I have read an article in Wall Street Journal about IPO, it said that because of low IPO, investment banks earn much less. I am a guy interested economics recently, so it is hard for me to understand in what way IPO is related with investment banks. I hope there are experts who can offer a better explanation.
Data management tools and time series operators tsset Declare data to be time-series data tsfill Fill in gaps in time variable 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 haver Load data from Haver Analytics database rolling Rolling-window and recursive estimation datetime business calendars User-definable business calendars Univariate time series Estimators 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 arch Autoregressive conditional heteroskedasticity (ARCH) family of estimators arch postestimation Postestimation tools for arch newey Regression with Newey–West standard errors newey postestimation Postestimation tools for newey prais Prais–Winsten and Cochrane–Orcutt regression prais postestimation Postestimation tools for prais ucm Unobserved-components model ucm postestimation Postestimation tools for ucm Timeseries smoothers and filters 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 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 psdensity Parametric spectral density estimation dfgls DF-GLS unit-root test dfuller Augmented Dickey–Fuller unit-root test pperron Phillips–Perron unit-root test regress postestimation time series Postestimation tools for regress with time series wntestb Bartlett’s periodogram-based test for white noise wntestq Portmanteau (Q) test for white noise autoregressive models Multivariate time series Estimators dfactor Dynamic-factor models dfactor postestimation Postestimation tools for dfactor 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 sspace State-space models sspace postestimation Postestimation tools for sspace var Vector autoregressive models var postestimation Postestimation tools for var var svar Structural vector autoregressive models var svar postestimation Postestimation tools for svar varbasic Fit a simple VAR and graph IRFs or FEVDs varbasic postestimation Postestimation tools for varbasic vec Vector error-correction models vec postestimation Postestimation tools for vec Diagnostic tools varlmar Perform LM test for residual autocorrelation varnorm Test for normally distributed disturbances 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 veclmar Perform LM test for residual autocorrelation vecnorm Test for normally distributed disturbances vecrank Estimate the cointegrating rank of a VECM vecstable Check the stability condition of VECM estimates Forecasting, inference, and interpretation irf create Obtain IRFs, dynamic-multiplier functions, and FEVDs fcast compute Compute dynamic forecasts of dependent variables vargranger Perform pairwise Granger causality tests Graphs and tables corrgram Tabulate and graph autocorrelations xcorr Cross-correlogram for bivariate time series pergram Periodogram irf graph Graph IRFs, dynamic-multiplier functions, and FEVDs irf cgraph Combine graphs of IRFs, dynamic-multiplier functions, and FEVDs irf ograph Graph overlaid IRFs, dynamic-multiplier functions, and FEVDs irf table Create tables of IRFs, dynamic-multiplier functions, and FEVDs irf ctable Combine tables of IRFs, dynamic-multiplier functions, and FEVDs fcast graph Graph forecasts of variables computed by fcast compute tsline Plot time-series data varstable Check the stability condition of VAR or SVAR estimates vecstable Check the stability condition of VECM estimates wntestb Bartlett’s periodogram-based test for white noise Results management tools irf add Add results from an IRF file to the active IRF file 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
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