@article{AMEM:71,
	author={T. Amemiya},
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	volume={12},
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	pages={1--13}
}

@article{ANDE:HSIA:81,
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	year={1981},
	pages={598--606}
}
@Article{ANDE:HSIA:82,
  author = 	 {T.W. Anderson and C. Hsiao},
  title = 	 {Formulation and estimation of dynamic models using panel data},
  journal = 	 {Journal of Econometrics},
  year = 	 {1982},
  OPTkey = 	 {},
  volume = 	 {18},
  OPTnumber = 	 {},
  pages = 	 {47-82},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@article{AREL:BOND:91,
	author={M. Arellano and S. Bond},
	title={Some Tests of Specification for Panel Data : Monte Carlo Evidence and an
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	volume={58},
	year={1991},
	pages={277--297}
}

@book{AREL:03,
	author={M. Arellano},
	title={Panel Data Econometrics},
	publisher={Oxford University press},
	year={2003}
}

@article{BALE:VARA:87,
	author={P. Balestra and J. Varadharajan--Krishnakumar},
	title={Full Information Estimations of a System of Simultaneous Equations With
		Error Components},
	journal={Econometric Theory},
	volume={3},
	year={1987},
	pages={223--246}
}

@article{BALT:81,
	author={B.H Baltagi},
	title={Simultaneous Equations With Error Components},
	journal={Journal of Econometrics},
	volume={17},
	year={1981},
	pages={21--49}
}

@book{BALT:01,
	author={B.H. Baltagi},
	title={Econometric Analysis of Panel Data},
	publisher={John Wiley and Sons ltd},
	year={2001},
	edition={3rd}
}

@book{BALT:03,
	author={B.H. Baltagi},
	title={Econometric Analysis of Panel Data},
	publisher={John Wiley and Sons ltd},
	year={2003},
	edition={4th}
}

@Book{PFAFF:08,
    title = {Analysis of Integrated and Cointegrated Time Series with R},
    author = {Bernhard Pfaff},
    publisher = {Springer},
    edition = {Second},
    address = {New York},
    year = {2008},
    note = {ISBN 0-387-27960-1},
    url = {https://CRAN.r-project.org/package=urca},
}

@Book{SEVE:02,
  author = 	 {Patrick Sevestre},
  ALTeditor = 	 {},
  title = 	 {Econom\'etrie des donn\'ees de panel},
  publisher = 	 {Dunod},
  year = 	 {2002},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTaddress = 	 {},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@Book{PIRO:11,
  author = 	 {Alain Pirotte},
  ALTeditor = 	 {},
  title = 	 {Econom\'etrie des donn\'ees de panel},
  publisher = 	 {Economica},
  year = 	 {2011},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTaddress = 	 {},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Book{HSIA:03,
  author = 	 {C. Hsiao},
  ALTeditor = 	 {},
  title = 	 {Analysis of Panel Data},
  publisher = 	 {Cambridge University Press},
  year = 	 {2003},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address = 	 {Cambridge},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@book{HSIA:14,
place={Cambridge},
edition={3},
series={Econometric Society Monographs},
title={Analysis of Panel Data},
DOI={10.1017/CBO9781139839327},
publisher={Cambridge University Press},
author={Hsiao, Cheng},
year={2014},
collection={Econometric Society Monographs}
}

@article{BLUN:BOND:98,
	author={R. Blundell and S. Bond},
	title={Initial Conditions and Moment Restrictions in Dynamic
                  Panel Data Models},
	journal={Journal of Econometrics},
	volume={87},
	year={1998},
	pages={115--143}
}

@article{BREU:PAGA:80,
	author={T.S. Breusch and A.R. Pagan},
	title={The Lagrange Multiplier Test and Its Applications to Model Specification
		in Econometrics},
	journal={Review of Economic Studies},
	volume={47},
	year={1980},
	pages={239--253}
}

@article{CRIB:04,
	author={F. Cribari--Neto},
	title={Asymptotic Inference Under Heteroskedasticity of Unknown Form},
	journal={Computational Statistics & Data Analysis},
	volume={45},
	year={2004},
	pages={215--233}
}

@article{GOUR:HOLL:MONF:82,
	author={C. Gourieroux and A. Holly and A. Monfort},
	title={Likelihood Ratio Test, Wald Test, and Kuhn--Tucker Test in Linear Models
		With Inequality Constraints on the Regression Parameters},
	journal={Econometrica},
	volume={50},
	year={1982},
	pages={63--80}
}

@book{GREE:03,
	author={W.H. Greene},
	title={Econometric Analysis},
	publisher={Prentice Hall},
	year={2003},
	edition={5th}
}

@book{GREE:12,
	author={W.H. Greene},
	title={Econometric Analysis},
	publisher={Prentice Hall},
	year={2012},
	edition={7th}
}

@book{GREE:18,
	author={W.H. Greene},
	title={Econometric Analysis},
	publisher={Prentice Hall},
	year={2018},
	edition={8th}
}

@article{HAUS:78,
	author={J.A. Hausman},
	title={Specification Tests in Econometrics},
	journal={Econometrica},
	volume={46},
	year={1978},
	pages={1251--1271}
}

@article{HAUS:TAYL:81,
	author={J.A. Hausman and W.E. Taylor},
	title={Panel Data and Unobservable Individual Effects},
	journal={Econometrica},
	volume={49},
	year={1981},
	pages={1377--1398}
}

@article{HOLT:NEWE:ROSE:88,
	author={D. Holtz--Eakin and W. Newey and H.S. Rosen},
	title={Estimating Vector Autoregressions With Panel Data},
	journal={Econometrica},
	volume={56},
	year={1988},
	pages={1371--1395}
}

@article{HOND:85,
	author={Y. Honda},
	title={Testing the Error Components Model With Non--Normal Disturbances},
	journal={Review of Economic Studies},
	volume={52},
	year={1985},
	pages={681--690}
}

@article{KING:WU:97,
	author={M.L. King and P.X. Wu},
	title={Locally Optimal One--Sided Tests for Multiparameter Hypothese},
	journal={Econometric Reviews},
	volume={33},
	year={1997},
	pages={523--529}
}

@article{MACK:WHIT:85,
	author={J.G. MacKinnon and H. White},
	title={Some Heteroskedasticity--Consistent Covariance Matrix Estimators With Improved
		Finite Sample Properties},
	journal={Journal of Econometrics},
	volume={29},
	year={1985},
	pages={305--325}
}

@article{NERLO:71,
	author={M. Nerlove},
	title={Further Evidence on the Estimation of Dynamic Economic Relations from a
		Time--Series of Cross--Sections},
	journal={Econometrica},
	volume={39},
	year={1971},
	pages={359--382}
}

@book{STOC:WATS:07,
  title={Introduction to econometrics},
  author={Stock, James H and Watson, Mark W},
  year={2007},
  publisher={Pearson/Addison Wesley Boston}
}

@article{SWAM:70,
	author={P.A.V.B. Swamy},
	title={Efficient Inference in a Random Coefficient Regression Model},
	journal={Econometrica},
	volume={38},
	year={1970},
	pages={311--323}
}

@book{SWAM:71,
	author={P.A.V.B. Swamy},
	title={Statistical Inference in Random Coefficient Regression Models},
	year={1971},
	publisher={Springer}
}

@article{SWAM:AROR:72,
	author={P.A.V.B. Swamy and S.S Arora},
	title={The Exact Finite Sample Properties of the Estimators of Coefficients in
		the Error Components Regression Models},
	journal={Econometrica},
	volume={40},
	year={1972},
	pages={261--275}
}

@article{WALL:HUSS:69,
	author={T.D. Wallace and A. Hussain},
	title={The Use of Error Components Models in Combining Cross Section With Time
		Series Data},
	journal={Econometrica},
	volume={37},
	year={1969},
	pages={55--72},
	number={1}
}

@book{WOOL:02,
	author={J.M. Wooldridge},
	title={Econometric Analysis of Cross--Section and Panel Data},
	year={2002},
	publisher={MIT Press}
}

@book{WOOL:10,
	author={J.M. Wooldridge},
	title={Econometric Analysis of Cross--Section and Panel Data},
	year={2010},
	edition={2nd},
	publisher={MIT Press},
}

@book{WOOL:13,
	author={J.M. Wooldridge},
	title={Introductory Econometrics: a modern approach},
	publisher={South-Western (Cengage Learning)},
	edition={5th},
	year={2013}
}

@article{ZEIL:04,
	author={A. Zeileis},
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	volume={11},
	year={2004},
	pages={1--17},
	number={10},
	url={https://www.jstatsoft.org/article/view/v011i10}
}

@article{WHIT:80,
  title={A heteroskedasticity-consistent covariance matrix estimator
                  and a direct test for heteroskedasticity},
  author={White, Halbert},
  journal={Econometrica},
  volume={48},
  number={4},
  pages={817--838},
  year={1980},
  publisher={JSTOR}
}

@article{WIND:05,
	author={F. Windmeijer},
	title={A Finite Sample Correction for the Variance of Linear Efficient Two--Steps
		{GMM} Estimators},
	journal={Journal of Econometrics},
	year={2005},
	volume={126},
	pages={25--51}
}

@article{MUNN:90,
	author={A. Munnell},
	title={Why Has Productivity Growth Declined? Productivity and Public Investment},
	journal={New England Economic Review},
	year={1990},
	pages={3--22}
}

@article{BERA:SOSA:YOON:01,
	author={A.K. Bera and W. Sosa--Escudero and M. Yoon},
	title={Tests for the Error Component Model in the Presence of Local Misspecification},
	journal={Journal of Econometrics},
	volume={101},
	year={2001},
	pages={1--23}
}

@article{BALT:LI:91,
	author={B. Baltagi and Q. Li},
	title={A Joint Test for Serial Correlation and Random Individual Effects},
	journal={Statistics and Probability Letters},
	volume={11},
	year={1991},
	pages={277--280}
}

@article{BALT:LI:95,
	author={B. Baltagi and Q. Li},
	title={Testing {AR}(1) Against {MA}(1) Disturbances in an Error Component Model},
	journal={Journal of Econometrics},
	volume={68},
	year={1995},
	pages={133--151}
}

@article{BALT:LI:97,
	author={B. Baltagi and Q. Li},
	title={Monte Carlo Results on Pure and Pretest Estimators of an Error Components
		Model With Autocorrelated Disturbances},
	journal={Annales d'Economie et de Statistique},
	volume={48},
	year={1997},
	pages={69--82}
}

@article{LAIR:WARE:82,
	author={N.M. Laird and J.H. Ware},
	title={Random--Effects Models for Longitudinal Data},
	journal={Biometrics},
	volume={38},
	year={1982},
	pages={963--974}
}

@article{BATE:04,
	author={Douglas Bates},
	title={Least Squares Calculations in \proglang{R}},
	journal={\proglang{R}--news},
	volume={4},
	year={2004},
	pages={17--20},
	number={1}
}

@article{MUND:78,
	author={Yair Mundlak},
	title={On the Pooling of Time Series and Cross Section Data},
	journal={Econometrica},
	volume={46},
	year={1978},
	pages={69--85},
	number={1}
}

@book{FOX:02,
	author={John Fox},
	title={An \proglang{R} and \proglang{S--plus} Companion to Applied Regression},
	publisher={Sage},
	year={2002}
}

@booklet{STOC:WATS:06,
	author={James Stock and Mark Watson},
	title={Heteroskedasticity--Robust Standard Errors for Fixed Effects Panel Data
		Regression},
	howpublished={NBER WP 0323},
	year={2006}
}

@article{STOC:WATS:08,
	author={James H. Stock and Mark W. Watson},
	title={Heteroskedasticity--Robust Standard Errors for Fixed Effects Panel Data
		Regression },
	journal={Econometrica },
	volume={76},
	year={2008},
	pages={155--174},
	number={1}
}


@manual{ATKI:THER:07,
	author={Beth Atkinson and Terry Therneau},
	title={\pkg{kinship}: Mixed--Effects Cox Models, Sparse Matrices, and Modeling Data
		from Large Pedigrees},
	year={2007},
	note={\proglang{R} package version 1.1.0-18},
	url={https://CRAN.R-project.org}
}

@manual{KOEN:NG:2016,
	author={Roger Koenker and Pin Ng},
	title={\pkg{SparseM}: Sparse Linear Algebra },
	year={2016},
	note={\proglang{R} package version 1.72},
	url={https://CRAN.R-project.org/package=SparseM}
}

@manual{BATE:MAEC:2016,
	author={Douglas Bates and Martin Maechler},
	title={\pkg{Matrix}: Sparse and Dense Matrix Classes and Methods },
	year={2016},
	note={\proglang{R} package version 1.2-7.1},
	url={https://CRAN.R-project.org/package=Matrix}
}

@manual{BATE:MAEC:07,
	author={Douglas Bates and Martin Maechler},
	title={\pkg{matrix}: A Matrix Package for \proglang{R}},
	year={2007},
	note={\proglang{R} package version 0.99875-2},
	url={https://CRAN.R-project.org/package=Matrix}
}

@manual{KOEN:NG:07,
	author={Roger Koenker and Pin Ng},
	title={\pkg{SparseM}: Sparse Linear Algebra},
	year={2007},
	note={\proglang{R} package version 0.74},
	url={https://CRAN.r-project.org/package=SparseM}
}

@manual{CROI:06,
	author={Yves Croissant},
	title={\pkg{Ecdat}: Data Sets for Econometrics},
	year={2006},
	note={\proglang{R} package version 0.1-5},
	url={https://CRAN.r-project.org/package=Ecdat}
}

@manual{FOX:06,
	author={John Fox},
	title={\pkg{car}: Companion to Applied Regression},
	year={2007},
	note={\proglang{R} package version 1.2-5},
	url={https://CRAN.r-project.org/package=car, http://socserv.socsci.mcmaster.ca/jfox/}
}

@manual{FOX:2016,
	author={John Fox},
	title={\pkg{car}: Companion to Applied Regression },
	year={2016},
	note={\proglang{R} package version 2.1-3},
	url={https://CRAN.R-project.org/package=car}
}


@article{ZEIL:HOTH:02,
	author={Achim Zeileis and Torsten Hothorn},
	title={Diagnostic Checking in Regression Relationships},
	journal={\proglang{R} News},
	volume={2},
	year={2002},
	pages={7--10},
	number={3},
	url={https://CRAN.R-project.org/doc/Rnews/}
}

@misc{HOTH:ZEIL:MILL:MITC:07,
	author={T. Hothorn and A. Zeileis and G. Millo and D. Mitchell},
	title={\pkg{lmtest}: Testing Linear Regression Models},
	year={2007},
	howpublished={\proglang{R} package version 0.9-19},
	url={https://CRAN.R-project.org/package=lmtest}
}

@manual{HOTH:ZEIL:FARE:CUMM:MILL:MITC:2015,
	author={T. Hothorn and A. Zeileis and R. W. Farebrother and C. Cummins and G. Millo and D. Mitchell},
	title={\pkg{lmtest}: Testing Linear Regression Models },
	year={2015},
	note={\proglang{R} package version 0.9-34},
	url={https://CRAN.R-project.org/package=lmtest}
}

@misc{LUML:ZEIL:07,
	author={T. Lumley and A. Zeileis},
	title={\pkg{sandwich}: Model--Robust Standard Error Estimation for Cross--Sectional,
		Time Series and Longitudinal Data},
	year={2007},
	howpublished={\proglang{R} package version 2.0-2},
	url={https://CRAN.R-project.org}
}

@manual{LUML:ZEIL:2015,
	author={T. Lumley and A. Zeileis},
	title={\pkg{sandwich}: Robust Covariance Matrix Estimators },
	year={2015},
	note={\proglang{R} package version 2.3-4},
	url={https://CRAN.R-project.org/package=sandwich}
}


@manual{PINH:BATE:DEBR:SARK:07,
	author={Jose Pinheiro and Douglas Bates and Saikat DebRoy and Deepayan Sarkar the
		\proglang{R} Core team},
	title={\pkg{nlme}: Linear and Nonlinear Mixed Effects Models},
	year={2007},
	note={\proglang{R} package version 3.1-86},
	url={https://CRAN.r-project.org/package=nlme}
}

@manual{BATE:07,
	author={Douglas Bates},
	title={\pkg{lme4}: Linear Mixed--Effects Models Using \proglang{S4} Classes},
	year={2007},
	note={\proglang{R} package version 0.99875-9},
	url={https://CRAN.r-project.org/package=lme4}
}

@article{CORN:RUPE:88,
	author={C. Cornwell and P. Rupert},
	title={Efficient Estimation With Panel Data: an Empirical Comparison of Instrumental
		Variables Estimators},
	journal={Journal of Applied Econometrics},
	volume={3},
	year={1988},
	pages={149--155}
}

@article{DRUK:03,
	author={D.M. Drukker},
	title={Testing for Serial Correlation in Linear Panel--Data Models},
	journal={The Stata Journal},
	volume={3},
	year={2003},
	pages={168-177},
	number={2}
}

@book{PINH:BATE:00,
	author={J.C. Pinheiro and D. Bates},
	title={Mixed--Effects Models in \proglang{S} and \proglang{S-plus}},
	publisher={Springer-Verlag},
	year={2000}
}

@unpublished{PESA:04,
	author={Pesaran, M.H.},
	title={General Diagnostic Tests for Cross Section Dependence in Panels},
	note={CESifo Working Paper Series, 1229},
	year={2004},
}

@article{DEHO:SARA:06,
	author={De Hoyos, R.E. and Sarafidis, V.},
	title={Testing for Cross--Sectional Dependence in Panel--Data Models},
	journal={The Stata Journal},
	volume={6},
	year={2006},
	pages={482-496},
	number={4}
}

@Manual{BIVA:08,
    title = {spdep: Spatial Dependence: Weighting Schemes, Statistics and Models},
    author = {Roger Bivand},
    year = {2008},
    note = {R package version 0.4-17},
 }


@Manual{R:2008,
  title     = {\proglang{R}: {A} Language and Environment for Statistical Computing},
  author    = {{\proglang{R} Development Core Team}},
  organization  = {\proglang{R} Foundation for Statistical Computing},
  address   = {Vienna, Austria},
  year      = {2008},
  note      = {{ISBN} 3-900051-07-0},
  url       = {https://www.r-project.org/}
}

  @Book{KLEI:ZEIL:08,
    title = {Applied Econometrics with {R}},
    author = {Christian Kleiber and Achim Zeileis},
    year = {2008},
    publisher = {Springer-Verlag},
    address = {New York},
    note = {{ISBN} 978-0-387-77316-2},
    url = {https://CRAN.R-project.org/package=AER},
 }

@Article{HARR:RUBI:78,
  author = 	 {Harrison, D. and Rubinfeld, D.L.},
  title = 	 {Hedonic housing prices and the demand for clean air},
  journal = 	 {Journal of Environmental Economics and Management},
  year = 	 {1978},
  OPTkey = 	 {},
  volume = 	 {5},
  OPTnumber = 	 {},
  pages = 	 {81-102},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{BALT:CHAN:94,
  author = 	 {Baltagi, B.H. and Chang, Y.J.},
  title = 	 {Incomplete panels: a comparative study of
                  alternative estimators for the unbalanced one-way
                  error component regression model},
  journal = 	 {Journal of Econometrics},
  year = 	 {1994},
  OPTkey = 	 {},
  volume = 	 {62},
  OPTnumber = {2},
  pages = 	 {67-89},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{ACEMO:JOHNO:ROBIN:YARED:08,
  author = 	 {A Daron Acemoglu and Simon Johnson and James A. Robinson and Pierre Yared},
  title = 	 {Income and democracy},
  journal = 	 {American Economic Review},
  year = 	 {2008},
  OPTkey = 	 {},
  volume = 	 {98(3)},
  OPTnumber = 	 {},
  pages = 	 {808-842},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{EGGER:PFAFF:04,
  author = 	 {Peter Egger and Michael Pfaffermayr},
  title = 	 {Distance, Trade, and {FDI}: A {H}ausman-{T}aylor {SUR} Approach},
  journal = 	 {Journal of Applied Econometrics},
  year = 	 {2004},
  OPTkey = 	 {},
  volume = 	 {19(2)},
  OPTnumber = 	 {},
  pages = 	 {227-46},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@Article{KESSL:HANSE:LESSM:11,
  author = 	 {Anke S. Kessler and  Nico A. Hansen and Christian Lessman},
  title = 	 {Interregional redistribution and mobility in
                  federations : a positive approach},
  journal = 	 {The Review of Economic Studies},
  year = 	 {2011},
  OPTkey = 	 {},
  volume = 	 {78},
  OPTnumber = 	 {},
  pages = 	 {1345-78},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{BREND:DRAZE:08,
  author = 	 {Adi Brender and Allan Drazen},
  title = 	 {Budget deficits and economic growth affect reelection prospects? Evidence froma large panel of countries},
  journal = 	 {American Economic Review},
  year = 	 {2008},
  OPTkey = 	 {},
  volume = 	 {98(5)},
  OPTnumber = 	 {},
  pages = 	 {2203-2220},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@Article{Croissant:Millo:2008,
   author        = {Yves Croissant and Giovanni Millo},
   title         = {Panel Data Econometrics in \proglang{R}:
                    The \pkg{plm} Package},
   journal       = {Journal of Statistical Software},
   year          = {2008},
   volume        = {27},
   number        = {2},
   pages         = {1-43},
   url           = {https://www.jstatsoft.org/article/view/v027i02}
}


@Article{RAUX:SOUCH:CROIS:09,
  author = 	 {Charles Raux and St\'ephanie Souche and Yves Croissant},
  title = 	 {How fair is pricing perceived to be? An empirical study},
  journal = 	 {Public Choice},
  year = 	 {2009},
  OPTkey = 	 {},
  volume = 	 {139(1)},
  OPTnumber = 	 {},
  pages = 	 {227-240},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@Article{KESSL:HANSE:12,
  author = 	 {Anke S. Kessler and Nico A. Hansen},
  title = 	 {Interregional redistribution and mobility in federations: a positive approach},
  journal = 	 {Review of Economic Studies},
  year = 	 {2012},
  OPTkey = 	 {},
  volume = 	 {78(4)},
  OPTnumber = 	 {},
  pages = 	 {1345-1378},
  month = 	 {march},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{DRAK:07,
  author = 	 {Konstantinos Drakos},
  title = 	 {The size of under-reporting bias in recorded transational terrorist activity},
  journal = 	 {Journal of the Royal Statistical Society, Series A (Statistics in Society)},
  year = 	 {2007},
  OPTkey = 	 {},
  volume = 	 {170(4)},
  OPTnumber = 	 {},
  pages = 	 {909-921},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@Article{PORTO:REVEL:12,
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@misc{GRETL:2021,
title = {Gretl User’s Guide},
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year = {2021},
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@Manual{spdep,
    title = {spdep: Spatial dependence: weighting schemes, statistics and models},
    author = {Roger Bivand and with contributions by Micah Altman and
                  Luc Anselin and Renato Assunção and Olaf Berke and
                  Andrew Bernat and Guillaume Blanchet and Eric
                  Blankmeyer and Marilia Carvalho and Bjarke
                  Christensen and Yongwan Chun and Carsten Dormann and
                  Stéphane Dray and Rein Halbersma and Elias Krainski
                  and Pierre Legendre and Nicholas Lewin-Koh and
                  Hongfei Li and Jielai Ma and Giovanni Millo and
                  Werner Mueller and Hisaji Ono and Pedro Peres-Neto
                  and Gianfranco Piras and Markus Reder and Michael
                  Tiefelsdorf and Danlin Yu.},
    year = {2012},
    note = {R package version 0.5-46},
    url = {https://CRAN.R-project.org/package=spdep},
}

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  OPTkey = 	 {},
  volume = 	 {9},
  number = 	 {2},
  pages = 	 {219-255},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{FARB:SILV:WACH:16,
  author = 	 {Farber, Henry S. and  Silverman, Dan and von Wachter, Till},
  title = 	 {Determinants of callbacks to job applications: an audit study},
  journal = 	 {American Economic Review},
  year = 	 {2016},
  OPTkey = 	 {},
  volume = 	 {106},
  number = 	 {5},
  pages = 	 {314-318},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{ALAN:HONO:HU:LETH:13,
  author = 	 {Alan, Sule and Honor\'e, Bo E. and Hu, Luojia and Leth-Petersen, Soren},
  title = 	 {Estimation of panel data regression model with two-sided censoring or truncation},
  journal = 	 {Journal of Econometric Methods},
  year = 	 {2013},
  OPTkey = 	 {},
  volume = 	 {3},
  number = 	 {1},
  pages = 	 {1-20},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{HAUS:HALL:GRIL:86,
  author = 	 {Hausman, J. and Hall, B.H. and Griliches, Z.},
  title = 	 {Patents and R&D: is there a lag?},
  journal = 	 {International Economic Review},
  year = 	 {1986},
  OPTkey = 	 {},
  OPTvolume = 	 {27},
  OPTnumber = 	 {},
  pages = 	 {265-283},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{CINC:97,
  author = 	 {Cincer, Michele},
  title = 	 {Patents, R&D, and technological spillovers at the firm level: some evidence from econometric count models for panel data},
  journal = 	 {Journal of Applied Econometrics},
  year = 	 {1997},
  OPTkey = 	 {},
  volume = 	 {12},
  number = 	 {3},
  OPTpages = 	 {265-280},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{ACCO:CORS:SIMO:14,
  author = 	 {Acconcia, Antonio and Corsetti, Giancarlo and Simonelli, Saverio},
  title = 	 {Mafia and public spending: evidence on the fiscal multiplier from a quasi-experiment},
  journal = 	 {American Economic Review},
  year = 	 {2014},
  OPTkey = 	 {},
  volume = 	 {104},
  OPTnumber = 	 {7},
  pages = 	 {2185-2209},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Manual{CROI:MILL:17,
    title = {pder: Panel Data Econometrics with R},
    author = {Yves Croissant and Giovanni Millo},
    year = {2017},
    note = {R package version 1.0-0},
    url = {http://www.r-project.org},
 }

@Manual{CROI:17,
    title = {pglm: panel generalized linear model},
    author = {Yves Croissant},
    year = {2017},
    note = {R package version 0.2-0},
    url = {http://www.r-project.org},
 }

@Manual{CROI:ZEIL:16,
    title = {truncreg: Truncated Gaussian Regression Models},
    author = {Yves Croissant and Achim Zeileis},
    year = {2016},
    note = {R package version 0.2-4},
    url = {https://CRAN.R-project.org/package=truncreg},
 }

@Book{survival-book,
    title = {Modeling Survival Data: Extending the {C}ox Model},
    author = {Therneau, Terry M. and Grambsch, Patricia M.},
    year = {2000},
    publisher = {Springer},
    address = {New York},
    isbn = {0-387-98784-3},
 }
 
@Manual{HENN:17,
    title = {censReg: Censored Regression (Tobit) Models},
    author = {Arne Henningsen},
    year = {2017},
    note = {R package version 0.5-26},
    url = {https://CRAN.R-project.org/package=censReg},
 }

@Book{VENA:RIPL:02,
    title = {Modern Applied Statistics with S},
    author = {W. N. Venables and B. D. Ripley},
    publisher = {Springer},
    edition = {4th},
    address = {New York},
    year = {2002},
    note = {ISBN 0-387-95457-0},
    url = {http://www.stats.ox.ac.uk/pub/MASS4},
 }

@Manual{PINH:17,
    title = {{nlme}: Linear and Nonlinear Mixed Effects Models},
    author = {Jose Pinheiro and Douglas Bates and Saikat DebRoy and Deepayan Sarkar and {R Core Team}},
    year = {2017},
    note = {R package version 3.1-131},
    url = {https://CRAN.R-project.org/package=nlme},
 }

@Article{JACK:11,
    title = {Multi-State Models for Panel Data: The {msm} Package for {R}},
    author = {Christopher H. Jackson},
    journal = {Journal of Statistical Software},
    year = {2011},
    volume = {38},
    number = {8},
    pages = {1--29},
    url = {https://www.jstatsoft.org/article/view/v038i08},
}

@Manual{WICK:FRAN:16,
    title = {dplyr: A Grammar of Data Manipulation},
    author = {Hadley Wickham and Romain Francois},
    year = {2016},
    note = {R package version 0.5.0},
    url = {https://CRAN.R-project.org/package=dplyr},
 }
 
@Manual{BDSM:14,
    title = {bdsmatrix: Routines for Block Diagonal Symmetric matrices},
    author = {Terry Therneau},
    year = {2014},
    note = {R package version 1.3-2},
    url = {https://CRAN.R-project.org/package=bdsmatrix},
}

@Article{SPAM:10,
    title = {{spam}: A Sparse Matrix {R} Package with Emphasis on {MCMC} Methods for {G}aussian {M}arkov Random Fields},
    author = {Reinhard Furrer and Stephan R. Sain},
    journal = {Journal of Statistical Software},
    year = {2010},
    volume = {36},
    number = {10},
    pages = {1--25},
    url = {https://www.jstatsoft.org/article/view/v036i10},
}

@Manual{MATR:16,
    title = {Matrix: Sparse and Dense Matrix Classes and Methods},
    author = {Douglas Bates and Martin Maechler},
    year = {2016},
    note = {R package version 1.2-4},
    url = {https://CRAN.R-project.org/package=Matrix},
}

@Article{MAXL:11,
    title = {maxLik: A package for maximum likelihood estimation in {R}},
    author = {Arne Henningsen and Ott Toomet},
    journal = {Computational Statistics},
    year = {2011},
    volume = {26},
    number = {3},
    pages = {443-458},
    doi = {10.1007/s00180-010-0217-1},
    url = {http://dx.doi.org/10.1007/s00180-010-0217-1},
}


@article{BALT:GRIF:83,
title = "Gasoline demand in the OECD: An application of pooling and
                  testing procedures",
journal = "European Economic Review",
volume = "22",
number = "2",
pages = "117 - 137",
year = "1983",
issn = "0014-2921",
OPTdoi = "https://doi.org/10.1016/0014-2921(83)90077-6",
url = "https://doi.org/10.1016/0014-2921(83)90077-6",
author = "Badi H. Baltagi and James M. Griffin",
abstract = "This study utilizes a pooled inter-country data set,
                  finding the long-run price-elasticity falls in the
                  range −0.55 to −0.9, depending on the choice of
                  pooled estimators. The estimators included the OLS,
                  within-, and between-country estimators, plus five
                  feasible GLS estimators. Even allowing for a
                  ten-year distributed lag on price to reflect changes
                  in auto-efficiency characteristics, the
                  within-country estimator yields appreciably more
                  inelastic estimates than did the O:S estimator,
                  which was heavily influenced by the between- or
                  inter-country variation. This difference raises
                  intriguing questions for future research."
}

@PhdThesis{GRUN:58,
title = {The determinants of corporate investment},
year =  {1958},
school = {Department of Economics, University of Chicago},
author = {Grunfeld, Yehuda}
}

@ARTICLE{KLEI:ZEIL:10,
title = {The Grunfeld Data at 50},
author = {Kleiber, Christian and Zeileis, Achim},
year = {2010},
journal = {German Economic Review},
volume = {11},
pages = {404-417},
abstract = {This paper revisits Grunfeld's well-known investment data,
                  one of the most widely used datasets in all of
                  applied econometrics, on the occasion of their 50th
                  anniversary. It presents, apparently for the first
                  time after the publication of the original Chicago
                  Ph.D. thesis, the full dataset, points out errors
                  and inconsistencies in several currently available
                  versions, and also revisits a number of empirical
                  studies from the literature of the last five
                  decades. Our findings provide a cautionary tale on
                  the use of widely known data and underline the need
                  for mandatory data and code archives. Copyright 2010
                  The Author. German Economic Review 2010 Verein für
                  Socialpolitik.},
url = {https://doi.org/10.1111/j.1468-0475.2010.00513.x}
}

@book{BESL:KUH:WELS:80,
title = {Regression diagnostics: identifying influential data and
                  sources of collinearity},
author= {Besley, David A. and Kuh, Edwin and Welsch, Roy E.},
year = {1980},
publisher={John Wiley and Sons ltd},
note = {Wiley series in probability and statistics}
}

@book{CAME:TRIV:05,
author = {Colin Cameron, Adrian and K. Trivedi, Pravin},
year = {2005},
title = {Microeconometrics: Methods and Applications},
publisher = {Cambridge University Press},
isbn = {0521848059},
doi = {10.1017/CBO9780511811241}
}

@article{ZILI:97,
 ISSN = {07350015},
 OPTURL = {https://www.jstor.org/stable/1392488},
 abstract = {I examine the empirical performance of instrumental
                  variables estimators with predetermined instruments
                  in an application to life-cycle labor supply under
                  uncertainty. The estimators studied are two-stage
                  least squares, generalized method-of-moments (GMM),
                  forward filter, independently weighted GMM, and
                  split-sample instrumental variables. I compare the
                  bias/efficiency trade-off for the estimators using
                  bootstrap algorithms suggested by Freedman and by
                  Brown and Newey. Results indicate that the downward
                  bias in GMM is quite severe as the number of moment
                  conditions expands, outweighing the gains in
                  efficiency. The forward-filter estimator, however,
                  has lower bias and is more efficient than two-stage
                  least squares.},
 author = {James P. Ziliak},
 journal = {Journal of Business & Economic Statistics},
 number = {4},
 pages = {419--431},
 publisher = {[American Statistical Association, Taylor & Francis,
                  Ltd.]},
 title = {Efficient Estimation with Panel Data When Instruments Are
                  Predetermined: An Empirical Comparison of
                  Moment-Condition Estimators},
 volume = {15},
 year = {1997}
}

@book{VERB:04,
author = {Verbeek, Marno},
year = {2004},
title = {A Guide to Modern Econometrics},
edition = {2nd},
publisher = {Wiley}
}

@article{FENG:HORR:12,
author = {Feng, Qu and Horrace, William C.},
title = {Alternative technical efficiency measures: Skew, bias and scale},
journal = {Journal of Applied Econometrics},
volume = {27},
number = {2},
pages = {253-268},
doi = {10.1002/jae.1190},
abstract = {SUMMARY In the fixed-effects stochastic frontier model an
                  efficiency measure relative to the best firm in the
                  sample is universally employed. This paper considers
                  a new measure relative to the worst firm in the
                  sample. We find that estimates of this measure have
                  smaller bias than those of the traditional measure
                  when the sample consists of many firms near the
                  efficient frontier. Moreover, a two-sided measure
                  relative to both the best and the worst firms is
                  proposed. Simulations suggest that the new measures
                  may be preferred depending on the skewness of the
                  inefficiency distribution and the scale of
                  efficiency differences. Copyright 2010 John Wiley \&
                  Sons, Ltd.},
year = {2012}
}

@book{HAYA:00,
author = {Hayashi, F.},
year = 2000,
publisher = {Princeton University Press},
title = Econometrics
}

@article{SUMM:HEST:91,
author = {Summers, Robert and Heston, Alan},
year = {1991},
month = {02},
pages = {327-68},
title = {The Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950–1988},
volume = {106},
journal = {The Quarterly Journal of Economics},
doi = {10.2307/2937941}
}

@ARTICLE{BALT:LEVI:92,
title = {Cigarette taxation: Raising revenues and reducing
                  consumption},
author = {Baltagi, Badi and Levin, Dan},
year = {1992},
journal = {Structural Change and Economic Dynamics},
volume = {3},
number = {2},
pages = {321-335},
url = {https://EconPapers.repec.org/RePEc:eee:streco:v:3:y:1992:i:2:p:321-335}
}

@article{BALT:GRIF:XION:00,
author = {Baltagi, Badi H. and Griffin, James M. and Xiong, Weiwen},
title = {To Pool or Not to Pool: Homogeneous Versus Heterogeneous
                  Estimators Applied to Cigarette Demand},
journal = {The Review of Economics and Statistics},
volume = {82},
number = {1},
pages = {117-126},
year = {2000},
doi = {10.1162/003465300558551},
URL = {https://doi.org/10.1162/003465300558551},
eprint = {https://doi.org/10.1162/003465300558551},
abstract = { This paper reexamines the benefits of pooling and, in
                  addition, contrasts the performance of newly
                  proposed heterogeneous estimators. The analysis
                  utilizes a panel data set from 46 American states
                  over the period 1963 to 1992 and a dynamic demand
                  specification for cigarettes. Also, the forecast
                  performance of the various estimators is compared.}
}

@article{IM:SEUN:SCHM:WOOL:99,
title = "Efficient estimation of panel data models with strictly
                  exogenous explanatory variables",
journal = "Journal of Econometrics",
volume = "93",
number = "1",
pages = "177 - 201",
year = "1999",
issn = "0304-4076",
OPTdoi = "https://doi.org/10.1016/S0304-4076(99)00008-1",
url = "https://doi.org/10.1016/S0304-4076(99)00008-1",
author = "Kyung So Im and Seung C. Ahn and Peter Schmidt and Jeffrey
                  M. Wooldridge",
keywords = "Panel data, Strict exogeneity, Efficiency, Redundancy",
abstract = "With panel data, exogeneity assumptions imply many more
                  moment conditions than standard estimators
                  use. However, many of the moment conditions may be
                  redundant, in the sense that they do not increase
                  efficiency; if so, we may establish the standard
                  estimators’ efficiency. We prove efficiency results
                  for GLS in a model with unrestricted error
                  covariance matrix, and for 3SLS in models where
                  regressors and errors are correlated, such as the
                  Hausman–Taylor model. For models with correlation
                  between regressors and errors, and with unrestricted
                  error covariance structure, we provide a simple
                  estimator based on a GLS generalization of
                  deviations from means."
}

@unpublished{AREL:BOND:98,
	author={Arellano, M. and Bond, S.},
	title={Dynamic panel data estimation using DPD98 for GAUSS: a guide for users},
	year={1998},
	url = {https://ifs.org.uk/publications/dpd-gauss},
	note = {unpublished}
}

@unpublished{DOOR:AREL:BOND:12,
	author={Arellano, M. and Bond, S.},
	title={Panel data estimation using DPD for Ox},
	year={2012},
	url = {https://www.doornik.com/download/oxmetrics7/Ox_Packages/dpd.pdf},
	note = {unpublished}
}

@article{BALT:FENG:KAO:12,
title = "A Lagrange Multiplier test for cross-sectional dependence in
                  a fixed effects panel data model",
journal = "Journal of Econometrics",
volume = "170",
number = "1",
pages = "164 - 177",
year = "2012",
issn = "0304-4076",
OPTdoi = "https://doi.org/10.1016/j.jeconom.2012.04.004",
url = "https://doi.org/10.1016/j.jeconom.2012.04.004",
author = "Badi H. Baltagi and Qu Feng and Chihwa Kao",
keywords = " test, Cross-sectional dependence, Fixed effects, High dimensional inference, John test, Panel data",
abstract = "It is well known that the standard Breusch and Pagan
                  (1980) LM test for cross-equation correlation in a
                  SUR model is not appropriate for testing
                  cross-sectional dependence in panel data models when
                  the number of cross-sectional units (n) is large and
                  the number of time periods (T) is small. In fact, a
                  scaled version of this LM test was proposed by
                  Pesaran (2004) and its finite sample bias was
                  corrected by Pesaran et al. (2008). This was done in
                  the context of a heterogeneous panel data
                  model. This paper derives the asymptotic bias of
                  this scaled version of the LM test in the context of
                  a fixed effects homogeneous panel data model. This
                  asymptotic bias is found to be a constant related to
                  n and T, which suggests a simple bias corrected LM
                  test for the null hypothesis. Additionally, the
                  paper carries out some Monte Carlo experiments to
                  compare the finite sample properties of this
                  proposed test with existing tests for
                  cross-sectional dependence."
}

@article{PESA:15,
author = { M.  Hashem Pesaran},
title = {Testing Weak Cross-Sectional Dependence in Large Panels},
journal = {Econometric Reviews},
volume = {34},
number = {6-10},
pages = {1089-1117},
year  = {2015},
publisher = {Taylor & Francis},
doi = {10.1080/07474938.2014.956623},
URL = {https://doi.org/10.1080/07474938.2014.956623},
eprint = {https://doi.org/10.1080/07474938.2014.956623}
}

@article{KANG:85,
title = "A note on the equivalence of specification tests in the
                  two-factor multivariate variance components model",
journal = "Journal of Econometrics",
volume = "28",
number = "2",
pages = "193 - 203",
year = "1985",
issn = "0304-4076",
OPTdoi = "https://doi.org/10.1016/0304-4076(85)90119-8",
url = "https://doi.org/10.1016/0304-4076(85)90119-8",
author = "Suk Kang",
abstract = "This note offers a generalization of Hausman and Taylor's
                  equivalence of specification tests in the
                  single-equation variance (error) components model to
                  the two-factor multivariate variance components
                  case. The relationship between the specification
                  tests and the hypothesis test in the model proposed
                  by Mundlak is also discussed."
}

@article{BALT:CHAN:LI:98,
author = {Baltagi, Badi and Chang, YA and Li, Q},
year = {1998},
month = {01},
pages = {1-20},
journal = "Advances in econometrics",
title = {Testing for random individual and time effects using unbalanced panel data},
volume = {13}
}

@book{ANDR:GOLS:SCMI:13,
author = {Andre\ss, Hans-Jürgen and Golsch, Katrin and Schmidt-Catran, Alexander},
year = {2013},
month = {01},
publisher = {Springer},
title = {Applied Panel Data Analysis for Economic and Social Surveys},
doi = {10.1007/978-3-642-32914-2}
}

@Article{CAME:MILL:15,
  author={A. Colin Cameron and Douglas L. Miller},
  title={A Practitioner's Guide to Cluster-Robust Inference},
  journal={Journal of Human Resources},
  year=2015,
  volume={50},
  number={2},
  pages={317-372},
  month={},
  keywords={},
  doi={},
  abstract={We consider statistical inference for regression when data
                  are grouped into clusters, with regression model
                  errors independent across clusters but correlated
                  within clusters. Examples include data on
                  individuals with clustering on village or region or
                  other category such as industry, and state-year
                  differences-in-differences studies with clustering
                  on state. In such settings, default standard errors
                  can greatly overstate estimator precision. Instead,
                  if the number of clusters is large, statistical
                  inference after OLS should be based on
                  cluster-robust standard errors. We outline the basic
                  method as well as many complications that can arise
                  in practice. These include cluster-specific fixed
                  effects, few clusters, multiway clustering, and
                  estimators other than OLS.},
  url={https://ideas.repec.org/a/uwp/jhriss/v50y2015i2p317-372.html}
}

@article{GRAN:69,
 ISSN = {00129682, 14680262},
 OPTURL = {https://www.jstor.org/stable/1912791},
 abstract = {There occurs on some occasions a difficulty in deciding
                  the direction of causality between two related
                  variables and also whether or not feedback is
                  occurring. Testable definitions of causality and
                  feedback are proposed and illustrated by use of
                  simple two-variable models. The important problem of
                  apparent instantaneous causality is discussed and it
                  is suggested that the problem often arises due to
                  slowness in recording information or because a
                  sufficiently wide class of possible causal variables
                  has not been used. It can be shown that the cross
                  spectrum between two variables can be decomposed
                  into two parts, each relating to a single causal arm
                  of a feedback situation. Measures of causal lag and
                  causal strength can then be constructed. A
                  generalisation of this result with the partial cross
                  spectrum is suggested.},
 author = {C. W. J. Granger},
 journal = {Econometrica},
 number = {3},
 pages = {424--438},
 publisher = {[Wiley, Econometric Society]},
 title = {Investigating Causal Relations by Econometric Models and
                  Cross-spectral Methods},
 volume = {37},
 year = {1969}
}

@Article{LOPE:WEBE:17,
  author={Luciano Lopez and Sylvain Weber},
  title={{Testing for Granger causality in panel data}},
  journal={Stata Journal},
  year=2017,
  volume={17},
  number={4},
  pages={972-984},
  month={December},
  keywords={xtgcause; Granger causality; panel datasets; bootstrap},
  doi={},
  abstract={With the development of large and long panel databases,
                  the theory surrounding panel causality evolves
                  quickly, and empirical researchers might find it
                  difficult to run the most recent techniques
                  developed in the literature. In this article, we
                  present the community-contributed command xtgcause,
                  which imple- ments a procedure proposed by
                  Dumitrescu and Hurlin (2012, Economic Modelling 29:
                  1450–1460) for detecting Granger causality in panel
                  datasets. Thus, it con- stitutes an effort to help
                  practitioners understand and apply the
                  test. xtgcause offers the possibility of selecting
                  the number of lags to include in the model by
                  minimizing the Akaike information criterion,
                  Bayesian information criterion, or Hannan–Quinn
                  information criterion, and it offers the possibility
                  to implement a bootstrap procedure to compute
                  p-values and critical values.},
  url={https://www.stata-journal.com/article.html?article=st0507}
}

@article{DUMI:HURL:12,
title = "Testing for Granger non-causality in heterogeneous panels",
journal = "Economic Modelling",
volume = "29",
number = "4",
pages = "1450 - 1460",
year = "2012",
issn = "0264-9993",
OPTdoi = "https://doi.org/10.1016/j.econmod.2012.02.014",
url = "https://doi.org/10.1016/j.econmod.2012.02.014",
author = "Elena-Ivona Dumitrescu and Christophe Hurlin",
keywords = "Granger non-causality, Panel data, Wald test",
abstract = "This paper proposes a very simple test of Granger (1969)
                  non-causality for heterogeneous panel data
                  models. Our test statistic is based on the
                  individual Wald statistics of Granger non causality
                  averaged across the cross-section units. First, this
                  statistic is shown to converge sequentially to a
                  standard normal distribution. Second, the
                  semi-asymptotic distribution of the average
                  statistic is characterized for a fixed T sample. A
                  standardized statistic based on an approximation of
                  the moments of Wald statistics is hence
                  proposed. Third, Monte Carlo experiments show that
                  our standardized panel statistics have very good
                  small sample properties, even in the presence of
                  cross-sectional dependence."
}

@article{SOSA:BERA:08,
author = {Walter Sosa-Escudero and Anil K. Bera},
title ={Tests for Unbalanced Error-Components Models under Local
                  Misspecification},
journal = {The Stata Journal},
volume = {8},
number = {1},
pages = {68-78},
year = {2008},
doi = {10.1177/1536867X0800800105},
URL = {https://doi.org/10.1177/1536867X0800800105},
eprint = {https://doi.org/10.1177/1536867X0800800105},
    abstract = { This paper derives unbalanced versions of the test
                  statistics for first-order serial correlation and
                  random individual effects summarized in
                  Sosa-Escudero and Bera (2001, Stata Technical
                  Bulletin Reprints, vol. 10, pp. 307–311), and
                  updates their xttest1 routine. The derived test
                  statistics should be useful for applied researchers
                  faced with the increasing availability of panel
                  information where not every individual or country is
                  observed for the full time span. The test statistics
                  proposed here are based on ordinary least-squares
                  residuals and hence are computationally very
                  simple.}
}

@article{DURB:WATS:50,
 ISSN = {00063444},
 OPTURL = {https://www.jstor.org/stable/2332391},
 author = {J. Durbin and G. S. Watson},
 journal = {Biometrika},
 number = {3/4},
 pages = {409--428},
 publisher = {[Oxford University Press, Biometrika Trust]},
 title = {Testing for Serial Correlation in Least Squares Regression: I},
 volume = {37},
 year = {1950}
}

@article{DURB:WATS:71,
 ISSN = {00063444},
 OPTURL = {https://www.jstor.org/stable/2334313},
 abstract = {The paper considers a number of problems arising from the
                  test of serial correlation based on the d statistic
                  proposed earlier by the authors (Durbin & Watson,
                  1950, 1951). Methods of computing the exact
                  distribution of d are investigated and the exact
                  distribution is compared with six approximations to
                  it for four sets of published data. It is found that
                  approximations suggested by Theil and Nagar and by
                  Hannan are too inaccurate for practical use but that
                  the beta approximation proposed in the 1950 and 1951
                  papers and a new approximation, called by us the a +
                  bd<sub>U</sub> approximation and based, like the
                  beta approximation, on the exact first two moments
                  of d, both perform well. The power of the d test is
                  compared with that of certain exact tests proposed
                  by Theil, Durbin, Koerts and Abrahamse from the
                  standpoint of invariance theory. It is shown that
                  the d test is locally most powerful invariant but
                  that the other tests are not. There are three
                  appendices. The first gives an account of the exact
                  distribution of d. The second derives the mean and
                  variance to a second order of approximation of a
                  modified maximum likelihood statistic closely
                  related to d. The third sets out details of the
                  computations required for the a + bd<sub>U</sub>
                  approximation.},
 author = {J. Durbin and G. S. Watson},
 journal = {Biometrika},
 number = {1},
 pages = {1--19},
 publisher = {[Oxford University Press, Biometrika Trust]},
 title = {Testing for Serial Correlation in Least Squares Regression. III},
 volume = {58},
 year = {1971}
}

@article{DURB:WATS:51,
    author = {Durbin, J. and Watson, G. S.},
    title = "{Testing for serial correlation in least squares regression. II}",
    journal = {Biometrika},
    volume = {38},
    number = {1-2},
    pages = {159-178},
    year = {1951},
    month = {06},
    issn = {0006-3444},
    doi = {10.1093/biomet/38.1-2.159},
    url = {https://doi.org/10.1093/biomet/38.1-2.159}
}

@article{BALT:WU:99,
 ISSN = {02664666, 14694360},
 OPTURL = {https://www.jstor.org/stable/3533276},
 abstract = {This paper deals with the estimation of unequally spaced
                  panel data regression models with AR(1) remainder
                  disturbances. A feasible generalized least squares
                  (GLS) procedure is proposed as a weighted least
                  squares that can handle a wide range of unequally
                  spaced panel data patterns. This procedure is simple
                  to compute and provides natural estimates of the
                  serial correlation and variance components
                  parameters. The paper also provides a locally best
                  invariant test for zero first-order serial
                  correlation against positive or negative serial
                  correlation in case of unequally spaced panel data.},
 author = {Badi H. Baltagi and Ping X. Wu},
 journal = {Econometric Theory},
 number = {6},
 pages = {814--823},
 publisher = {Cambridge University Press},
 title = {Unequally Spaced Panel Data Regressions with AR(1) Disturbances},
 volume = {15},
 year = {1999}
}

@article{BHAR:FRAN:NARE:82,
 ISSN = {00346527, 1467937X},
 OPTURL = {https://www.jstor.org/stable/2297285},
 abstract = {This paper generalizes the Durbin-Watson type statistics
                  to test the OLS residuals from the fixed effects
                  model for serial independence. Also generalized are
                  the tests proposed by Sargan and Bhargava for the
                  hypothesis that the residuals form a random walk. A
                  method for efficient estimation of the parameters is
                  also developed. Finally, an earnings function is
                  estimated using the Michigan Survey of Income
                  Dynamics in order to illustrate the uses of the
                  tests and the estimation procedures developed in
                  this paper.},
 author = {A. Bhargava and L. Franzini and W. Narendranathan},
 journal = {The Review of Economic Studies},
 number = {4},
 pages = {533--549},
 publisher = {[Oxford University Press, Review of Economic Studies, Ltd.]},
 title = {Serial Correlation and the Fixed Effects Model},
 volume = {49},
 year = {1982}
}

@article{MACK:94,
 ISSN = {07350015},
 OPTURL = {https://www.jstor.org/stable/1391481},
 abstract = {This article uses Monte Carlo experiments and response
                  surface regressions in a novel way to calculate
                  approximate asymptotic distribution functions for
                  several well-known unit-root and cointegration test
                  statistics. These allow empirical workers to
                  calculate approximate P values for these tests. The
                  results of the article are based on an extensive set
                  of Monte Carlo experiments, which yield
                  finite-sample quantiles for several sample
                  sizes. Based on these, response surface regressions
                  are used to obtain asymptotic quantiles for many
                  different test sizes. Then approximate distribution
                  functions with simple functional forms are estimated
                  from these asymptotic quantiles.},
 author = {James G. MacKinnon},
 journal = {Journal of Business & Economic Statistics},
 number = {2},
 pages = {167--176},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
 title = {Approximate Asymptotic Distribution Functions for Unit-Root and Cointegration Tests},
 volume = {12},
 year = {1994}
}

@article{MACK:96,
 ISSN = {08837252},
 OPTURL = {https://www.jstor.org/stable/2285154},
 author = {James G. MacKinnon},
 journal = {Journal of Applied Econometrics},
 number = {6},
 pages = {601--618},
 title = {Numerical Distribution Functions for Unit Root and Cointegration Tests},
 volume = {11},
 year = {1996}
}

@article{KWIA:PHIL:SCHM:SHIN:92,
title = "Testing the null hypothesis of stationarity against the
                  alternative of a unit root: How sure are we that
                  economic time series have a unit root?",
journal = "Journal of Econometrics",
volume = "54",
number = "1",
pages = "159 - 178",
year = "1992",
issn = "0304-4076",
OPTdoi = "https://doi.org/10.1016/0304-4076(92)90104-Y",
url = "https://doi.org/10.1016/0304-4076(92)90104-Y",
author = "Denis Kwiatkowski and Peter C.B. Phillips and Peter Schmidt and Yongcheol Shin",
abstract = "We propose a test of the null hypothesis that an
                  observable series is stationary around a
                  deterministic trend. The series is expressed as the
                  sum of deterministic trend, random walk, and
                  stationary error, and the test is the LM test of the
                  hypothesis that the random walk has zero
                  variance. The asymptotic distribution of the
                  statistic is derived under the null and under the
                  alternative that the series is
                  difference-stationary. Finite sample size and power
                  are considered in a Monte Carlo experiment. The test
                  is applied to the Nelson-Plosser data, and for many
                  of these series the hypothesis of trend stationarity
                  cannot be rejected."
}


@article{AHRE:PINC:81,
author = {Ahrens, H. and Pincus, R.},
title = {On Two Measures of Unbalancedness in a One-Way Model and
         Their Relation to Efficiency},
journal = {Biometrical Journal},
volume = {23},
number = {3},
pages = {227-235},
doi = {10.1002/bimj.4710230302},
abstract = {Abstract This paper discusses two measures of
                  unbalancedness in a one-way model and shows that for
                  a given statistical procedure they may serve as
                  measures of efficiency of a design. They also allow
                  to compare for example estimation methods for
                  variance components in designs with a fixed level of
                  unbalancedness.},
year = {1981}
}

@article{BALT:SONG:JUNG:02,
 ISSN = {13684221, 1368423X},
 OPTURL = {https://www.jstor.org/stable/23114905},
 abstract = {This paper considers the unbalanced two-way error
                  component model studied by Wansbeek and Kapteyn
                  (1989). Alternative analysis of variance (ANOVA),
                  minimum norm quadratic unbiased and restricted
                  maximum likelihood (REML) estimation procedures are
                  proposed. The mean squared error performance of
                  these estimators are compared using Monte Carlo
                  experiments. Results show that for the estimates of
                  the variance components, the computationally more
                  demanding maximum likelihood (ML) and minimum
                  variance quadratic unbiased (MIVQUE) estimators are
                  recommended, especially if the unbalanced pattern is
                  severe. However, focusing on the regression
                  coefficient estimates, the simple ANOVA methods
                  perform just as well as the computationally
                  demanding ML and MIVQUE methods and are
                  recommended.},
 author = {Badi H. Baltagi and Seuck H. Song and Byoung C. Jung},
 journal = {The Econometrics Journal},
 number = {2},
 pages = {480--493},
 publisher = {[Royal Economic Society, Wiley]},
 title = {A comparative study of alternative estimators for the
                  unbalanced two-way error component regression model},
 volume = {5},
 year = {2002}
}

@misc{GOUL:13,
author = "Gould, W.",
year = 2013,
url = {https://www.stata.com/support/faqs/statistics/intercept-in-fixed-effects-model/},
title = {How can there be an intercept in the fixed-effects model
                  estimated by xtreg, fe?}
}

@Article{HOEC:07,
  author={Daniel Hoechle},
  title={{Robust standard errors for panel regressions with cross-sectional dependence}},
  journal={Stata Journal},
  year=2007,
  volume={7},
  number={3},
  pages={281-312},
  month={September},
  keywords={xtscc; robust standard errors; nonparametric covariance estimation},
  doi={},
  abstract={I present a new Stata program, xtscc, that estimates
                  pooled ordinary least-squares/weighted least-squares
                  regression and fixed-effects (within) regression
                  models with Driscoll and Kraay (Review of Economics
                  and Statistics 80: 549–560) standard errors. By
                  running Monte Carlo simulations, I compare the
                  finite-sample properties of the cross-sectional
                  dependence-consistent Driscoll-Kraay estimator with
                  the properties of other, more commonly used
                  covariance matrix estimators that do not account for
                  cross-sectional dependence. The results indicate
                  that Driscol-Kraay standard errors are well
                  calibrated when cross-sectional dependence is
                  present. However, erroneously ignoring
                  cross-sectional correlation in the estimation of
                  panel models can lead to severely biased statistical
                  results. I illustrate the xtscc program by
                  considering an application from empirical
                  finance. Thereby, I also propose a Hausman-type test
                  for fixed effects that is robust to general forms of
                  cross-sectional and temporal dependence. Copyright
                  2007 by StataCorp LP.},
  url={https://ideas.repec.org/a/tsj/stataj/v7y2007i3p281-312.html}
}



@manual{THER:14,
	author={Terry Therneau},
	title={\pkg{bdsmatrix}: Routines for Block Diagonal Symmetric matrices},
	year={2014},
	note={\proglang{R} package version 1.3-2},
	url={https://CRAN.R-project.org/package=bdsmatrix}
}

@Article{COTT:2017,
  author  = {Cottrell, A.},
  title   = {Random effects estimators for unbalanced panel data: a
                  Monte Carlo analysis},
  journal = {gretl working papers},
  year    = {2017},
  number  = {4},
  url     = {https://EconPapers.repec.org/RePEc:anc:wgretl:4}
}

@article{CHOI:01,
title = "Unit root tests for panel data",
journal = "Journal of International Money and Finance",
volume = "20",
number = "2",
pages = "249 - 272",
year = "2001",
issn = "0261-5606",
OPTdoi = "https://doi.org/10.1016/S0261-5606(00)00048-6",
url = "https://doi.org/10.1016/S0261-5606(00)00048-6",
author = "In Choi",
keywords = "Unit root test, Panel data, Meta-analysis, Purchasing power parity",
abstract = "This paper develops unit root tests for panel data. These
                  tests are devised under more general assumptions
                  than the tests previously proposed. First, the
                  number of groups in the panel data is assumed to be
                  either finite or infinite. Second, each group is
                  assumed to have different types of nonstochastic and
                  stochastic components. Third, the time series spans
                  for the groups are assumed to be all
                  different. Fourth, the alternative where some groups
                  have a unit root and others do not can be dealt with
                  by the tests. The tests can also be used for the
                  null of stationarity and for cointegration, once
                  relevant changes are made in the model, hypotheses,
                  assumptions and underlying tests. The main idea for
                  our unit root tests is to combine p-values from a
                  unit root test applied to each group in the panel
                  data. Combining p-values to formulate tests is a
                  common practice in meta-analysis. This paper also
                  reports the finite sample performance of our
                  combination unit root tests and Im et al.'s [Mimeo
                  (1995)] t-bar test. The results show that most of
                  the combination tests are more powerful than the
                  t-bar test in finite samples. Application of the
                  combination unit root tests to the post-Bretton
                  Woods US real exchange rate data provides some
                  evidence in favor of the PPP hypothesis."
}

@article{HADR:00,
 ISSN = {13684221, 1368423X},
 OPTURL = {https://www.jstor.org/stable/23114886},
 abstract = {This paper proposes a residual-based Lagrange multiplier
                  (LM) test for a null that the individual observed
                  series are stationary around a deterministic level
                  or around a deterministic trend against the
                  alternative of a unit root in panel data. The tests
                  which are asymptotically similar under the null,
                  belong to the locally best invariant (LBI) test
                  statistics. The asymptotic distributions of the
                  statistics are derived under the null and are shown
                  to be normally distributed. Finite sample sizes and
                  powers are considered in a Monte Carlo
                  experiment. The empirical sizes of the tests are
                  close to the true size even in small samples. The
                  testing procedure is easy to apply, including, to
                  panel data models with fixed effects, individual
                  deterministic trends and heterogeneous errors across
                  cross-sections. It is also shown how to apply the
                  tests to the more general case of serially
                  correlated disturbance terms.},
 author = {Kaddour Hadri},
 journal = {The Econometrics Journal},
 number = {2},
 pages = {148--161},
 publisher = {[Royal Economic Society, Wiley]},
 title = {Testing for stationarity in heterogeneous panel data},
 volume = {3},
 year = {2000}
}


@book{CROI:MILL:18,
   title = {Panel Data Econometrics with R},
   author={Croissant, Yves and Millo, Giovanni},
   publisher={John Wiley and Sons ltd},
   year={2018},
   edition={1st}
}

@Article{HANCK:13,
  author    = {Christoph Hanck},
  title     = {An Intersection Test for Panel Unit Roots},
  journal   = {Econometric Reviews},
  year      = {2013},
  OPTkey    = {},
  volume    = {32},
  OPTnumber = {2},
  pages     = {183-203},
  OPTmonth  = {},
  OPTnote   = {},
  OPTannote = {}
}

@Article{SIMES:86,
  author    = {R. J. Simes},
  title     = {An improved Bonferroni procedure for multiple tests of significance},
  journal   = {Biometrika},
  year      = {1986},
  OPTkey    = {},
  volume    = {73},
  OPTnumber = {3},
  pages     = {751-754},
  OPTmonth  = {},
  OPTnote   = {},
  OPTannote = {}
}

@Article{HOMM:88,
  author    = {G. Hommel},
  title     = {A stage wise rejective multiple test procedure based on a modified
  Bonferroni test},
  journal   = {Biometrika},
  year      = {1988},
  OPTkey    = {},
  volume    = {75},
  OPTnumber = {2},
  pages     = {383-386},
  OPTmonth  = {},
  OPTnote   = {},
  OPTannote = {}
}

@article{POI:03,
  title={From the help desk: Swamy’s random-coefficients model},
  author={Poi, Brian P.},
  journal={Stata Journal},
  volume={3},
  number={3},
  pages={302--308},
  year={2003},
  publisher={StataCorp LP}
}