Narrow Results

This study compares the sensitivity of each household nonperforming loans (NPLs) category in Malaysia, allowing asymmetry across different household credit types, credit cards, personal uses, purchase of residential properties and purchase of transport vehicles. Differences in the impact of household debt on the Malaysian household NPLs are found evident. In the sample period from 2006 to 2018, the findings suggest an asymmetric impact of credit card debts on the household NPLs. This linkage is explained by only short-run negative changes in credit card outstanding loans. Besides that, the residential property loans behave asymmetrically in the long and short run, with higher impact sources from the negative changes. In a disaggregated NPL analysis focusing on a different type of loan portfolio, the asymmetry further enhances policy and regulation-making in managing household credit risk.

Aiming at the problem that the application of Artificial Neural Network (ANN) only stays in the position of biology and single medical treatment, the creation of commercial value is limited, and the general commercial neural network technology is not enough, combined with information set setting construction model, RNN classification model, FLS method, GRU neural network operation method, LP economic trend method, Copula function model, Sol time sense data model, PIO factor model and other methods to optimize the economic benefits generated by ANN. The prediction model in the macroeconomic variable set is constructed by using the information set setting, and the commercial products preferred by the crowd are extracted. At the same time, the analysis and utilization of macroeconomic trends are proposed for the basis of economic and commercial decision-making, so as to better solve the problems of unit economic data status and gating structure, and further rationally arrange neural network resources. The use of multiple models for macroeconomic uncertainty and artificial neural network to determine the relationship between systematic commercial goods provides help, but also make the macro-financial risk indicators of the value of the range of change more obvious. The results of the improvement of the experimental method show that the use of multi-model algorithm makes the positive benefit value of macro-economy more effective, and the ability of artificial neural network technology to bring benefits is stronger.

This article aims to show that the literature so far has not been able to present a statistically robust answer to the question of what drove Singapore's spectacular savings rates. A substantial part of the literature — particularly earlier studies — must be rejected on methodological grounds since the time-series properties of the respective data series were not taken into consideration when choosing the appropriate testing method. Others have omitted potentially crucial determinants of savings or have wrongly disaggregated Gross National Savings. This unsatisfactory state of investigation into Singapore's saving behavior is unfortunate because savings play such a central role in Singapore's economic history since the country's independence. For future research the article also supplies new data series, which disaggregate Singapore's national savings after taking the country's peculiarities into consideration. The critical assessment is also intended as a guideline to future researchers of which mistakes to avoid and where potential pitfalls lie.

The global financial crisis of 2008 challenges some relevant aspects of macroeconomic theory such as the neutrality of money. This paper shows that this neutrality is based on the unrealistic assumption of perfect competition. Relaxing this alone (without time lags, price rigidities, menu costs and other frictions) makes money no longer necessarily neutral and hence makes financial crises and institutions much more important. The presence of increasing returns to scale at the firm level and to specialization at the economy level due to the division of labor also makes finance much more important than suggested by traditional economics.

In order to effectively improve the accuracy of Consumer Price Index (CPI) prediction so as to more truly reflect the overall level of the country’s macroeconomic situation, a CPI big data prediction method based on wavelet twin support vector machine (SVM) is proposed. First, the historical CPI data are decomposed into high-frequency part and low-frequency part by wavelet transform. Then a more advanced twin SVM is used to build a prediction model to obtain two kinds of prediction results. Finally, the wavelet reconstruction method is used to fuse the two kinds of prediction results to obtain the final CPI prediction results. The wavelet twin SVM model is used to fit and predict CPI index. Experimental results show that compared with the similar prediction methods, the proposed prediction method has higher fitting accuracy and smaller root mean square error.

A macroeconomic model based on the economic variables (i) assets, (ii) leverage (defined as debt over asset) and (iii) trust (defined as the maximum sustainable leverage) is proposed to investigate the role of credit in the dynamics of economic growth, and how credit may be associated with both economic performance and confidence. Our first notable finding is the mechanism of reward/penalty associated with patience, as quantified by the return on assets. In regular economies where the EBITA/Assets ratio is larger than the cost of debt, starting with a trust higher than leverage results in the highest long-term return on assets (which can be seen as a proxy for economic growth). Therefore, patient economies that first build trust and then increase leverage are positively rewarded. Our second main finding concerns a recommendation for the reaction of a central bank to an external shock that affects negatively the economic growth. We find that late policy intervention in the model economy results in the highest long-term return on assets. However, this comes at the cost of suffering longer from the crisis until the intervention occurs. The phenomenon that late intervention is most effective to attain a high long-term return on assets can be ascribed to the fact that postponing intervention allows trust to increase first, and it is most effective to intervene when trust is high. These results are derived from two fundamental assumptions underlying our model: (a) trust tends to increase when it is above leverage; (b) economic agents learn optimally to adjust debt for a given level of trust and amount of assets. Using a Markov Switching Model for the EBITA/Assets ratio, we have successfully calibrated our model to the empirical data of the return on equity of the EURO STOXX 50 for the time period 2000–2013. We find that dynamics of leverage and trust can be highly nonmonotonous with curved trajectories, as a result of the nonlinear coupling between the variables. This has an important implication for policy makers, suggesting that simple linear forecasting can be deceiving in some regimes and may lead to inappropriate policy decisions.

Recent studies have established that macroeconomic time series exhibit fractal properties. Empirical tests here demonstrate that interest rates, exchange rates, output and prices all show evidence of a non-integer fractal dimension. Several classes of volatility models widely used in econometrics can give rise to fractality. In the paradigm proposed here, fractality results from multiplicative relationships between residual noise terms in simultaneous equation systems. The emergence of fractality in a large-scale econometric model is analyzed. The model uses well-established structural equations, so that all variables converge toward their equilibrium paths in the long run. The forecasted paths are then embedded in noise, and the model is re-simulated at a higher frequency. The simultaneity of the model equations causes the embedding noise to take on fractal properties. Multi-scaling demonstrates that the model simulations reproduce the fractal properties of the real-world time series reasonably well. Finally, it is possible to forecast at short horizons using an algorithm that exploits two aspects of fractality, scaling symmetries and intermittency. Ratios of rates of change capture proximate symmetries. A logit regression is used to predict the conditional probability of extreme events.

This study investigates the usefulness of auditors' opinions, market factors, macroeconomic factors, and industry factors in predicting financial distress of Taiwanese firms. Specifically, two non-traditional auditors' opinions are evaluated: "long-term investment audited by other auditors" ("other auditor"), and "realized investment income based on non-audited financial statements" ("no auditor").

The results of the 22 discrete-time hazard models show that "other auditor" opinions have incremental contribution in predicting financial distress, in addition to "going concern" opinions. This suggests that "other auditor" opinions possess higher risk of overstating earnings and firms with such income items are more likely to fail. Besides, we find that the macroeconomic factors studied significantly explain financial distress. Particularly, the survivals of electronic firms are more sensitive to earnings due to higher earnings fluctuations in such firms. Finally, models with auditors' opinions, market factors, macroeconomic factors, and industry factors perform better than the financial ratio-only model in financial distress prediction.

Based on the notions of bipolar lattices and L-sets, YinYang bipolar fuzzy sets and fuzzy equilibrium relations are presented for bipolar clustering, optimization, and global regulation. While a bipolar L-set is defined as a bipolar equilibrium function L that maps a bipolar object set X over an arbitrary bipolar lattice B as L:X ⇒ B, this work focuses on the unit square lattice B_F = [-1, 0] × [0, 1]. A strong or weak bipolar fuzzy equilibrium relation in a bipolar set X is then defined as a reflexive, symmetric, and bipolar interactive (or transitive) fuzzy relation μ_R: X ⇒ B_F. Three types of bipolar α-level sets are presented for bipolar defuzzification and depolarization. It is shown that a fuzzy equilibrium relation is a non-linear bipolar generalization and/or fusion of multiple similarity relations, which induces disjoint or joint bipolar fuzzy subsets including quasi-coalition, conflict, and harmony sets. Equilibrium energy and stability analysis can then be utilized on different clusters for optimization and global regulation purposes. Thus, this work provides a unified approach to truth, fuzziness, and polarity and leads to a holistic theory for cognitive-map-based visualization, optimization, decision, global regulation, and coordination. Basic concepts are illustrated with a simulation in macroeconomics.

This research paper aims to study the impact of COVID-19 on the economy, where we have extended the standard epidemiological model, the Susceptible–Infected–Recovered (SIR) model. In the economy’s dynamics, we have incorporated aggregate demand and aggregate supply and further set up a system of three-dimensional nonlinear differential equations, which is more likely the Labor–Infected–Quarantine (LIQ) model, an extension of the SIR model. Here, we have derived the steady-state condition and analyzed the model’s stability. In addition, we have done a comparative static analysis of parameters with some policy implications. We aim to achieve the twin objective, i.e., controlling the pandemic and reviving the economy under the set of the policy mix.

The global crisis has not invalidated everything about macroeconomics. However, it has highlighted some of mistakes of the discipline's dominant intellectual framework. Post-macroeconomic thinking recommended in this paper should not be understood as another metanarrative of the end of metanarratives. The use of the prefix post here suggests and emphasises much more than temporal posterity. Post-macroeconomics should follow from macroeconomics more than it follows after macroeconomics. The theorising of post-macroeconomics is therefore neither systematically oppositional, nor hegemonic. It does not advocate a "dialectic opposition" between macroeconomics and post-macroeconomics. Rather, it suggests that the latter builds on the former and goes beyond it.

The objective of this paper is to analyze the concept and determinants of "sovereign risk" and the role of the credit risk rating agencies which serve internationally as the main reference instruments employed by economic agents to assess this risk. The paper also tries to identify macroeconomic variables which could be associated with sovereign risk ratings awarded by rating agencies to each country. After examining the indicators on an individual basis, their potential as a group is tested econometrically as a determinant of the class of sovereign risk into which national economies fall. Our results constitute a set of indicators which emerging economies would be well advised to improve upon.

Transitioning to a low-carbon economy will require significant investment to transform energy systems, alter the built environment and adapt infrastructure. A strategy to finance this investment is needed if the limit of a 2°C increase in global mean temperatures is to be respected. Also, high-income countries have pledged to pay the "agreed full incremental costs" of climate-change mitigation by developing countries, which are not necessarily the same as incremental investment costs. Building on simulations using Integrated Assessment Models and historical evidence, this paper explores some of the issues posed by this dual financing challenge. We discuss the "fiscal self-reliance" of the energy sector, finding that carbon pricing would generate sufficient fiscal revenues within each region to finance total investment in energy supply. Even when allowing for trade in emission permits, regional carbon fiscal revenues should still suffice to cover both their own investment in energy supply and permit purchases from abroad. We show that incremental energy-supply investment (and saving) needs are well within the range of past variation of aggregate investment, and argue that the challenge is rather to ensure that revenues from carbon pricing and other sources are complemented by investment in the appropriate sectors. But fairness and equity are likely to warrant transfers from advanced industrial countries to developing nations.

Peer-to-peer (P2P) lending is a fast growing financial technology (FinTech) trend that is displacing traditional retail banking. Studies on P2P lending have focused on predicting individual interest rates or default probabilities. However, the relationship between aggregated P2P interest rates and the general economy will be of interest to investors and borrowers as the P2P credit market matures. We show that the variation in P2P interest rates across grade types is determined by three macroeconomic latent factors formed by Canonical Correlation Analysis (CCA)—macro default, investor uncertainty, and the fundamental value of the market. However, the variation in P2P interest rates across term types cannot be explained by the general economy.

As technology revolutionizes the methods of both production and communication, economists have to constantly adjust their theories explaining the economy according to new market structures and efficiencies, and the controversial concept of decentralization emerging in recent decades should also be examined in terms of its capacity to induce structural changes in the economy. This paper delves into this topic and examines some cases of hypothetical decentralized markets, including the financial industry and the monetary system, in order to provide a preliminary illustration of how an economy composed of such markets would function and their corresponding benefits and risks, through a preliminary discussion about existing literatures and theories aiming to inspire further researches within the field.

In this chapter, we talk about the macroeconomic and health impacts of the COVID-19 pandemic in India. We begin by providing evidence on the evolution of aggregate income and active infections during the first wave of the pandemic. We then discuss a macro-SIR model that provides an integrated framework for studying both the economic and health ramifications of the pandemic. Finally, we quantify the economic costs associated with two government policies implemented during this time, lockdowns and income transfers, and analyze how successful they were in slowing down the disease spread.