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This paper seeks to explore the sensitivity of these parameters and their impact on fiscal policy outcomes. We use the existing literature to establish possible ranges for each parameter, and we examine how changes within these ranges can alter the outcomes of fiscal policy. In this way, we aim to highlight the importance of these parameters in the formulation and evaluation of fiscal policy.

The role of fiscal policy, its effects and multipliers continues to be a subject of intense debate in macroeconomics. Despite adopting a New Keynesian approach within a macroeconomic model, the reactions of macroeconomic variables to fiscal shocks can vary across different contexts and theoretical frameworks. This paper aims to investigate these diverse reactions by conducting a sensitivity analysis of parameters. Specifically, the study examines how key variables respond to fiscal shocks under different parameter settings. By analyzing the behavioral dynamics of these variables, this research contributes to the ongoing discussion on fiscal policy. The findings offer valuable insights to enrich the understanding of the complex relationship between fiscal shocks and macroeconomic outcomes, thus facilitating informed policy debates.

This paper aims to investigate key elements of New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models. The focus is on the calibration of parameters and their impact on macroeconomic variables, such as output and inflation. The study also examines how different parameter settings affect the response of monetary policy to fiscal measures. In conclusion, this study has relied on theoretical exploration and a comprehensive review of existing literature. The parameters and their relationships have been analyzed within a robust theoretical framework, offering valuable insights for further research on how these factors influence model forecasts and inform policy recommendations derived from New Keynesian DSGE models. Moving forward, it is recommended that future work includes empirical analyses to test the reliability and effectiveness of parameter calibrations in real-world conditions. This will contribute to enhancing the accuracy and relevance of DSGE models for economic policy decision-making.

This study is motivated by the aim to provide a deeper understanding of the roles macroeconomic model parameters play concerning responses to expansionary fiscal policies and the subsequent reactions of monetary authorities. Comprehensive reviews that encompass this breadth of relationships within a single text are rare in the literature, making this work a valuable contribution to stimulating discussions on macroeconomic policies.

This study aims to investigate the impact of brain drain on government AI readiness in EU member countries, considering the distinctive governance characteristics, macroeconomic conditions and varying levels of ICT specialists.

The research employs a dynamic panel data model using the System Generalized Method of Moments (GMM) to analyze the relationship between brain drain and government AI readiness from 2018 to 2022. The study incorporates various control variables such as GDP per capita growth, government expenditure growth, employed ICT specialists and several governance indicators.

The results indicate that brain drain negatively affects government AI readiness. Additionally, the presence of ICT specialists, robust governance structures and positive macroeconomic indicators such as GDP per capita growth and government expenditure growth positively influence AI readiness.

Major limitations include the focus on a specific region of countries and the relatively short period analyzed. Future research could extend the analysis with more comprehensive datasets and consider additional variables that might influence AI readiness, such as the integration of AI with emerging quantum computing technologies and the impact of governance reforms and international collaborations on AI readiness.

The theoretical value of this study lies in providing a nuanced understanding of how brain drain impacts government AI readiness, emphasizing the critical roles of skilled human capital, effective governance and macroeconomic factors in enhancing AI capabilities, thereby filling a significant gap in the existing literature.

This research fills a significant gap in the existing literature by providing a comprehensive analysis of the interaction between brain drain and government AI readiness. It uses control variables such as ICT specialists, governance structures and macroeconomic factors within the context of the European Union. It offers novel insights for policymakers to enhance AI readiness through targeted interventions addressing brain drain and fostering a supportive environment for AI innovation.

This paper, based on the 2022 Master Class delivered at the 50th National Economic Meeting organized by ANPEC, discusses how post-Keynesian macroeconomics and New Developmentalism complement each other to understand middle-income economies' development in financial globalization. It summarizes my academic reflection about the advance in post-Keynesian thinking to develop macroeconomics for peripheral middle-income economies.

As part of this reflection, I first bring up the idea of a developmental convention and, next, how peripheral financialization impacts the elaboration of this convention. Given the asymmetric configuration of the international financial system and the context of hierarchical currencies, I discuss the challenge of overcoming underdevelopment in peripheral economies. The post-Keynesian macroeconomics and advances in the structuralist debate provide the analytical tools to understand how peripheral economies develop virtuous or vicious growth cycles. At the end of the paper, I present some comments on the stagnation of the Brazilian economy.

The growth strategy with foreign savings does not provide the conditions for middle-income economies to operate with sufficient economic policy autonomy to promote productive transformation. To this end, a developmental convention should replace the neoliberal convention that has dominated since the 1970s.

The dynamics of peripheral, middle-income economies, often influenced by international liquidity flows, are a crucial area of study. This research underscores the importance of understanding these dynamics, as it forms the basis for economic policy recommendations. The paper also highlights the inadequacy of the growth strategy with foreign savings in the current configuration of the international financial system, emphasizing the need for middle-income economies to operate with greater economic policy autonomy to foster productive transformation.

This study quantitatively examines the relationship between economic fluctuations and government budget size in the context of China’s fiscal decentralization, drawing inspiration from theoretical predictions of the Keynesian view and empirical studies on other economies.

The panel comprises 31 provinces or equivalents in mainland China, spanning from 1994 to 2019. Diverse estimation strategies including two-way fixed effect regression, the generalized method of moments (GMM) and threshold regressions are, utilized.

The results suggest that under the “tax-assignment system”, neither the central government’s fiscal transfers nor the provincial budgetary revenues or expenditures help reduce economic volatility. Surprisingly, some regression outcomes suggest that government size measures destabilize business cycles.

While the study does not provide supportive evidence for the stabilizing effect of public budgets in Chinese provinces, it promotes a rethinking of the government’s intricate role in macroeconomic stabilization in the context of China’s fiscal decentralization.

As the Philippines enters into the era of the “next normal,” that is, from the pandemic era called the “new normal,” we look into how the Philippines' state of tourism and built environment sectors. We also revisit its situation having the “longest lockdown in the world” and look into iterations of socioeconomic models, business platforms, and other elements that lead to the dream of a postpandemic sustainable tourism industry. In this inquiry, we highlight the importance of elements of the built environment, both at the macroeconomic level and at the micro units of business, civil society, and the tourism sector at large. While there has been a substantive discussion on the nexus of the tourism and built environment sectors, little has been devoted to the challenges these intertwined sectors faced during the COVID-19 pandemic. We suggest complementation of practices from the macroscale to the microlevel tourism and built environment sectors, and vice-versa, which will ensure the full complementation of the sectors. These proposals are in full contextualization of the objective toward the postpandemic continued recovery, growth, and sustainability, from the local, national, and the regional economy of Southeast Asia, and also to various parts of the world.

The measurement of regional competitiveness is becoming essential for policymakers to address territorial disparities, while considering the issue of correlations among indicators. Therefore, the purpose of this paper is to measure regional competitiveness using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) by considering different distance measures and two levels of analysis to provide a comparative and comprehensive measurement of regional competitiveness in Europe.

The authors apply TOPSIS based on three different distance measures (the Manhattan, the Euclidean and the Mahalanobis distance measures) to the regions of the EU Regional Competitiveness Index (RCI) 2019, which is taken as the frame of reference.

The authors replicate the RCI by using TOPSIS with a less preferred choice of distance measure, indicating TOPSIS as a valuable method for policymakers in the analysis of regional competitiveness. The authors argue in favour of the Mahalanobis distance measure as the best of the three, as it considers correlations among macro-economic indicators.

This study aims to make three contributions. Firstly, by replicating the RCI by means of TOPSIS with a less preferred choice of distance measure, the paper provides a benchmark for future research on regional competitiveness. Secondly, by suggesting the use of TOPSIS with the use of the Mahalanobis distance measure, the authors show how to measure regional competitiveness by taking into account correlations among pillars. Thirdly, the authors argue in favour of considering clusters of regions when measuring regional competitiveness.

Marketplace lending has substantially changed since the first peer-to-peer lending platforms emerged in 2006. The industry is now an alternative to bank lending, predicted to total $70 billion for consumer and business loans worldwide by 2030. Marketplace lending is often deemed less safe than bank loans, mainly due to these portfolios' high degree of hidden information. These include needing more information on borrowers and potential correlations between them, which might lead to higher risk than is apparent at first glance. Deterministic processes cannot capture tail risk appropriately, so platforms and lenders should employ stochastic processes. This chapter introduces a Monte Carlo simulation and machine learning (ML) process to evaluate and monitor portfolios. For marketplace lending to become a viable and sustainable alternative to bank lending platforms, they must better evaluate, monitor, and manage tail risk in marketplace loans and develop tools to monitor and manage financial risk losses.

The aim of this article is to assess the macroeconomic consequences of some specific aspects of financialization (i.e. share buy-back) using a hybrid post-Keynesian model of growth and distribution based on Kaldorian and Kaleckian characteristics.

The study follows a post-Keynesian approach and deals with financialization issues by implementing several numerical simulations.

The numerical simulations reveal the negative real impacts of massive share repurchases on the rate of accumulation because they immediately siphon off revenues directly intended for investment projects. Moreover, the negative effect of share buy-backs is reinforced especially when firms' investment decisions are more sensitive to a variation in retained earnings. Next, this macro-model also reproduces several well-known figures of the Kaleckian tradition and the paradox of costs.

The present article can be considered as a starting point for further theoretical extensions and requires empirical validation.

The Kaldor-Kalecki macro-model could be useful for policymakers who are interested in containing some of the negative excesses of financialization.

This study aims to investigate the dynamics between exogenous shocks, financial stress and economic performance in the USA from January 1995 to August 2023.

Granger-causality tests and impulse response analyses are used to examine causal relationships and dynamic responses among crude oil prices, real M2 money supply, financial stress and key economic indicators.

This study reveals a significant correlation between elevated financial stress and reduced real output, along with disruptions in the labor market, potentially leading to economic recessionary trends. Failure to address these challenges could perpetuate labor market difficulties, weaken capital accumulation within the loanable funds market and ultimately hinder long-term economic growth prospects in the USA.

This study offers insights for policymakers to mitigate financial stress. Recommendations include enhancing financial surveillance, strengthening regulatory frameworks, promoting economic diversification and implementing countercyclical policies to stabilize the economy and support labor markets. In addition, proactive monitoring of financial stress indicators can serve as early warning signals, aiding in timely interventions and effective risk management strategies.

This research provides a comprehensive analysis of how the financial stress index (FSI) mediates the effects of external shocks on the US economy, addressing a gap in existing literature. The integration of the FSI into the analysis enhances the understanding of the transmission channels through which external shocks influence the economy.

In this research, we explore the dynamics among measures of income inequality in the USA, male and female unemployment rates, and growth in government transfer using time series data.

This research adopts a macro-econometric approach to estimate a structural VAR model using time series data.

Our structural impulse responses found that growth in government transfer increases unemployment rates for both males and females. Female income inequality declines with increased government transfer. When the female income ratio rises, we observe that government transfer outlays fall over the forecast horizon. Variance decomposition finds that growth in government transfers is impacted by the male unemployment rate relatively more than the female unemployment rate. This research, therefore, suggests gender-specific government transfers to reduce income inequality. This, in effect, may reduce government transfer outlays over time.

This research, therefore, suggests gender-specific government transfers to reduce income inequality. This, in effect, may reduce government transfer outlays over time.

This research investigates the dynamics among income inequality, government transfer, and unemployment rates. There is a dearth of research articles that adopt a macro-econometric in this area.