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Time-Series Reference Manual

Publisher:  Stata Press
Copyright:  2013
ISBN-13:  978-1-59718-127-3
Pages:  790
 
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Table of contents

intro Introduction to time-series manual
time series Introduction to time-series commands
 
arch Autoregressive conditional heteroskedasticity (ARCH) family of estimators
arch postestimation Postestimation tools for arch
arfima Autoregressive fractionally integrated moving-average models
arfima postestimation Postestimation tools for arfima
arima ARIMA, ARMAX, and other dynamic regression models
arima postestimation Postestimation tools for arima
 
corrgram Tabulate and graph autocorrelations
cumsp Cumulative spectral distribution
 
dfactor Dynamic-factor models
dfactor postestimation Postestimation tools for dfactor
dfgls DF-GLS unit-root test
dfuller Augmented Dickey–Fuller unit-root test
 
fcast compute Compute dynamic forecasts of dependent variables after var, svar, or vec
fcast graph Graph forecasts of variables computed by fcast compute
forecast Econometric model forecasting
forecast adjust Adjust a variable by add factoring, replacing, etc.
forecast clear Clear current model from memory
forecast coefvector Specify an equation via a coefficient vector
forecast create Create a new forecast model
forecast describe Describe features of the forecast model
forecast drop Drop forecast variables
forecast estimates Add estimation results to a forecast model
forecast exogenous Declare exogenous variables
forecast identity Add an identity to a forecast model
forecast list List forecast commands composing current model
forecast query Check whether a forecast model has been started
forecast solve Obtain static and dynamic forecasts
 
irf Create and analyze IRFs, dynamic-multiplier functions, and FEVDs
irf add Add results from an IRF file to the active IRF file
irf cgraph Combine graphs of IRFs, dynamic-multiplier functions, and FEVDs
irf create Obtain IRFs, dynamic-multiplier functions, and FEVDs
irf ctable Combine tables of IRFs, dynamic-multiplier functions, and FEVDs
irf describe Describe an IRF file
irf drop Drop IRF results from the active IRF file
irf graph Graph IRFs, dynamic-multiplier functions, and FEVDs
irf ograph Graph overlaid IRFs, dynamic-multiplier functions, and FEVDs
irf rename Rename an IRF result in an IRF file
irf set Set the active IRF file
irf table Create tables of IRFs, dynamic-multiplier functions, and FEVDs
 
mgarch Multivariate GARCH models
mgarch ccc Constant conditional correlation multivariate GARCH models
mgarch ccc postestimation Postestimation tools for mgarch ccc
mgarch dcc Dynamic conditional correlation multivariate GARCH models
mgarch dcc postestimation Postestimation tools for mgarch dcc
mgarch dvech Diagonal vech multivariate GARCH models
mgarch dvech postestimation Postestimation tools for mgarch dvech
mgarch vcc Varying conditional correlation multivariate GARCH models
mgarch vcc postestimation Postestimation tools for mgarch vcc
 
newey Regression with Newey–West standard errors
newey postestimation Postestimation tools for newey
 
pergram Periodogram
pperron Phillips–Perron unit-root test
prais Prais–Winsten and Cochrane–Orcutt regression
prais postestimation Postestimation tools for prais
psdensity Parametric spectral density estimation after arima, arfima, and ucm
 
rolling Rolling-window and recursive estimation
 
sspace State-space models
sspace postestimation Postestimation tools for sspace
 
tsappend Add observations to a time-series dataset
tsfill Fill in gaps in time variable
tsfilter Filter a time-series, keeping only selected periodicities
tsfilter bk Baxter–King time-series filter
tsfilter bw Butterworth time-series filter
tsfilter cf Christiano–Fitzgerald time-series filter
tsfilter hp Hodrick–Prescott time-series filter
tsline Plot time-series data
tsreport Report time-series aspects of a dataset or estimation sample
tsrevar Time-series operator programming command
tsset Declare data to be time-series data
tssmooth Smooth and forecast univariate time-series data
tssmooth dexponential Double-exponential smoothing
tssmooth exponential Single-exponential smoothing
tssmooth hwinters Holt–Winters nonseasonal smoothing
tssmooth ma Moving-average filter
tssmooth nl Nonlinear filter
tssmooth shwinters Holt–Winters seasonal smoothing
 
ucm Unobserved-components models
ucm postestimation Postestimation tools for ucm
 
var intro Introduction to vector autoregression models
var Vector autoregression models
var postestimation Postestimation tools for var
var svar Structural vector autoregression models
var svar postestimation Postestimation tools for svar
varbasic Fit a simple VAR and graph IRFs or FEVDs
varbasic postestimation Postestimation tools for varbasic
vargranger Perform pairwise Granger causality tests after var or svar
varlmar Perform LM test 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
vec intro Introduction to vector error-correction models
vec Vector error-correction models
vec postestimation Postestimation tools for vec
veclmar Perform LM test for residual autocorrelation after vec
vecnorm Test for normally distributed disturbances after vec
vecrank Estimate the cointegrating rank of a VECM
vecstable Check the stability condition of VECM estimates
 
wntestb Barlett's periodogram-based test for white noise
wntestq Portmanteau (Q) test for white noise
 
xcorr Cross-correlogram for bivariate time series
 
Glossary
 
Subject and author index
 
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