Incorporating Fuzzy Logic in Harrod’s Economic Growth Model

This paper suggests the possibility of incorporating the methodology of fuzzy logic theory into Harrod’s economic growth model, a classic model of economic dynamics for studying the growth of a developing economy based on the assumption that an economy with only savings and investment income is in equilibrium when savings are equal to investment. This model was the first precursor to exogenous growth models, which in turn gave rise to endogenous growth models. This article therefore represents a first step towards introducing fuzzy logic into economic growth models. The study concerned considers consumption and savings to depend on income by means of uncertain factors, and investment to depend on the variation of income through the accelerator factor, which we consider uncertain. These conditions are used to determine the equilibrium growth rate of income and investment, as well as the uncertain values for these variables in terms of fuzzy numbers. As a result, the new model is shown to expand the classical model by incorporating uncertainty into its variables.

ESTIMATION OF PARAMETERS OF ECONOMIC GROWTH AUTOREGRESSIVE MODELS

The construction of time series models based on statistical data is one of the central tasks of modern econometric studies. Numerous economic models based on differential equations are used to explain the patterns of economic growth. The article is devoted to the estimation of parameters of economic growth models based on solutions of homogeneous differential equations with constant coefficients. A comparative analysis of methods for estimating the parameters of autoregression of economic dynamics series with additive interference in the output signal is carried out. The simulation results showed that the full least squares method gives the most accurate estimates. The most commonly used least squares method gives the worst estimates.

COVID 19 – Is this a Result of Population Growth – Was a Malthusian Catastrophe Inevitable?

This article examines theories of population growth and puts forward a new economic growth model that would reduce the instance of natural disasters and pandemics. Keywords: Covid19, population growth, economic growth models

Cross-National Variation in Growth Models

The chapter presents a new framework for categorizing economic growth models and applies it to twenty-eight OECD countries from 1995 to 2016. The framework draws on three fundamental ways in which economies can benefit from additional demand: the private sector (households and companies) can spend more than its income, the public sector can spend more than its revenues, or the economy sells more abroad than it imports. The empirical section uses fuzzy-set ideal type analysis to identify the combinations in which advanced economies used these three ways of boosting demand in three subperiods between 1995 and 2016. The results show that most economies use at least one of the three sources of extra demand to tackle the era of low growth. At the same time, there are clear differences in growth models between groups of countries. These are in line with clusters that the literature commonly identifies due to their institutional similarities. The growth models in this chapter are therefore outcomes of differences in growth regimes.

COVID-19, the Climate, and Transformative Change: Comparing the Social Anatomies of Crises and Their Regulatory Responses

Despite forces struggling to reduce global warming growing stronger, there has been mixed success in generating substantive policy implementation, while the global spread of the coronavirus has prompted strong and far-reaching governmental responses around the world. This paper addresses the complex and partly contradictory responses to these two crises, investigating their social anatomies. Using temporality, spatiality, and epistemic authority as the main conceptual vehicles, the two crises are systematically compared. Despite sharing a number of similarities, the most striking difference between the two crises is the urgency of action to counter the rapid spread of the pandemic as compared to the slow and meager action to mitigate longstanding, well-documented, and accelerating climate change. Although the tide now seems to have turned towards a quick and massive effort to restore the status quo—including attempts to restart the existing economic growth models, which imply an obvious risk for substantially increasing CO2 emissions—the article finally points at some signs of an opening window of opportunity for green growth and degrowth initiatives. However, these signs have to be realistically interpreted in relation to the broader context of power relations in terms of governance configurations and regulatory strategies worldwide at different levels of society.

The Application of Fractional Calculus in Chinese Economic Growth Models

In this paper, we apply Caputo-type fractional order calculus to simulate China’s gross domestic product (GDP) growth based on R software, which is a free software environment for statistical computing and graphics. Moreover, we compare the results for the fractional model with the integer order model. In addition, we show the importance of variables according to the BIC criterion. The study shows that Caputo fractional order calculus can produce a better model and perform more accurately in predicting the GDP values from 2012–2016.