Comparison of non-survey techniques for constructing regional input–output tables

Performance of Bidimensional Location Quotients for Constructing Input-Output Tables

“…STPE is used to calculate the relative distance in absolute terms between the estimated coefficient and the true coefficient. Multiplying it by one hundred yields error as a percentage (Jalili 2000;Jackson and Murray 2004;Bonfiglio 2005;Lampiris et al 2019). MAD calculates the difference (in absolute value) between estimated and true coefficients, yielding the absolute mean of the differences when divided by the total number of elements in the matrix (Morrison unlike other works, which focus solely on sums by rows or columns through a matrix of coefficients or the Leontief inverse matrix.…”

“…Multiple research papers delve into the search for the optimal value of this parameter (Kowalewksi 2015;Flegg and Tohmo 2016;Lamonica and Chelli 2018). This unknown value becomes problematic and its calculation is thus arduous (Lampiris et al 2019), probably because it is quite sensitive due to the design of the corresponding formulas. Recently and in a context of identical available information, Pereira-López et al (2020) carried out a two-dimensional reformulation of LQs (for domestic flow tables, though extrapolated to total flows with certain nuances), thus employing two parameters.…”

“…2015) applied an extension of the Flegg methodology and revealed values between 0.11 and 0.17, which are relatively low compared to previous studies Lampiris et al (2019). compared technical coefficient matrices and estimated Leontief inverse matrices using traditional LQs for several EU countries.…”

“…and Smith 1974;Jackson and Murray 2004;Bonfiglio 2005;Bonfiglio and Chelli 2008;Miller and Blair 2009;Kowalewksi 2015;Wiebe and Lenzen 2016;Lamonica and Chelli 2018;Lampiris et al 2019;Lamonica et al 2020).MAPE is practically the average of STPE(Oosterhaven et al 2003;Mínguez et al 2009;Miller and Blair 2009;Lampiris et al 2019;Flegg and Tohmo 2019;Jahn et al 2020). SD-MAD is the standard deviation to the median absolute deviation between the estimated and true coefficients(Lamonica and Chelli 2018).…”

“…SD-MAD is the standard deviation to the median absolute deviation between the estimated and true coefficients(Lamonica and Chelli 2018). Theil index is known as the inequality index, as it estimates the overall distance ratio, and thus indicates perfect equality when equal to zero(Jalili 2000;Lahr and Stevens 2002;Jackson and Murray 2004;Bonfiglio 2005;Flegg and Tohmo 2013;Kowalewksi 2015;Flegg and Tohmo 2019;Lampiris et al 2019;Jahn et al 2020). This study compares matrices element by element,…”