nlsur.bib

@inbook{stata_nlsur,
	author = {{StataCorp LP}},
	booktitle = {{Stata Base Rerference Manual Release 14}},
	edition = {Version 14},
	isbn = {1-59718-164-1},
	location = {College Station, Texas},
	publisher = {Stata Press},
	title = {{nlsur - Estimation of nonlinear systems of equations}},
	url = {http://www.stata.com/manuals13/rnlsur.pdf},
	year = {2015}
}

@book{Greene,
	author = {Greene, William H.},
	edition = {7. Auflage},
	location = {Edinburgh Gate, Harlow},
	publisher = {Pearson Education Limited},
	title = {{Econometric Analysis - International Edition}},
	year = {2012}
}

@manual{Matrix,
	author = {Bates, Douglas and Maechler, Martin},
	note = {R package version 1.2-2},
	title = {{Matrix: Sparse and Dense Matrix Classes and Methods}},
	url = {http://CRAN.R-project.org/package=Matrix},
	year = {2015}
}

@article{Hartley1961,
	abstract = {},
	author = {Hartley, H. O.},
	copyright = {Copyright {\copyright} 1961 American Statistical Association and American Society for Quality},
	issn = {00401706},
	journal = {Technometrics},
	jstor_articletype = {research-article},
	jstor_formatteddate = {May, 1961},
	jstor_issuetitle = {},
	language = {English},
	number = {2},
	pages = {pp. 269--280},
	publisher = {Taylor \& Francis, Ltd. on behalf of American Statistical Association and American Society for Quality},
	title = {{The Modified Gauss-Newton Method for the Fitting of Non-Linear Regression Functions by Least Squares}},
	url = {http://www.jstor.org/stable/1266117},
	volume = {3},
	year = {1961}
}

@article{Box1960,
	author = {Box, George E. P.},
	doi = {10.1111/j.1749-6632.1960.tb42843.x},
	issn = {1749-6632},
	journal = {Annals of the New York Academy of Sciences},
	number = {3},
	pages = {792--816},
	publisher = {Blackwell Publishing Ltd},
	title = {{Fitting Empirical Data}},
	url = {http://dx.doi.org/10.1111/j.1749-6632.1960.tb42843.x},
	volume = {86},
	year = {1960}
}

@book{Bates1988,
	abstract = {The prelims comprise:


* The Nonlinear Regression Model

* Determining the Least Squares Estimates

* Nonlinear Regression Inference Using the Linear Approximation

* Nonlinear Least Squares via Sums of Squares

* Use of the Linear Approximation
},
	address = {New York},
	author = {Bates, Douglas M. and Watts, Donald G.},
	doi = {10.1002/9780470316757.ch2},
	isbn = {9780470316757},
	keywords = {nonlinear regression model; expectation surface; least squares estimates; Gauss-Newton method; nonlinear regression inference},
	publisher = {John Wiley \& Sons, Inc.},
	title = {{Nonlinear Regression Analysis and Its Applications}},
	url = {http://dx.doi.org/10.1002/9780470316757.ch2},
	year = {2008}
}

@inbook{Davidson2004,
	author = {Davidson, Russel and MacKinnon, James G.},
	booktitle = {{Econometric Theory and Methods}},
	chapter = {6},
	location = {New York},
	pages = {213--256},
	publisher = {Oxford University Press},
	title = {{Nonlinear Regression}},
	year = {2004}
}

@article{RcppArmadillo2014,
	author = {Eddelbuettel, Dirk and Sanderson, Conrad},
	journal = {Computational Statistics and Data Analysis},
	month = {March},
	pages = {1054--1063},
	title = {{RcppArmadillo: Accelerating R with high-performance C++ linear algebra}},
	url = {http://dx.doi.org/10.1016/j.csda.2013.02.005},
	volume = {71},
	year = {2014}
}

@book{Gallant1987,
	author = {Gallant, A. Ronald},
	location = {New York},
	publisher = {John Wiley \& Sons},
	title = {{Nonlinear Statistical Models}},
	year = {1987}
}

@manual{R,
	address = {Vienna, Austria},
	author = {{R Core Team}},
	organization = {R Foundation for Statistical Computing},
	title = {{R: A Language and Environment for Statistical Computing}},
	url = {https://www.R-project.org/},
	year = {2015}
}

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

@book{Judge88,
	added-at = {2009-08-21T09:55:59.000+0200},
	address = {New York [u.a.]},
	author = {Judge, {George G.} and Hill, {R.} Carter and Griffiths, {William E.} and L{\"u}tkepohl, {Helmut} and Lee, {Tsoung-Chao}},
	edition = {2. ed.},
	isbn = {0471624144},
	keywords = {USA {\"O}konometrie Methoden\_und\_Techniken\_der\_Volkswirtschaft},
	pagetotal = {XXXVII, 1024},
	ppn_gvk = {027542998},
	publisher = {Wiley},
	timestamp = {2009-08-21T09:57:44.000+0200},
	title = {{Introduction to the theory and practice of econometrics}},
	x-fetchedfrom = {Bibsonomy},
	year = 1988
}

@book{car,
	address = {Thousand Oaks {CA}},
	author = {Fox, John and Weisberg, Sanford},
	edition = {Second},
	publisher = {Sage},
	title = {{An {R} Companion to Applied Regression}},
	url = {http://socserv.socsci.mcmaster.ca/jfox/Books/Companion},
	year = {2011}
}

@article{Berndt1975,
	author = {Berndt, Ernst R and Wood, David O},
	journal = {The review of Economics and Statistics},
	pages = {259--268},
	publisher = {JSTOR},
	title = {{Technology, prices, and the derived demand for energy}},
	url = {http://www.jstor.org/stable/1923910},
	x-fetchedfrom = {Google Scholar},
	year = {1975}
}

@article{Zellner62,
	abstract = {In this paper a method of estimating the parameters of a set of regression
	equations is reported which involves application of Aitken's generalized
	least-squares [1] to the whole system of equations. Under conditions
	generally encountered in practice, it is found that the regression
	coefficient estimators so obtained are at least asymptotically more
	efficient than those obtained by an equation-by-equation application
	of least squares. This gain in efficiency can be quite large if "independent"
	variables in different equations are not highly correlated and if
	disturbance terms in different equations are highly correlated. Further,
	tests of the hypothesis that all regression equation coefficient
	vectors are equal, based on "micro" and "macro" data, are described.
	If this hypothesis is accepted, there will be no aggregation bias.
	Finally, the estimation procedure and the "micro-test" for aggregation
	bias are applied in the analysis of annual investment data, 1935-1954,
	for two firms.},
	author = {Zellner, Arnold},
	copyright = {Copyright {\^A}{\copyright} 1962 American Statistical Association},
	issn = {01621459},
	journal = {Journal of the American Statistical Association},
	jstor_articletype = {research-article},
	jstor_formatteddate = {Jun., 1962},
	language = {English},
	number = {298},
	pages = {348--368},
	publisher = {American Statistical Association},
	title = {{An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias}},
	url = {http://www.jstor.org/stable/2281644},
	volume = {57},
	year = {1962}
}

@article{systemfit,
	author = {Henningsen, Arne and Hamann, Jeff D.},
	journal = {Journal of Statistical Software},
	number = {4},
	pages = {1--40},
	title = {{systemfit: A Package for Estimating Systems of Simultaneous Equations in R}},
	url = {http://www.jstatsoft.org/v23/i04/},
	volume = {23},
	year = {2007}
}

@manual{ucminf,
	author = {Nielsen, Hans Bruun and Mortensen, Stig Bousgaard},
	note = {R package version 1.1-3},
	title = {{ucminf: General-purpose unconstrained non-linear optimization}},
	url = {https://CRAN.R-project.org/package=ucminf},
	year = {2012}
}

@techreport{Nielsen2000,
	author = {Nielsen, Hans Bruun},
	location = {Lyngby},
	number = {IMM-REP-2000-19},
	publisher = {Department Of Mathematical Modelling},
	school = {Technical University of Denmark},
	series = {{IMM}},
	title = {{UCMINF -- An Algorithm For Unconstrained, Nonlinear Optimization}},
	type = {Report},
	url = {http://www.imm.dtu.dk/documents/ftp/tr00/tr19_00.pdf},
	year = {2000}
}

@inbook{stata_nlcom,
	author = {{StataCorp LP}},
	booktitle = {{Stata Base Rerference Manual Release 14}},
	edition = {Version 14},
	isbn = {1-59718-164-1},
	location = {College Station, Texas},
	publisher = {Stata Press},
	title = {{nlcom - Nonlinear combinations of estimators}},
	url = {http://www.stata.com/manuals13/rnlsur.pdf},
	year = {2015}
}

@book{Wooldridge02,
	address = {Cambridge, Massachusetts},
	author = {Wooldridge, Jeffrey M.},
	publisher = {The MIT Press},
	title = {{Econometric Analysis of Cross Section and Panel Data}},
	year = {2002}
}

@article{Bates81,
	author = {Bates, Douglas M. and Watts, Donald G.},
	journal = {Technometrics},
	month = may,
	number = {2},
	title = {{A Relative Offset Orthogonality Convergence Criterion for Nonlinear Least Squares}},
	volume = {23},
	year = {1981}
}

@manual{Henningsen14,
	author = {Henningsen, Arne},
	note = {R package version 0.6-16},
	title = {{micEconAids: Demand Analysis with the Almost Ideal Demand System (AIDS)}},
	url = {https://CRAN.R-project.org/package=micEconAids},
	year = {2014}
}

@article{Deaton80,
	author = {Deaton, Angus and Muellbauer, John},
	copyright = {Copyright {\^A}{\copyright} 1980 American Economic Association},
	issn = {00028282},
	journal = {The American Economic Review},
	jstor_articletype = {research-article},
	jstor_formatteddate = {Jun., 1980},
	language = {English},
	number = {3},
	owner = {jmg},
	pages = {312--326},
	publisher = {American Economic Association},
	timestamp = {2011.11.29},
	title = {{An Almost Ideal Demand System}},
	url = {http://www.jstor.org/stable/1805222},
	volume = {70},
	year = {1980}
}

@book{Blanciforti86,
	author = {Blanciforti, Laura and Green, Richard D. and King, Gordon A.},
	location = {University of California, Davis},
	month = aug,
	series = {{Giannini Foundation Monograph Number 40}},
	title = {{U.S. Consumer Behavior Over the Postwar Period: An Almost Ideal Demand System Analysis}},
	url = {http://s.giannini.ucop.edu/uploads/giannini_public/dd/ca/ddca8064-0a64-46e7-bd3a-0720626071bd/40-postwar.pdf},
	year = {1986}
}

@article{Banks97,
	abstract = {This paper presents a model of consumer demand that is consistent
	with the observed expenditure patterns of individual consumers in
	a long time series of expenditure surveys and is also able to provide
	a detailed welfare analysis of shifts in relative prices. A nonparametric
	analysis of consumer expenditure patterns suggests that Engel curves
	require quadratic terms in the logarithm of expenditure. While popular
	models of demand such as the Translog or the Almost Ideal Demand
	Systems do allow flexible price responses within a theoretically
	coherent structure, they have expenditure share Engel curves that
	are linear in the logarithm of total expenditure. We derive the complete
	class of integrable quadratic logarithmic expenditure share systems.
	A specification from this class is estimated on a large pooled data
	set of U.K. households. Models that fail to account for Engel curvature
	are found to generate important distortions in the patterns of welfare
	losses associated with a tax increase.},
	author = {Banks, James and Blundell, Richard and Lewbel, Arthur},
	copyright = {Copyright {\^A}{\copyright} 1997 The MIT Press},
	issn = {00346535},
	journal = {The Review of Economics and Statistics},
	jstor_articletype = {research-article},
	jstor_formatteddate = {Nov., 1997},
	language = {English},
	number = {4},
	pages = {527--539},
	publisher = {The MIT Press},
	title = {{Quadratic Engel Curves and Consumer Demand}},
	url = {http://www.jstor.org/stable/2951405},
	volume = {79},
	year = {1997}
}

@article{Ray83,
	abstract = {The {\lq}cost of a child{\rq} or, the general equivalence scale as it is more commonly known, is a concept of considerable importance in matters relating to public policy and welfare. It has played a crucial part in numerous theoretical and empirical studies relating to poverty, income distribution, dietary needs, income maintenance programs, supplementary and child benefits and other forms of social security payments in a welfare state, and in assessing the horizontal equity of income taxation. In spite of a long tradition of estimating the scale from observed household budget data, there remain severe problems in estimation and interpretation of the scales. This paper proposes a new methodology for calculating the scale using a framework which is consistent with utility theory and which overcomes the identification problem without having to enforce the arbitrary prior assumptions of recent studies. The proposed method allows easy calculation of not only the basic {\lq}scale{\rq} parameter but also how it varies with price and reference utility. We illustrate the usefulness of the procedure by estimating on U.K. budget data at two different levels of aggregation and employing two sets of quite different functional forms. The results are plausible, compare favourably with one another and, hence, confirm the robustness and usefulness of the proposed procedure.},
	author = {Ray, Ranjan},
	issn = {0047-2727},
	journal = {Journal of Public Economics},
	note = {demographische Skalierung, les},
	number = {1},
	pages = {89--102},
	title = {{Measuring the costs of children: An alternative approach}},
	url = {http://www.sciencedirect.com/science/article/pii/0047272783900580},
	volume = {22},
	year = {1983}
}

@article{Poi12,
	abstract = {Previously, to fit an almost\endashideal demand system in Stata, one
	would have to use the nlsur command and write a function evaluator
	program as described in [R] nlsur and Poi (2008, Stata Journal 8:
	554\endash556). In this article, I introduce the command quaids,
	which obviates the need for any programming by the user. The command
	fits Deaton and Muellbauer{\rq}s (1980b, American Economic Review 70:
	312\endash326) original almost\endashideal demand\endashsystem model
	as well as Banks, Blundell, and Lewbel{\rq}s (1997, Review of Economics
	and Statistics 79: 527\endash539) quadratic variant. Demographic
	variables can also be included in the model. Postestimation tools
	calculate expenditure and price elasticities.},
	address = {College Station, TX},
	author = {Poi, Brian P.},
	journal = {Stata Journal},
	number = {3},
	pages = {433--446(14)},
	publisher = {Stata Press},
	title = {{Easy demand-system estimation with quaids}},
	url = {http://www.stata-journal.com/article.html?article=st0268},
	volume = {12},
	year = {2012}
}