The Validity of Cross-Sectionally Estimated Behavior Equations in Time Series Applications
Regression estimates from cross-section and time series sample data are often different. Some reasons why these discrepancies arise are presented, along with quantative results for three investment functions, explaining investment by gross internal funds and the firm's capital stock, and using...
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Veröffentlicht in: | Econometrica 1959-04, Vol.27 (2), p.197-214 |
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description | Regression estimates from cross-section and time series sample data are often different. Some reasons why these discrepancies arise are presented, along with quantative results for three investment functions, explaining investment by gross internal funds and the firm's capital stock, and using individual firm observations. A substantial analytical advantage can be gained from having both time series and cross-sections for an identical group of firms so that the error variance structure for estimates based on both sorts of data can be analysed efficiently. |
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A substantial analytical advantage can be gained from having both time series and cross-sections for an identical group of firms so that the error variance structure for estimates based on both sorts of data can be analysed efficiently.</description><subject>Business cycles</subject><subject>Capital investments</subject><subject>Capital stock</subject><subject>Capital stocks</subject><subject>Coefficients</subject><subject>Dependent variables</subject><subject>Econometrics</subject><subject>Error rates</subject><subject>Estimates</subject><subject>Estimation bias</subject><subject>Fixed assets</subject><subject>Hypotheses</subject><subject>Independent variables</subject><subject>Influence</subject><subject>Investments</subject><subject>Profits</subject><subject>Regression coefficients</subject><subject>Standard error</subject><subject>Statistical variance</subject><subject>Time series</subject><subject>Time series 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Edwin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c211t-a0a32dff6cca526423195ab5bbe6fd0af0a6f73693d939af751afec27e7e035e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1959</creationdate><topic>Business cycles</topic><topic>Capital investments</topic><topic>Capital stock</topic><topic>Capital stocks</topic><topic>Coefficients</topic><topic>Dependent variables</topic><topic>Econometrics</topic><topic>Error rates</topic><topic>Estimates</topic><topic>Estimation bias</topic><topic>Fixed assets</topic><topic>Hypotheses</topic><topic>Independent variables</topic><topic>Influence</topic><topic>Investments</topic><topic>Profits</topic><topic>Regression coefficients</topic><topic>Standard error</topic><topic>Statistical variance</topic><topic>Time series</topic><topic>Time series 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Edwin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Validity of Cross-Sectionally Estimated Behavior Equations in Time Series Applications</atitle><jtitle>Econometrica</jtitle><date>1959-04-01</date><risdate>1959</risdate><volume>27</volume><issue>2</issue><spage>197</spage><epage>214</epage><pages>197-214</pages><issn>0012-9682</issn><eissn>1468-0262</eissn><coden>ECMTA7</coden><abstract>Regression estimates from cross-section and time series sample data are often different. Some reasons why these discrepancies arise are presented, along with quantative results for three investment functions, explaining investment by gross internal funds and the firm's capital stock, and using individual firm observations. A substantial analytical advantage can be gained from having both time series and cross-sections for an identical group of firms so that the error variance structure for estimates based on both sorts of data can be analysed efficiently.</abstract><cop>Evanston</cop><pub>The Econometric Society</pub><doi>10.2307/1909442</doi><tpages>18</tpages></addata></record> |
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source | Jstor Complete Legacy; JSTOR Mathematics & Statistics |
subjects | Business cycles Capital investments Capital stock Capital stocks Coefficients Dependent variables Econometrics Error rates Estimates Estimation bias Fixed assets Hypotheses Independent variables Influence Investments Profits Regression coefficients Standard error Statistical variance Time series Time series forecasting Variables |
title | The Validity of Cross-Sectionally Estimated Behavior Equations in Time Series Applications |
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