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Identifying the determinants of foreign direct investment: a data-specific model selection approach
In this paper, we examine the potential determinants of foreign direct investment. For this purpose, we apply new exact subset selection procedures, which are based on idealized assumptions, as well as their possibly more plausible empirical counterparts to an international data set to select the op...
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Published in: | Statistical papers (Berlin, Germany) Germany), 2012-08, Vol.53 (3), p.739-752 |
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cites | cdi_FETCH-LOGICAL-c382t-7b8c3b3192d8a3f882d05f1a6a0dbed6b21d4844a97798f423406c3d20b5af493 |
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container_title | Statistical papers (Berlin, Germany) |
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creator | Reschenhofer, Erhard Schilde, Michael Oberecker, Eva Payr, Ellen Tandogan, Hasan T. Wakolbinger, Lea M. |
description | In this paper, we examine the potential determinants of foreign direct investment. For this purpose, we apply new exact subset selection procedures, which are based on idealized assumptions, as well as their possibly more plausible empirical counterparts to an international data set to select the optimal set of predictors. Unlike the standard model selection procedures
AIC
and
BIC
, which penalize only the number of variables included in a model, and the subset selection procedures
RIC
and
MRIC
, which consider also the total number of available candidate variables, our data-specific procedures even take the correlation structure of all candidate variables into account. Our main focus is on a new procedure, which we have designed for situations where some of the potential predictors are certain to be included in the model. For a sample of 73 developing countries, this procedure selects only four variables, namely imports, net income from abroad, gross capital formation, and GDP per capita. An important secondary finding of our study is that the data-specific procedures, which are based on extensive simulations and are therefore very time-consuming, can be approximated reasonably well by the much simpler exact methods. |
doi_str_mv | 10.1007/s00362-011-0377-2 |
format | article |
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AIC
and
BIC
, which penalize only the number of variables included in a model, and the subset selection procedures
RIC
and
MRIC
, which consider also the total number of available candidate variables, our data-specific procedures even take the correlation structure of all candidate variables into account. Our main focus is on a new procedure, which we have designed for situations where some of the potential predictors are certain to be included in the model. For a sample of 73 developing countries, this procedure selects only four variables, namely imports, net income from abroad, gross capital formation, and GDP per capita. An important secondary finding of our study is that the data-specific procedures, which are based on extensive simulations and are therefore very time-consuming, can be approximated reasonably well by the much simpler exact methods.</description><identifier>ISSN: 0932-5026</identifier><identifier>EISSN: 1613-9798</identifier><identifier>DOI: 10.1007/s00362-011-0377-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Approximation ; Developing countries ; Economic development ; Economic growth ; Economic Theory/Quantitative Economics/Mathematical Methods ; Economics ; Finance ; Foreign investment ; GDP ; Gross Domestic Product ; Independent variables ; Insurance ; Investigations ; LDCs ; Management ; Mathematical models ; Mathematics and Statistics ; Operations Research/Decision Theory ; Probability Theory and Stochastic Processes ; Regular Article ; Statistics ; Statistics for Business ; Studies ; Variables</subject><ispartof>Statistical papers (Berlin, Germany), 2012-08, Vol.53 (3), p.739-752</ispartof><rights>Springer-Verlag 2011</rights><rights>Springer-Verlag 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c382t-7b8c3b3192d8a3f882d05f1a6a0dbed6b21d4844a97798f423406c3d20b5af493</citedby><cites>FETCH-LOGICAL-c382t-7b8c3b3192d8a3f882d05f1a6a0dbed6b21d4844a97798f423406c3d20b5af493</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1026666999/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1026666999?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,11667,27901,27902,36037,36038,44339,74638</link.rule.ids></links><search><creatorcontrib>Reschenhofer, Erhard</creatorcontrib><creatorcontrib>Schilde, Michael</creatorcontrib><creatorcontrib>Oberecker, Eva</creatorcontrib><creatorcontrib>Payr, Ellen</creatorcontrib><creatorcontrib>Tandogan, Hasan T.</creatorcontrib><creatorcontrib>Wakolbinger, Lea M.</creatorcontrib><title>Identifying the determinants of foreign direct investment: a data-specific model selection approach</title><title>Statistical papers (Berlin, Germany)</title><addtitle>Stat Papers</addtitle><description>In this paper, we examine the potential determinants of foreign direct investment. For this purpose, we apply new exact subset selection procedures, which are based on idealized assumptions, as well as their possibly more plausible empirical counterparts to an international data set to select the optimal set of predictors. Unlike the standard model selection procedures
AIC
and
BIC
, which penalize only the number of variables included in a model, and the subset selection procedures
RIC
and
MRIC
, which consider also the total number of available candidate variables, our data-specific procedures even take the correlation structure of all candidate variables into account. Our main focus is on a new procedure, which we have designed for situations where some of the potential predictors are certain to be included in the model. For a sample of 73 developing countries, this procedure selects only four variables, namely imports, net income from abroad, gross capital formation, and GDP per capita. 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For this purpose, we apply new exact subset selection procedures, which are based on idealized assumptions, as well as their possibly more plausible empirical counterparts to an international data set to select the optimal set of predictors. Unlike the standard model selection procedures
AIC
and
BIC
, which penalize only the number of variables included in a model, and the subset selection procedures
RIC
and
MRIC
, which consider also the total number of available candidate variables, our data-specific procedures even take the correlation structure of all candidate variables into account. Our main focus is on a new procedure, which we have designed for situations where some of the potential predictors are certain to be included in the model. For a sample of 73 developing countries, this procedure selects only four variables, namely imports, net income from abroad, gross capital formation, and GDP per capita. An important secondary finding of our study is that the data-specific procedures, which are based on extensive simulations and are therefore very time-consuming, can be approximated reasonably well by the much simpler exact methods.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><doi>10.1007/s00362-011-0377-2</doi><tpages>14</tpages></addata></record> |
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source | EBSCOhost Business Source Ultimate; EBSCOhost Econlit with Full Text; ABI/INFORM Global; Springer Link |
subjects | Approximation Developing countries Economic development Economic growth Economic Theory/Quantitative Economics/Mathematical Methods Economics Finance Foreign investment GDP Gross Domestic Product Independent variables Insurance Investigations LDCs Management Mathematical models Mathematics and Statistics Operations Research/Decision Theory Probability Theory and Stochastic Processes Regular Article Statistics Statistics for Business Studies Variables |
title | Identifying the determinants of foreign direct investment: a data-specific model selection approach |
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