model. Computationally, these models are exactly the same. 
 See logistic estimation commands for a list of related estimation commands. 
 
 Options 
 +-------+ ----+ Model +------------------------------------------------------------ 
 group(varname) is required; it specifies an identifier variable (numeric or string) for the matched groups. strata(varname) is a synonym for group(). 
 offset(varname); see estimation options. 
 constraints(numlist); see estimation options. 
 +-----------+ ----+ SE/Robust +-------------------------------------------------------- 
 vce(vcetype); see vce_option. 
 robust, cluster(varname); see estimation options. 
 +-----------+ ----+ Reporting +-------------------------------------------------------- 
 level(#); see estimation options. 
 or reports the estimated coefficients transformed to odds ratios, i.e., exp(b) rather than b. Standard errors and confidence intervals are similarly transformed. This option affects how results are displayed, not how they are estimated. or may be specified at estimation or when replaying previously estimated results. 
 +-------------+ ----+ Max options +------------------------------------------------------ 
 maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, hessian, gradient, showstep, tolerance(#), ltolerance(#), gtolerance(#), nrtolerance(#), nonrtolerance, from(init_specs); see maximize. These options are seldom used. 
 
 Examples 
 Matched case-control data 
 . clogit low lwt nonwhite smoke ptd, group(pairid) 
 . clogit low lwt nonwhite smoke ptd, group(groupid) 
 . clogit low lwt nonwhite smoke ptd, group(groupid) or 
 Fixed-effects logit 
 . clogit union age grade not_smsa, group(idcode) 
 McFadden's choice model 
 . clogit promoted tenureb fracatt female, group(poolid) 
 
 Also see 
 Manual: [R] clogit 
 Online: clogit postestimation; cloglog, constraint, logistic, logit, mlogit, nlogit, ologit, oprobit, probit, scobit, xtcloglog, xtgee, xtlogit, xtprobit 
 

Stata help for multivariate

 help multivariate ------------------------------------------------------------------------------- 
 Title 
 [MV] multivariate -- Introduction to multivariate commands 
 
 Description 
 This overview organizes and presents the multivariate commands conceptually, that is, according to the similarities in the functions that they perform. 
 The help files listed under the heading Cluster analysis give an introduction to commands that perform cluster analysis on variables or the similarity or dissimilarity values within a matrix. 
 The commands listed under the heading Factor analysis and principal component analysis provide factor analysis of a correlation matrix and principal component analysis (PCA) of a correlation or covariance matrix. The correlation or covariance matrix can be provided directly or computed from variables. 
 The commands listed under the heading Rotation provide methods for rotating a factor or PCA solution or for rotating a matrix. Also provided is Procrustean rotation analysis for rotating a set of variables to best match another set of variables. 
 The commands listed under Multivariate analysis of variance and related techniques provide canonical correlation analysis, multivariate regression, multivariate analysis of variance (MANOVA), and comparison of multivariate means. 
 The commands listed under Multidimensional scaling and biplots provide classic MDS and two-dimensional biplots. MDS can be performed on the variables or on proximity data in a matrix or as proximity data in long format. 
 The commands listed under Correspondence analysis provide simple correspondence analysis (CA) on the cross-tabulation of two categorical variables or on a matrix. 
 
 Cluster analysis 
 * cluster Introduction to cluster analysis commands * clustermat Introduction to clustermat commands matrix dissimilarity Compute similarity or dissimilarity measures; may be used by clustermat * Contains a list of commands 
 Factor analysis and principal component analysis 
 factor Factor analysis factor postestimation Postestimation tools for factor and factormat pca Principal component analysis pca postestimation Postestimation tools for pca and pcamat rotate Orthogonal and oblique rotations after factor and pca screeplot Scree plot of eigenvalues scoreplot Score and loading plots after factor and pca 
 Rotation 
 rotate Orthogonal and oblique rotations after factor and pca rotatemat Orthogonal and oblique rotation of a Stata matrix procrustes Procrustes transformation procrustes postestimation Postestimation tools for procrustes 
 Multivariate analysis of variance and related techniques 
 canon Canonical correlations canon postestimation Postestimation tools for canon mvreg Multivariate regression mvreg postestimation Postestimation tools for mvreg manova Multivariate analysis of variance and covariance manova postestimation Postestimation tools for manova hotelling Hotelling's T-squared generalized means test 
 Multidimensional scaling and biplots 
 mds Multidimensional scaling for twoway data mds postestimation Postestimation tools for mds, mdsmat, and mdslong mdslong Multidimensional scaling of proximity data in long format mdsmat Multidimensional scaling of proximity data in a matrix biplot Biplots 
 Correspondence analysis 
 ca Simple correspondence analysis ca postestimation Postestimation tools for ca and camat 
 
 Also see 
 Manual: [MV] multivariate 
 Online: cluster, clustermat, matrix dissimilarity, factor, factor postestimation, pca, pca postestimation, rotate, screeplot, scoreplot, rotatemat, procrustes, procrustes postestimation, canon, canon postestimation, mvreg, mvreg postestimation, manova, manova postestimation, hotelling, mds, mds postestimation, mdslong, mdsmat, biplot, ca, ca postestimation 

生成2阶以上的滞后项

gen y_2=l2.y

gen y_3=l3.y

Statachen，上面的书你都有电子版？你有Maximum Likelihood Estimation with Stata, 急需阿