Abstract

The capacity of input-output tables to reflect the structural peculiarities of an economy and to forecast, on this basis, its evolution, depends essentially on the characteristics of the matrix A—matrix of I-O (or technical) coefficients. However, the temporal behaviour of these coefficients is yet an open question. In most applications, the stability of matrix A is usually admitted. This is a reasonable assumption only for a short-medium term. In the case of longer intervals, the question is much more complicated.We shall empirically discuss this problem by using Romanian input-output tables. Our statistical option was motivated inter alia by the existence of official annual data for two decades (1989–2009).As an introduction, Sect. 1 characterises the general framework of paper. Section 2—The main characteristics of I-O coefficients as statistical time series—examines the variability of technical coefficients expressed in both volume and value terms. The analysis is convergent to other previous works, confirming that the evolution of these coefficients in real and nominal terms is roughly similar. The main finding of this section is that, on one hand, the I-O coefficients are volatile, but on the other, they are serially correlated.Consequently, Sect. 3—Attractor hypothesis—examines a possible presence of attractors in corresponding statistical series. The paper describes a methodology to approximate these using new indicators obtained by summation—in columns and rows—of the technical coefficients (colsums sca j and rowsums sra i ). The RAS method is involved as a connecting technique between these indicators and sectoral data.Section 4—Conclusions—presents the main conclusions of the research and outlines several possible future developments. The database and econometric analysis are presented in Statistical and Econometric Appendix.JEL Classification: C12, C32, C43, C67.

Highlights

  • The capacity of input-output tables to reflect the structural peculiarities of the economy and to forecast, on this basis, its evolution, depends essentially on the characteristics of matrix A of I-O coefficients

  • What needs to be documented is the nature of this volatility, and the highly questionable factor is the presence of non-linearities in the respective statistical series

  • J aa1j aa2j aa3j aa4j aa5j aa6j aa7j aa8j aa9j aa10j. This affects both determinations of I-O coefficients, either in volume or in value terms; the first is referred to as real volatility and the second as nominal volatility

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Summary

Introduction

We investigate whether the I-O coefficients series could contain sets of attractor points To answer this question, a methodology for their numerical estimation will be applied to the available data. 3. The robustness of structural changes analysis and of the sectoral dynamic general equilibrium models depends mainly on the temporal behaviour of I-O coefficients. The robustness of structural changes analysis and of the sectoral dynamic general equilibrium models depends mainly on the temporal behaviour of I-O coefficients Where rePij = Pi/Pj. The I-O coefficients at constant prices were estimated using formula (3), which is equivalent to caij = aij/rePij. Econometric estimations involve several aggregative indicators resulted from the technical coefficients in value terms, namely:. These approximate the contribution of each sector to the intermediary consumption of the entire economy

The Main Characteristics of I-O Coefficients as Statistical Time Series
Attractor Hypothesis
The system SyS1scr has been solved using other four techniques
Findings
Conclusions

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