Projection of Supply and Use Tables: Methods and their Empirical Assessment

Temursho, Umed; Yamano, Norihiko; Webb, Colin

doi:10.2139/ssrn.1539089

Cited by 10 publications

(14 citation statements)

References 30 publications

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“…(Temurshoev et al, 2011)). The WAPE indicator is defined as a weighted average of the variation rates of the (i,j)-th element with respect to the same (i,j)-th element of the other matrix, which is usually considered as the true/official value 1 .…”

Section: Methods For Comparing the Main Components Of The Databasementioning

confidence: 99%

Comparing the Gtap-Mrio and Wiod Databases for Carbon Footprint Analysis

Arto

Rueda‐Cantuche

Peters

2014

Economic Systems Research

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In this paper we explore the use of two different global multiregional input-output databases (GTAP-MRIO and WIOD) for the calculation of the global carbon emissions embodied in the final demand of nations (carbon footprint). We start our analysis with a description of the main characteristics of the databases and comparing their main components. Then, we calculate the carbon footprint with both databases and identify (from a global perspective) the most relevant factors underlying the resulting differences using structural decomposition analysis. The main conclusion that can be drawn is that, on average, certain elements of both databases can be said to be similar in around 75% to 80%, being only a few elements in each table the main drivers of the major differences. The divergences in the datasets of four countries explain almost 50% of the differences in the carbon footprint (USA 19.7%, China 18.1%, Russia 6.4% and India 4.3%). Industry wise, 50% of the differences can be explained by the divergences in three industries: (electricity 32.7%, refining 9.9% and inland transport 7.1%).

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Section: Methods For Comparing the Main Components Of The Databasementioning

confidence: 99%

Comparing the Gtap-Mrio and Wiod Databases for Carbon Footprint Analysis

Arto

Rueda‐Cantuche

Peters

2014

Economic Systems Research

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“…It should be mentioned that the Euro method also requires that the Use table at basic prices is distinguished between domestic and imported intermediate and final uses. We do not explain the Euro and EUKLEMS methods in this paper due to space constraints, and refer the reader to Temurshoev et al (2010) who present detailed description of both methods for updating SUTs.…”

Section: Empirical Assessmentmentioning

confidence: 99%

“…In Temurshoev et al (2010) eight different methods were studied and evaluated in the estimation of the Dutch and Spanish Use and Supply tables and it was concluded that the well-known (G)RAS algorithm was generally superior.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of Supply and Use Tables

Temursho

Timmer²

2010

SSRN Journal

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We propose a new biproportional method specifically designed for joint projection of Supply and Use tables (SUTs). In contrast to standard inputoutput techniques, this method does not require the availability of total outputs by product for the projection year(s), a condition which is not often met in practice. The algorithm, called the SUT-RAS method, jointly estimates SUTs that are immediately consistent. It is applicable to different settings of SUTs, such as the frameworks with basic prices and purchasers' prices, and a setting in which Use tables are separated into domestic and imported uses. Our empirical evaluations show that the SUT-RAS method performs quite well compared to widely used short-cut methods.

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“…Data balancing occurs in IO analysis under different circumstances, of which the most thoroughly explored is the problem of table update when row and column sums,m R i andm C j , are known for the current year, and interior points, µ * ij , are known from a previous year [13]. In this problem, the goal is to update each interior point to m * ij , such that M * e =m R and M * e =m C .…”

Section: Proportional and Cross-entropy Methodsmentioning

confidence: 99%

“…For example, in the classical table update problem [5], there is an initial estimate from the previous year for interior points (low quality data) and row and column sums for the present year (high quality data). In practice the data update problem combines data from multiple sources and differing degrees of trustworthiness [11][12][13]. The present paper deals with the general problem of combining data with differing degrees of quality (e.g., data from national statistical offices, from international organizations, survey data, etc.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to the Balancing of Statistical Economic Data

Rodrigues

2014

Entropy

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Abstract:This paper addresses the problem of balancing statistical economic data, when data structure is arbitrary and both uncertainty estimates and a ranking of data quality are available. Using a Bayesian approach, the prior configuration is described as a multivariate random vector and the balanced posterior is obtained by application of relative entropy minimization. The paper shows that conventional data balancing methods, such as generalized least squares, weighted least squares and biproportional methods are particular cases of the general method described here. As a consequence, it is possible to determine the underlying assumptions and range of application of each traditional method. In particular, the popular biproportional method is found to assume that all source data has the same relative uncertainty. Finally, this paper proposes a simple linear iterative method that generalizes the biproportional method to the data balancing problem with arbitrary data structure, uncertainty estimates and multiple data quality levels.

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Projection of Supply and Use Tables: Methods and their Empirical Assessment

Cited by 10 publications

References 30 publications

Comparing the Gtap-Mrio and Wiod Databases for Carbon Footprint Analysis

Comparing the Gtap-Mrio and Wiod Databases for Carbon Footprint Analysis

Joint Estimation of Supply and Use Tables

A Bayesian Approach to the Balancing of Statistical Economic Data

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