The Problem of Optimum Stratification

Dalenius, Tore

doi:10.1080/03461238.1950.10432042

Cited by 132 publications

(90 citation statements)

References 1 publication

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“…Dalenius (1950Dalenius ( , 1957 and others developed methods of approximating optimal stratum boundaries under Neyman or proportional allocation. Lavallée and Hidiroglou (1988) developed an algorithm for stratifying skewed distributions based on stratum boundary work by Sethi (1963).…”

Section: Optimizationmentioning

confidence: 99%

Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey

Harter¹,

Chen²,

McMichael

et al. 2017

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This PDF document was made available from www.rti.org as a public service of RTI International. More information about RTI Press can be found at http://www.rti.org/rtipress. RTI International is an independent, nonprofit research organization dedicated to improving the human condition by turning knowledge into practice. The RTI Press mission is to disseminate information about RTI research, analytic tools, and technical expertise to a national and international audience. RTI Press publications are peerreviewed by at least two independent substantive experts and one or more Press editors. Suggested Citation

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Section: Optimizationmentioning

confidence: 99%

Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey

Harter¹,

Chen²,

McMichael

et al. 2017

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“…Suppose a sample size n is to be taken from the whole population, and let nrs denote the sample size allocated to the (r, s) th stratum and zrsi the values of the population units in the (r, s) th , i = 1, 2, 3,… Let the variable Z be the study variable defined by where i = h + 1, h + 2,…, T -1, and j = k + 1, k + 2,…, U -1, and μrsz denotes the mean of the population for Z in (r, s) th stratum. The minimal equations given above were obtained by Dalenius (1950) when the stratification variable is same as the estimation variable. When the density functions of the auxiliary variables Y and X are known, then the distribution of Z is not known due to the auxiliary variables used to obtain optimum points.…”

Section: Stratification Pointsmentioning

confidence: 99%

“…Dalenius (1950) pioneered the work of obtaining optimum strata boundaries by minimizing the variance. See also Dalenius and Gurney (1951), Mahalanobis DANISH & RIZVI 3 (1952), Aoyama (1954), Dalenius and Hodges (1959), Durbin (1959), Singh (1975Singh ( , 1977, and Verma (2008).…”

Section: Introductionmentioning

confidence: 99%

Optimum stratification in bivariate auxiliary variables under Neyman allocation

Danish

Rizvi

2018

J. Mod. Appl. Stat. Methods

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“…Therefore each industry requires individual cut-offs, based on the distribution of employees within an industry. These cut-off values are determined according to Dalenius and Gurney (1951).…”

Section: Enterprise Panelmentioning

confidence: 99%

Macro and Micro Level Impulse Responses: A Survey Experimental Identification Procedure

et al. 2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis paper analyzes the effects of macroeconomic shocks on prices and output at different levels of aggregation using a bottom up approach. We show how to generate firm level impulse responses by incorporating experimental settings into surveys and by exposing firm executives to treatment scenarios. Aggregation then results in industry level and economy wide impulse responses. We further show that the effects obtained from survey experiments can be mapped into impulse responses retrieved from VARs. We apply the procedure to study the effects of oil price shocks using a representative sample of over 1000 Swiss firms. At the aggregate and industry level our findings confirm, with some notable exceptions, results from a standard VAR. At the micro level we analyze the driving forces behind firm specific impulse responses, controlling for several firm characteristics via panel data analysis and thereby solving existing puzzles.JEL classifications: C32, C83, C99, E31

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The Problem of Optimum Stratification

Cited by 132 publications

References 1 publication

Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey

Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey

Optimum stratification in bivariate auxiliary variables under Neyman allocation

Macro and Micro Level Impulse Responses: A Survey Experimental Identification Procedure

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