The Inforum Model of the Polish Economy
A demand-oriented model for Poland is developed. A number of special features are incorporated to reflect the realities of the Polish economy and to take advantage of some of the statistics which are available in Poland but not in Western Europe. These special features include disequilibrium indicators in the consumer demand equations, direct measurement of capital stocks and technological progress, allowance for hoarding of obsolete equipment, strong connections between input coeffcients and new investment, and the treatment of liquidity overhang. Results of simulations showed correctly in advance the effects of the 1990 austerity policy. Experiments indicate the best way to use foreign investment funds.
Increase of electricity price and energy efficiency: analysis using the macroeconomic interindustry model of Taiwan
ABSTRACT From 2012 to 2013, the price of electricity in Taiwan increased by 19.78%. This large increase forced producers to invest in improving electricity efficiency. In this paper, the macroeconomic interindustry model of Taiwan (MEIT) is developed to study the economic effects of high electricity prices and consequential improvement in energy efficiency. MEIT describes the structure of 47 industries, from both real and price-income approaches. To resolve inconsistent data, RAS is employed. A technical model is also integrated to consider technical feasibility, which offsets the usual shortcomings of technological analysis in an economic model. The iron and steel industry is taken as a case study. Estimated results indicate that higher electricity prices negatively affect Taiwan’s economy. However, improving energy efficiency can reduce some of this effect.
Complexity, Network-Decisions And Alternative Risk Premia: Some New Theories Of Structural Change And Portfolio Decisions.
Structural Change is the basis for many decisions such as government fiscal and monetary policies, government interventions, corporate-expenditure decisions, corporate strategic decisions, portfolio management decisions in finance; household allocation decisions, etc.. Structural change in industries and national economies is increasingly more difficult to detect partly because of the growth and dominance of Complex Systems and “Networks” in modern industries and economies – such Networks and Complex Systems include GVCs (global value chains), retail store networks; franchising networks; internet networks; services networks; credit-chains in industries; online social networks; etc.. The relationship between Complexity and Structural Change is well established in the physics, applied math and economic dynamics literatures. Traditional definitions and modeling of structural change have erroneously focused on changes in labor, technological progress, market share, and or capital – which are grossly inadequate in the new economy and post- global financial crisis (as confirmed by changes in the global retailing and housing industries and other industries). Also the modeling of structural change seems to be constrained by the availability of data, and often omits the relationship between structural change on one hand, and globalization, financial stability and international financial contagion. This article: i) introduces the Operations Strategy Model of structural change, explains how it differs from the Lewis Model of Structural Change; and explains the often omitted relationship between operations strategy (of companies and government agencies) and structural change; ii) introduces other new Complexity-based models/theories of Structural Change in industries; iii) reviews the relationships among Operations Research, Complexity and Structural Change; iv) explains the symbiotic relationship between structural change on one hand, and economic growth, financial stability, financial contagion and globalization; v) discusses the significant gap between academic research and practice and explains some issues and factors that should be considered in the development and testing of structural change models; vi) explains how alternative risk premia can be generated from new models of Structural Change.