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L A T EX for Economics and Business Administration Thomas de - PowerPoint PPT Presentation

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L A T EX for Economics and Business Administration Thomas de Graaff January 13, 2020 1 Introduction Why this workshop? In the social sciences few attention to what tools to use (and why) you just use what colleagues, friends or

  1. L A T EX for Economics and Business Administration Thomas de Graaff January 13, 2020 1

  2. Introduction

  3. Why this workshop? • In the social sciences few attention to what tools to use (and why) • you just use what colleagues, friends or teachers used • huge fixed (and sometimes sunk) costs 2

  4. Why this workshop? • In the social sciences few attention to what tools to use (and why) • you just use what colleagues, friends or teachers used • huge fixed (and sometimes sunk) costs • Increasing use of L A T EX • more user friendly (editors, online environments) • combination with markdown used on internet/blogs • tight connection with (statistical) software ( R / Python / Stata ) • combination with data science 2

  5. What I want (and don’t want) with this workshop • Give a general introduction of why some tools work together • L A T EX • reference managers • (statistical) output • Give an introduction to L A T EX • First vanilla basics (including references) • Next workshop: more advanced stuff • What I do not want • Tell you what applications to use (you need to decide and make a well-informed decision) 3

  6. Background • T EX created by Donald Knuth (70’s) • L A T EX is a set of macro’s around TeX (1986) • L A T EX is a typesetting program, not a Word processor • So edit code that needs to be compiled • Editors • specific: TeXstudio, “Beware of bugs in the above code; I TeXshop, Rstudio • general: Sublime, have only proved it correct, not tried it.” Atom, Vim, Emacs 4

  7. Showcase: Tufte lay-out style 5

  8. Showcase: tikz and PGFPlots ω L B V L ( ω, r ; s ) = v 2 1 . 5 1 0 . 5 0 2 0 1 . 5 0 . 5 V H ( ω, r ; s ) = v K A K B 1 1 D 0 . 5 1 . 5 B C H ( ω, r ; s ) = κ C A 0 2 C L ( ω, r ; s ) = κ 0 0 . 5 1 1 . 5 2 L A r 6

  9. Showcase: posters Stochasticfrontiermodelswithspatialdependence Thomas de Graaff tgraaff@feweb.vu.nl VU University Amsterdam & Netherlands Environmental Assessment Agency The problem and research aim Technical inefficiencies in Europe’s manufacturing: an application Estimation of technical efficiencies may be biased in the presence of spatial dependence or unobserved spatial het- erogeneity amongst regions. Te aim is therefore to simultaneously model and consistently estimate a model that incorporates both technical inefficiencies and spatial dependence. Efficiencies 0.0 - 0.1 x 2 x 2 0.2 0.3 0.4 A 1 A 2 A 1 A 2 0.5 A 3 A 3 Country A 0.6 0.7 0.8 0.9 TE B 1.0 Y Y Y A ˆ ˆ ˆ Y TE ˆ Y TE Y B A TE B 3 B 3 B 2 B 2 B 1 Country B B 1 x 1 x 1 Lef: Standard technical efficiencies Right: with additional spatial lag Stochastic production frontiers Introducing a spatial lag Empirical specification Conclusions Assume that regional production, y , can be modeled as: Because multivariate skew-normal distributions are We estimate for the period 1991–2008 a neoclassical 1. Spatial dependence and stochastic frontiers can be closed under affine transformations (similarly to normal y = f ( X ; β T ) TE , growth model of the manufacturing sector across 273 Eu- simulteneously and consistently estimated using distributions), we may write: ropean NUTS-2 regions with the following specification: multivariate skew-normal distribution functions B ln ( y ) = ln ( X ) β + ξ ln y ( t ) where X are regional production factors, β the pa- (1) y ( 0 ) = β 0 + β 1 ln y ( 0 ) + β 2 ln s + β 3 ln ( n + 0.05 ) + ξ 2. In the presence of spatial dependence, regional rameters of the production function and TE is the re- technical inefficiency differences can be signifi- where B = ( I − ρ W ) and with ξ again a multivariate skew gional specific technical efficiency. By assuming a Cobb- cantly mitigated Douglas and that TE = exp (− u ) , we get: normal distribution with Ω = ω ( B ′ B ) − 1 ) . Tis leads to where s is the savings rate and y ( t ) the GVA in manufac- the following loglikelihood: ln y = ln ( X ) β − u + v , turing measured at time t , n the manufacturing working Key references population growth rate, ξ is skew-normally distributed − n 2 ln ( πω 2 ) + ln ∣ B ∣ − e ′ e 2 ω 2 + ∑ ln2Φ ( α ω e ) and the convergence rate across regions is calculated as: λ = − ln ( 1 + ˆ β 1 ) . [1] A breu , M. Spatial Determinants of Economic Growth ˆ Using skew-normal distributions and Technology Diffusion . Tela Tesis Publishers, where e is the vector of residuals of model (1). Amsterdam, 2005. Let u and v be distributed as: Estimation results Ω ∗ = ( 1 − δ T [2] A igner , D. J., L ovell , C. A. K., and S chmidt , P. ( u v ) ∼ N ( 0, Ω ∗ ) , Ω ) − δ Finding technical inefficiencies Formulation and Estimation of Stochastic Produc- Variable Growth Frontier Spatial We need to find TE = exp ( u ) or E ( u ∣ ξ ) given that u < 0. √ tion Frontier Models. Journal of Econometrics 6 We are interested in ξ = ln y − ln X = Pr ( v ∣ u < 0 ) (via model model frontier Because we can write as well ξ = δ ∣ u ∣ + ( 1 − δ 2 ) v (1977), 21–37. conditioning): leading to ln y ∼ SN ( ln ( X ) β , Ω, α ) ; a 0.30 † 0.68 ∗∗ with δ < 0 (via convolution), where u ∼ N ( 0, 1 ) and Constant 0.07 v ∼ N ( 0, Ω ) , the following general expression holds: ln ( y 0 ) -0.50 ∗∗ -0.47 ∗∗ -0.41 ∗∗ [3] A zzalini , A., and C apitanio , A. Statistical Appli- multivariate skew-normal distribution with: ln s 0.55 ∗∗ 0.51 ∗∗ 0.45 ∗∗ cation of the Multivariate Skew-Normal distribution. u ∣ ξ ∼ N c (( D ′ Σ − 1 D + I ) f ( ln y ) = 2 ϕ ( ln y − ln ( X ) β ; Ω ) Φ ( α ω ( ln y − ln ( X ) β ) − 1 D ′ Σ − 1 e , ( D ′ Σ − 1 D + I ) − 1 ) ln ( n + g + δ ) -0.19 ∗∗ -0.18 ∗∗ -0.18 ∗∗ Journal of Royal Statistical Society 61 (1999), 579–602. ω 0.30 ∗∗ 0.44 ∗∗ 0.36 ∗∗ α -2.91 ∗∗ -2.57 ∗∗ [4] D ominguez -M olina , J. A., G onzalez -F arias , G., where N c indicates a normal distribution truncated at 0, ρ 0.91 ∗∗ and R amos -Q uiroga , R. Skew-normality in • ω is a scale parameter √ D is a diagonal matrix with δ ’s on the diagonal and Σ stochastic frontier analysis. In Ske w-Elliptical dis- ( I − D 2 ) Ω. Te expectation can now be readily Logl. -0.21 0.21 0.36 • α is a measure of skewness equals ˆ 0.053 ∗∗ 0.049 ∗∗ 0.041 ∗∗ tributions and their applications , M. G. Genton, Ed. λ • α = ( 1 − δ T Ω − 1 δ ) derived. Chapman & Hall/CRC, 2004, ch. 13, pp. 223–242. − 1 / 2 Ω − 1 δ Significance levels : † : 10% ∗ : 5% ∗∗ : 1% 7

  10. Disadvantages • not WYSIWYG 8

  11. Disadvantages • not WYSIWYG • you nead to learn (quite) some commands • Learning curve, but • hurray for cheat sheets and Google 8

  12. Disadvantages • not WYSIWYG • you nead to learn (quite) some commands • Learning curve, but • hurray for cheat sheets and Google • Difficult to cooperate with people from the other side 8

  13. Disadvantages • not WYSIWYG • you nead to learn (quite) some commands • Learning curve, but • hurray for cheat sheets and Google • Difficult to cooperate with people from the other side • Basic L A T EX has difficulties with incorporating new fonts (Hoefler, minion pro) • XeTeX • For the purists: L A T EX does it right (L A T EX vs Word) 8

  14. Disadvantages • not WYSIWYG • you nead to learn (quite) some commands • Learning curve, but • hurray for cheat sheets and Google • Difficult to cooperate with people from the other side • Basic L A T EX has difficulties with incorporating new fonts (Hoefler, minion pro) • XeTeX • For the purists: L A T EX does it right (L A T EX vs Word) • Difficult to create unstructured and ugly documents 8

  15. Advantages • free (as in beer & in speach) 9

  16. Advantages • free (as in beer & in speach) • WYSIWYM 9

  17. Advantages • free (as in beer & in speach) • WYSIWYM • consistent lay-out throughout the whole document (including tables, appendices, formulas, source code, etc) 9

  18. Advantages • free (as in beer & in speach) • WYSIWYM • consistent lay-out throughout the whole document (including tables, appendices, formulas, source code, etc) • internal references are a breeze (citations, ToC, ToT . . . ) 9

  19. Advantages • free (as in beer & in speach) • WYSIWYM • consistent lay-out throughout the whole document (including tables, appendices, formulas, source code, etc) • internal references are a breeze (citations, ToC, ToT . . . ) • forced to structure documents 9

  20. Advantages • free (as in beer & in speach) • WYSIWYM • consistent lay-out throughout the whole document (including tables, appendices, formulas, source code, etc) • internal references are a breeze (citations, ToC, ToT . . . ) • forced to structure documents • macros around plain text, thus scriptable 9

  21. Advantages • free (as in beer & in speach) • WYSIWYM • consistent lay-out throughout the whole document (including tables, appendices, formulas, source code, etc) • internal references are a breeze (citations, ToC, ToT . . . ) • forced to structure documents • macros around plain text, thus scriptable • large community, thus a package for almost everything (books, articles, presentation, posters, exams, musicscores) 9

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