NEW I've added some new material for spatial econometric analysis.
For feedback please email me at email@example.com
This command implements the Pesaran and Smith (1995) Mean Group (MG) estimator, the Pesaran (2006) Common Correlated Effects Mean Group (CCEMG) estimator and the Augmented Mean Group (AMG) estimator introduced here and discussed and tested here. For these macro panel estimators the cross-panel average estimates can be reported as unweighted or outlier-robust means, where the latter is implemented via the rreg command. Optionally the routine reports the group-specific regression results and produces residuals and predicted values based on these. The example in the helpfile links to a dataset (+ related working paper) and goes through all the options of the command. This code is applied in our REStat paper on R&D spillovers. Other illustrations with data (including a Stata Journal article) can be found here.
This command runs the Maddala and Wu (1999) as well as the Pesaran (2007) panel unit root tests for multiple variables and lags. This is not a new command for these panel unit root tests but a convenient tool using the existing xtfisher and pescadf commands written by Scott Merryman and Piotr Lewandowski respectively (both commands need to be installed for multipurt to work). In the future this will hopefully also include the CIPSM panel unit root test by Pesaran, Smith and Yamagata (2009). The example in the helpfile links to a dataset (+ related working paper) and goes through all the options of the command. Further illustrations with data can be found here.
This command implements the Pesaran (2004) CD test for cross-section dependence in panel time-series data. The routine builds on the xtcsd command by De Hoyos and Sarafidis (2006) but is not a post-estimation command: xtcd can be applied to variables as well as to residuals, provided the latter have been previously computed as a separate variable series (using for instance the xtmg command). Furthermore, in the multipurt spirit up to 9 variable or residuals series can be tested together. Further illustrations with data can be found here.
Please note: This version and the one on SSC has a bug whereby the average number of observations per correlation as well as the balancedness/unbalancedness of the panel are wrong. This will be corrected in the forthcoming Mata version of the routine.
Investigating heterogeneous panel cointegration with an error correction test - ado-file, help-fileVersion: 1.0.0 still in planning
This command implements the error correction based cointegration test by Gengenbach, Urbain and Westerlund (2009), which builds on earlier work by Westerlund (2007). The test investigates error correction at the group-level (e.g. country) and can account for cross-section dependence.
Version: 1.0.0 still in planning
This command implements the Pesaran, Smith and Yamagata (2009) panel unit root test. It extends the cross sectionally augmented panel unit root test proposed by Pesaran (2007) to the case of a multifactor error structure. The basic idea is to exploit information regarding the unobserved factors that are shared by other time series in addition to the variable under consideration. The test procedure only requires specification of the maximum number of factors, in contrast to other panel unit root tests based on principal components that require in addition the estimation of the number of factors as well as the factors themselves.
* The Bai et al (2009) CupBC and CupFM estimators
* The Bai & Kao (2006) estimator
* The Bai & Ng (2002) procedure to determine relevant number of factors
* The Bai & Ng (2004) PANIC attack
* The Mark & Sul (2003) Panel DOLS
* The Pedroni (1999, 2004) cointegration tests
* The Pedroni (2004) Group Mean FMOLS estimator
xtpatternvar which gives you insights into the unbalancedness of the panel and which observations are missing. Missing observations are a common feature of macro panel data, so maybe also have a look at the paper by Ron Smith and Ali Tasiran on 'Random coefficients models of arms imports' in Economic Modelling, 2010, Vol.27(6).
xtunitroot breitung) is implemented in Stata 11; requires a strongly balanced panel. Note that the help file for xtunitroot provides a nice overview of all the tests.
* The Hadri (2000) panel unit root/stationarity test (xtunitroot hadri) is implemented in Stata 11; requires a strongly balanced panel. Prior to this version you can use the command written by Kit Baum (hadrilm).
* The Harris & Tzavalis (1999) panel unit root/stationarity test (xtunitroot ht) is implemented in Stata 11; requires a balanced panel.
* The Im, Pesaran & Shin (1997, 2003) panel unit root/stationarity test (ipshin) was coded by Fabian Bornhorst and Kit Baum; country series cannot have gaps. This is also implement in Stata 11 (xtunitroot ips).
* The Levin, Lin & Chu (1992, 2002) panel unit root/stationarity test (levinlin) was coded by Fabian Bornhorst and Kit Baum; country series cannot have gaps. This is also implement in Stata 11 (xtunitroot llc).
* The Maddala & Wu (1999) panel unit root/stationarity test (xtfisher) was coded by Scott Merryman. The option 'pp' implements the Phillips and Perron (1988) test at the country-level instead. Both of these options are also implement in Stata 11 (xtunitroot fisher).
* The Pesaran (2006) CIPS panel unit root/stationarity test (pescadf) was coded by Piotr Lewandowski.
xtwest) was coded by Damiaan Persyn; it requires that the country series do not have gaps but as a very useful feature tells the user which country series violate this requirement.
xtcsd). This command operates as a post-estimation command following xtreg, fe or re. See xtcd above for a more flexible procedure.
* A number of tools for spatial econometric analysis including Moran's (1950) I statistic were written by Maurizio Pisati (this covers a number of commands, most conveniently found via -findit spatial- or for a description the Stata Bulletin 60, sg162). A new set of spatial estimators for Stata (spmlreg) has been developed by P. Wilner Jeanty, with David Drukker et al also chipping in rather substantially for cross-section analysis (sppack). Note that for all spatial econometric analysis it is required that you specify a spatial weight matrix. The Pisati commands allow you to do so provided you have relevant data.
Stata Bulletin 60, sg162, free access). A new set of spatial estimators for Stata (spmlreg) has recently been created by P. Wilner Jeanty, with Stata's David Drukker et al also chipping in rather substantially for cross-section analysis (sppack). Note that for all spatial econometric analysis it is required that you specify a spatial weight matrix. The Pisati commands allow you to do so provided you have relevant data. Further spatial commands in Stata include spagg (something to do with dyadic data), spautoc (Moran and Geary measures of spatial correlation), spgrid (creating two-dimensional grids), sphdist (spherical distance), splagvar (create spatial lags and other tools), spmon (monadic data), spseudor2 (create measure for goodness-of-fit), spspc and spundir (more dyadic data tools), spwmatfill and spwmatrix (tools for creation/import/export of spatial weight matrices). Franzese, Hays and Kachi (2010) also have code for their m-star estimator (multiparametric spatiotemporal autoregressive model).
There are some new resources for spatial econometrics provided in the form of a talk by Maurizio Pisati at the 2012 German Stata Users Group, as well as two talks to be given at the Spanish Stata User Group meeting later this year (September 2012). In general it's worth your time looking through the materials for past meetings.
Tim Conley at Western Ontario provides detailed code for his work on GMM estimation with cross-sectional dependence.
NEW Federico Belotti, Gordon Hughes and Andrea Piano Mortari have created a suite of fixed and random-effects spatial models for balanced panel data in form of the xsmle command. This includes Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), Spatial Durbin Model (SDM), Spatial Autoregressive Model with Autoregressive Disturbances (SAC), Generalized Spatial Random-effects Model (GSPRE). The command can compute direct, indirect and total spatial effects (LeSage, 2008) and can also transform the data to accommodate fixed effects. With the -mi- prefix command this estimator can also be used for unbalanced panels.
xtdolshm; you will also need to install ltimbimata. Both can be found via -ssc install- or -findit- in Stata. Make sure you have the latest version of this command, since Diallo has recently ironed out a bug related to the computation of the confidence intervals. Also: email him rather than me if you have any questions about this command!
* The Swamy (1970) Random Coefficient Model (RCM) estimator is implemented in Stata (xtrc). The special edition of Economic Modelling on P.A.V.B Swamy should be useful for anybody using the RCM estimator: Volume 27, Issue 6, November 2010.
'Untested Assumptions and Data Slicing: A Critical Review of Firm-Level Production Function Estimators' with Christian Helmers. The Stata and R code for this paper will be posted here in due course. Since we're working on a book manuscript on firm-level productivity analysis (with Ralf Martin) there'll be a dedicated website with all the code through the publishers Oxford University Press, most likely some time after Summer 2013. If you're keen to use our ACF code... read the paper and/or wait till then.