Designing a Nowcasting Model for GDP Growth: A Practical Approach

This study provides a comparative analysis of the economic growth paths of Ukraine and Poland from a growth‑model perspective and determines how to calibrate Ukraine’s growth model to converge with Poland’s booming economy. The methodology comprises an approach to operationalizing growth models for GDP growth decomposition into “import‑adjusted” demand components, drawing on national input‑output data from 2000 to 2019. I found that from 2000 to 2003, both European economies relied on a combination of exports and domestic consumption. Expanded trade integration and an FDI boost after Poland joined the EU in 2004 spurred the Polish growth model’s shift to a distinctively export‑led, FDI‑driven strategy with accelerated GDP growth rates. In Ukraine, in the wake of the great financial crisis, I identified a transition to a consumption‑led growth model that, along with a declining investment component of aggregate demand, led to fading growth rates. An analysis of sectoral contributions to GDP growth revealed that avoiding deindustrialization in Poland underpinned the country’s export‑led strategy, unlike Ukraine, which underwent a key sectoral shift from manufacturing to a commodities‑based orientation after 2008. Both these economies demonstrated a high level of integration into global value chains, focusing on labor‑intensive manufacturing and services, but Poland has outperformed Ukraine in terms of share of high value‑added exports, which increased after EU accession. Following the Polish pattern, I propose that Ukraine’s growth model should activate the FDI driver of economic growth, upgrading the export structure and moving up value chains to unlock the country’s growth opportunities. The study represents the first comparison of Ukraine’s and Poland’s economic growth paths that traces the changes in dominant final demand components and macro‑sectors in the two countries’ economic growth profiles. This paper contributes to the comparative political economy literature on the growth models of peripheral economies, providing insights that can inform policies for growth model transformation.

What is the growth model of emerging economies striving to develop rapidly to become advanced economies? South Korea is the only poor economy, sixty years ago, to have become an emerging economy and then an advanced economy. For advanced economies since 1961, a new growth and distribution model has been proposed to explain the increase in the profit share of income and, simultaneously, the significant decline in GDP growth and labor productivity. Macroeconomic developments in South Korea are analyzed in detail, with standardized data available since 1975. It appears that high GDP and productivity growth are also associated with a low profit share, less than one-third over the long period from 1975 to 2019, as was the case for advanced economies during the Golden Age of Capitalism. South Korea is also the only advanced economy since the 2008 financial crisis to maintain high GDP and productivity growth. The considered growth and distribution model provides important insights both for advanced economies and for an emerging economy becoming an advanced economy like South Korea. For rapid economic growth, the key factors appear to be a low profit share, well below one-third, income-led growth, reduced inequality, and high R&D spending.

In this paper, I develop a measure of human capital stock that is similar to measuring physical capital by its replacement cost. This measure builds on measures of average educational attainment of the labour force. While it is far from an ideal measure, it is an interesting complement to the educational attainment series and other existing measures of human capital accumulation. In cross–country panel regressions, use of this measure of human capital accumulation yields a positive and significant, but relatively small (about ten per cent) elasticity with percapita GDP growth. Unlike physical capital, the stock of human capital as a share of GDP increases with GDP. This is consistent with the Barro et al. (1995) model of growth with non–mobile human capital and with some predictions of Romer’s (1990) model of endogenous growth, but it is not consistent with the predictions of some other growth models.

Given the present level of institutional quality and the significant role of the government sector in the economy, the Russian Federation has depleted the potential of the current model of growth which is based on commodity exports. The dramatic deceleration of the GDP growth rate down to less than 2% in the end of 2012 and the beginning of 2013 bears the evidence to this proposition. At the moment, the government considers the choice between expansionist and conservative scenarios, which both lie on the assumption of long-term conservation of existing imperfect institutions. However, according to our estimates, it is impossible to create a new model of growth ignoring the role of private initiative, healthy institutions of market economy and investment in human capital. We distinguish two groups that are increasing their influence nowadays and can potentially become the driving force of a new model of Russian economic growth: “new business”, dynamic companies that are oriented at the development in the market conditions but lack incentive to invest within existing institutional framework; “new bureaucracy”, consisting of progressive regional elites, who are interested in the development of their area, and efficient professionals of the federal level.

The objective of this study was to investigate whether the two most common growth mechanics modeling frameworks, the constrained-mixture growth model and the kinematic growth model, could be reconciled mathematically. The purpose of this effort was to provide practical guidelines for potential users of these modeling frameworks. Results showed that the kinematic growth model is mathematically consistent with a special form of the constrained-mixture growth model, where only one generation of a growing solid exists at any given time, overturning its entire solid mass at each instant of growth in order to adopt the reference configuration dictated by the growth deformation. The thermodynamics of the kinematic growth model, along with the specialized constrained-mixture growth model, requires a cellular supply of chemical energy to allow deposition of solid mass under a stressed state. A back-of-the-envelope calculation shows that the amount of chemical energy required to sustain biological growth under these models is negligibly small, when compared to the amount of energy normally consumed daily by the human body. In conclusion, this study successfully reconciled the two most popular growth theories for biological growth and explained the special circumstances under which the constrained-mixture growth model reduces to the kinematic growth model.

We develop a forecasting model of GDP growth that features regime-switching behavior and an error-correction mechanism (ECM). Regime changes are manifested in the behavior of a stochastic regime drift component that moves between expansionary and contractionary phases, thus generating cycles in GDP growth. Regime changes are triggered stochastically by an observable indicator variable we refer to as a tension index. This is constructed as the geometric sum of deviations of actual GDP growth from a corresponding sustainable (interpretable as the growth rate of potential GDP). At the onset of an expansionary regime, the regime drift component begins to move along an increasing trajectory. As GDP growth follows along this trajectory, the tension index tends to increase; as it does so, the probability that a regime change will be triggered also increases. We model this probability using a logistic specification that is increasing in the absolute value of the index. In the event of a regime change, the process becomes reversed, and the regime drift begins to decline along a newly established path. Linking the behavior of the tension index to GDP growth thus enables us to capture floor and ceiling effects. In the context of the model, the longevity of the economic expansion of the 1990s is attributable to the moderate nature of output growth observed over this period, which generated correspondingly moderate levels of the tension index, and low probabilities of a reversal.

Relevance. The development of the Eurasian Union means economic integration between its member countries (Armenia, Belarus, Kazakhstan, Kyrgyzstan, and Russia). For more efficient coordination of this process, it is necessary to analyze national models of economic growth and adjust the Union’s economic policy accordingly. Research objective. The study aims to identify the national models of economic growth by applying the structural analysis method and building regression models of GDP growth. Data and methods. The study relies on the structural analysis of GDP growth and regression analysis of the impact of macroeconomic policy instruments. Results. The study provides an overview of the research literature on the factors affecting economic growth (e.g. the financial structure, government expenditures. Based on the results of the structural analysis, a classification of the models of economic growth in terms of expenditures and sectors is proposed. This classification can be used to devise measures stimulating cooperation and integration within the Eurasian Union. Conclusions. The study has revealed the differences between the national models of economic growth by looking at each country’s reaction to the crises of 2009 and 2015. These differences correspond to the peculiarities of each country’s economic policy and need to be taken into account in the Union’s policy-making.

The purpose of this study is to analyze various macroeconomic factors that affect Gross Domestic Product growth in ASEAN-4 countries. The type of research is descriptive and explanatory. The research method used is the quantitative method. Based on the phenomenon, purposive sampling is used to determine the countries studied. Using secondary data, the panel data is formed from a combination of countries and a certain period of years. Multivariable regression is used to process panel data. The results show that inflation rate, political stability, and control of corruption have a significant effect on GDP growth. The novelty of this research is the new model of GDP growth in ASEAN-4 countries where control of corruption serves as an intervening variable that affects GDP growth. The findings suggest that to maintain an increase in GDP growth in this region, the governments should keep the inflation rate under control and continue striving to reduce corruption. Controlling the level of corruption can be done by maintaining the stability of the political situation and controlling the inflation rate.

De nouvelles statistiques americaines provenant du NIPA contredisent certains des fameux faits stylises de Kaldor et invitent a une reformulation de la theorie moderne de la croissance economique. Parmi ces faits nouveaux, deux sont a mettre en avant plus particulierement : une baisse continue du prix relatif des biens durables et une hausse continue du ratio equipement reel sur PIB reel. Pour etre en accord avec ces nouveaux faits stylises, les modeles de croissance doivent inclure au moins deux secteurs et s’attaquer au probleme de la definition du produit agrege. Dans cet article, nous utilisons la theorie economique des nombres-indices pour definir le taux de croissance du produit reel dans un modele de croissance avec changement technique incorpore. Les principaux resultats sont les suivants : (i) la methode du NIPA mesure la croissance conformement a la theorie economique des nombres-indices, et (ii) lorsque le taux de croissance est mesure comme le fait le NIPA, nous trouvons que la contribution du changement technique incorpore a la croissance du PIB par tete aux Etats-Unis est de 69%. Ceci confirme l’hypothese selon laquelle le changement technique incorpore est important pour la croissance.

The economic impact of Articial Intelligence (AI) is studied using a (semi) endogenous growth model with two novel features. First, the task approach from labor economics is reformulated and integrated into a growth model. Second, the standard representative household assumption is rejected, so that aggregate demand restrictions can be introduced. With these novel features it is shown that (i) AI automation can decrease the share of labor income no matter the size of the elasticity of substitution between AI and labor, and (ii) when this elasticity is high, AI will unambiguously reduce aggregate demand and slow down GDP growth, even in the face of the positive technology shock that AI entails. If the elasticity of substitution is low, then GDP, productivity and wage growth may however still slow down, because the economy will then fail to benefit from the supply-side driven capacity expansion potential that AI can deliver. The model can thus explain why advanced countries tend to experience, despite much AI hype, the simultaneous existence of rather high employment with stagnating wages, productivity, and GDP.

The fundamental aim of this study is to fetch out the influence of NODA (net official development assistance) and EDS (external debt stock) on GDP growth in South Asian and Southeast Asian designated economies over 1971 to 2019. This study refers to the Solow-Swan model of economic growth as hypothetical framework in its elementary sort and employ factors such as NODA, EDS, savings, capital, depreciation, governance, and in addition total natural resources rent as control variable. Using panel data sourced from WDI (world development indicators). This study employed various econometric techniques comprise FEM (fixed effect model), panel cointegration, panel dynamic least square (DOLS), and Granger causality test for desired regression estimations. Empirical estimations evident that EDS has a negative and significant impact on GDP growth whereas, NODA had a negative and insignificant impact on GDP growth. A positive and significant influence of savings on GDP growth observed and estimation confirm that by increasing 1% in savings may cause an increase in GDP growth by 13.19 units. Capital has a positive and significant impact on GDP growth and estimation outcomes confirm that an increase of 1% in capital may cause an increase of 10.14 units. A negative and significant impact of depreciation on GDP growth revealed and it is intended that increasing 1% in depreciation may cause a decrease in GDP growth by 5.26 units. A positive and significant impact of TNRR on GDP growth revealed and estimations confirm that an increase of 1% in total natural resource rent may cause an increase in GDP growth by 0.099 units. While, impact of governance on GDP growth revealed insignificant. Lastly, it is also endorsed that a uni-directional Granger causality runs from GDP growth to EDS, EDS to NODA, and no causal relationship has been confirmed between NODA and GDP growth in SA and SEA designated economies over 1971 to 2019.

The fundamental aim of this study is to fetch out the influence of NODA (net official development assistance) and EDS (external debt stock) on GDP growth in South Asian and Southeast Asian designated economies over 1971 to 2019. This study refers to the Solow-Swan model of economic growth as hypothetical framework in its elementary sort and employ factors such as NODA, EDS, savings, capital, depreciation, governance, and in addition total natural resources rent as control variable. Using panel data sourced from WDI (world development indicators). This study employed various econometric techniques comprise FEM (fixed effect model), panel cointegration, panel dynamic least square (DOLS), and Granger causality test for desired regression estimations. Empirical estimations evident that EDS has a negative and significant impact on GDP growth whereas, NODA had a negative and insignificant impact on GDP growth. A positive and significant influence of savings on GDP growth observed and estimation confirm that by increasing 1% in savings may cause an increase in GDP growth by 13.19 units. Capital has a positive and significant impact on GDP growth and estimation outcomes confirm that an increase of 1% in capital may cause an increase of 10.14 units. A negative and significant impact of depreciation on GDP growth revealed and it is intended that increasing 1% in depreciation may cause a decrease in GDP growth by 5.26 units. A positive and significant impact of TNRR on GDP growth revealed and estimations confirm that an increase of 1% in total natural resource rent may cause an increase in GDP growth by 0.099 units. While, impact of governance on GDP growth revealed insignificant. Lastly, it is also endorsed that a uni-directional Granger causality runs from GDP growth to EDS, EDS to NODA, and no causal relationship has been confirmed between NODA and GDP growth in SA and SEA designated economies over 1971 to 2019.

This chapter summarizes and elaborates on the “growth models perspective” proposed by Baccaro and Pontusson (2016). In addition, the chapter updates the previous analysis of post-Fordist growth trajectories in Germany, Italy, Sweden and the UK. With growth models operationalized in terms of the contributions of different components of aggregate demands to GDP growth, the analysis shows that the growth models that these countries adopted in the period prior to the crisis of 2008–10 have by and large persisted into the 2010s. Exports remain the driver of German economy while household consumption remains the key to British economic growth and Italy continues to be mired by economic stagnation. Sweden is the one case for which one observes a shift in growth dynamics in the wake of the crisis, with domestic consumption playing a more important role relative to net exports.

In this paper we use pooled cross-sectional (longitudinal data) in a sample of 10 Balkan countries. The period we cover is from 1950-2009 data are for population and economic growth. In the theoretical part we present optimal intergenerational model of population growth .The optimal population growth depends on capital in the future period and future consumption. Consumption should be greater than zero, and less than total capital of the cur-rent generation. In the econometric part OLS regression with dummies the coefficient on Ma-cedonia, is highest significant coefficient meaning, if we control for Macedonia we will on average find more positive association between growth of GDP and population growth. Hausman test was in favor of fixed effects model, but fixed effects and Random effects model showed that there is positive coefficient between GDP growth and population growth. Coefficient in the FE model was statistically significant, which was not case in RE model. From the Fischer’s panel unit root test we reject the null hypothesis that panels contain unit root and we accept the alternative that at least one panel is stationary, for the population growth and GDP growth.

Assessment of the causal links between energy, technologies, and economic growth in China: An application of wavelet coherence and hybrid quantile causality approaches