Embedding R in Windows applications, and executing R remotely

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Microeconomic Analysis with R Arne Henningsen Department of Agricultural Economics University of Kiel, Germany Since its first public release in 1993, the “R language and environment for statistical computing” (R Development Core Team, 2003) has been used more and more for statistical analyses. However, in the first years it was not much used by economists and econometricians, but this situation has been changing in recent years. One cornerstone was the article “R: Yet another econometric programming environment” by Cribari-Neto and Zarkos (1999). Three years later Racine and Hyndman (2002) published the article “Using R to teach econometrics”. And Arai (2002) has a “A brief guide to R for beginners in econometrics” in the web that has been updated and improved several times within the past one and a half years. Over the last years the number of R packages that are useful for economists also increased. One of these packages is called “systemfit” (Hamann and Henningsen, 2004) and provides functions to estimate systems of linear or non-linear equations. Many economic models consist of several equations, which should be estimated simultaneously, because the disturbance terms are likely contemporaneously correlated or the economic theory requires cross-equation restrictions. This is especially the case in microeconomic modeling. We extended the “systemfit” package to make it suitable for microeconomic modeling (e.g. incorporating cross-equation restrictions). Subsequently, we used it for several microeconomic demand and production analyses. The demand analyses were done with the “Almost Ideal Demand System” (AIDS) (Deaton and Muellbauer, 1980) and the production analyses with the “symmetric normalized quadratic” profit function. On the useR! conference we want to demonstrate this on a poster and on a laptop computer. Furthermore a first release of a new R package will be presented that contains functions for microeconomic modeling (e.g. a function that carries out a full demand analysis with the AIDS with only a single command). Applied economists interested in microeconomic modeling will be invited to contribute to this package by providing functions for other functional forms. References Arai, M. (2002). A brief guide to R for beginners in econometrics. http://people.su.se/ ~ma/R_intro/R_intro.pdf. Cribari-Neto, F. and Zarkos, S. G. (1999). R: Yet another econometric programming environment. Journal of Applied Econometrics, 14:319–329. Deaton, A. and Muellbauer, J. (1980). An Almost Ideal Demand System. The American Economic Review, 70:312–326. Hamann, J. D. and Henningsen, A. (2004). systemfit - simultaneous equation estimation package for R. http://cran.r-project.org/src/contrib/PACKAGES.html#systemfit. R Development Core Team (2003). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-00-3. Racine, J. and Hyndman, R. (2002). Using R to teach econometrics. Journal of Applied Econometrics, 17:175–189.
Page 1 and 2: Embedding R in Windows applications
Page 3 and 4: Using R in a Distributed Computer L
Page 5 and 6: hoa - A package bundle for higher o
Page 7 and 8: Using R for Statistical Seismology
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Page 11 and 12: Calculating the autocovariances of
Page 13 and 14: Using R for predicting air traffic
Page 15 and 16: Deployment of validated statistical
Page 17 and 18: RMAGEML: integrating MAGE-ML format
Page 19 and 20: Given the large, and growing, exten
Page 21 and 22: It is our intention to extend the m
Page 23 and 24: Programming with financial data: Co
Page 25 and 26: 2.1 The foundation provided by Knop
Page 27 and 28: • permits the choice for each nod
Page 29 and 30: Computational cost and time threads
Page 31 and 32: Getting Started With the R Commande
Page 33 and 34: Bayesian Methods in Geostatistics:
Page 35 and 36: The Extreme Value Toolkit (extRemes
Page 37 and 38: BayesMix: An R package for Bayesian
Page 39 and 40: The Statistical Lab - Teaching and
Page 41: PRSG Nirvana — A parallel tool to
Page 45 and 46: Multi-state Markov modelling with R
Page 47 and 48: metrics: Towards a package for doin
Page 49 and 50: Performance of Spinal Supporting Mu
Page 51 and 52: Distributions of finite and infinit
Page 53 and 54: RUnit - A Software Development And
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Page 57 and 58: An Adventure in randomForest - A Se
Page 59 and 60: Simulating Society in R András Lö
Page 61 and 62: MCMCpack: An Evolving R Package for
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Page 65 and 66: Taking into account spatial depende
Page 67 and 68: which correctly records in the char
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Page 71 and 72: userR!2004 Conference Abstract R at
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Page 75 and 76: Option pricing using R John Randal
Page 77 and 78: Bioassay analysis using R Christian
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Page 83 and 84: Multiple Imputation and Model Selec
Page 85 and 86: a. b. c. d. Figure 2: Influence by
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Page 89 and 90: JGR: a unified interface to R Marti
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Rough Set based Rule Induction Pack
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Using R in other Applications Vario
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Using R and SPSS simultaneously in
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