2021 Conference Announcement - (2024) Volume 9, Issue 7
Received: 26-Sep-2022, Manuscript No. jbbs-23-87910;
Editor assigned: 28-Sep-2022, Pre QC No. P-87910;
Reviewed: 12-Oct-2022, QC No. Q-87910;
Revised: 18-Oct-2022, Manuscript No. R-87910;
, DOI: 10.37421/2162-6359.2023.12.682
, QI Number: 1
Citation: Imbalzano, Marco. â??Making Use of Machine Learning Algorithms for Multimodal Equipment to Assist in COVID-19's Assessment.â? J Bioengineer & Biomedical Sci 12 (2022): 325.
Copyright: Â© 2022 Imbalzano M. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Sources of funding : 1
The global financial crisis of the late 2000s rekindled interest in asset price bubble research. The experience brought home to some people the significance of analyzing asset price bubbles in our economy. Others realized that asset price bubbles are much more significant than previously thought. A lot of attention has been paid to the financial and regulatory factors that encourage "frothy" asset values, which are characteristic of bubbles. Although it is undeniably significant, there are other aspects that need to be better understood. We also need a deeper understanding of asset price bubbles' entire life cycle, from their inception through their growth and spread, their final collapse, and the subsequent cleanup. It is becoming increasingly clear to researchers that bubbles are not a one-time, external phenomenon. The problem lies in incorporating endogenous bubble behavior into our policy models using a more comprehensive strategy. Even though this kind of behavior is rare, it is caused by a number of things that could have been avoided, like financial instability, bad laws, and poor risk management decisions.
At the end of the 1990s, Asia became the epicenter of a massive global financial crisis that eventually reached the rest of the world. Beginning in Thailand, the 1997–1998 Asian financial crisis quickly spread to neighboring nations. When Bangkok separated the Thai baht from the US dollar, it started a currency crisis that led to a series of devaluations and significant capital outflows. In the first six months, the value of the Indonesian rupiah dropped by 80%, the Thai baht by more than 50%, the South Korean won by about 50%, and the Malaysian ringgit by 45%. Capital inflows to the most affected economies decreased by more than $100 billion in the first year of the crisis. In terms of volume and scope, the Asian financial crisis transformed into a global one when it reached the economies of Brazil and Russia.
There are numerous repercussions of the Asian financial crisis. The events of 1997 and 1998 may also be viewed as a crisis of governance at all major political levels, despite the fact that the crisis is frequently referred to as a financial or economic crisis: global, national, and local The state's inability to manage globalization processes and demands from foreign actors, as well as its historical regulatory responsibilities, were particularly exposed by the Asian financial crisis. Despite Malaysia's relatively successful short-term capital controls and Prime Minister Mahathir bin Mohamad's ability to resist IMF-style reforms, the majority of states' inability to resist IMF pressures and reforms brought attention to the erosion of state authority and the loss of government control. The most striking scenario was that of Indonesia, where governmental flaws contributed to the transformation of an economic crisis into a political crisis, which ultimately resulted in the death of Suharto, who had ruled politics in Indonesia for more than 30 years [1-5].
The estimates above were based on the aggregate projection methodology, which has known limitations. Employment elasticities change as well, as different economic sectors grow or shrink at different rates. The elasticity of employment, for instance, is quite low (and even negative) in Indonesian agriculture, but it is extremely strong in construction, commerce, and services. Contrarily, agriculture employs more people than construction, trade, and services combined. Consequently, the former's fate remains crucial to the Indonesian economy. When estimating employment, it is crucial to take into account sectoral production growth and employment elasticities. A major obstacle to doing so is the absence of accurate predictions for sectoral production growth. The current author was unable to locate figures in Indonesia that were comparable. 11 On the other hand, Credit Lyonnais provided a series of estimates by sector, projecting a 5% decline in the Indonesian economy overall in 1998. In place of this, and in accordance with the within-country projection of zero GDP growth, we developed an alternative set of sectoral forecasts on the basis of the following assumptions.
In economies with a significant tail risk, a short-term overshoot of inflation should not be considered a policy error but rather a component of an optimal response to monetary policy. In other words, tight inflation targeting is not optimal in the current policy environment. Additionally, the model's more complex tradeoffs may make it more difficult to communicate with central banks with flexible inflation-targeting regimes, which have previously persuaded the public that medium-term inflation deviations from target were a yardstick for a central bank's performance without observable supply or demand shocks.
The authors declare that there is no conflict of interest associated with this manuscript.