Predicting the Global Crisis Recovery Period: Lessons from the 1997 Crisis
The objective of this paper is to identify the best indicator in forecasting the recovery period from the current global crisis for Malaysia. Initially, to determine the best indicator for the recovery period, we construct a simple forecasting model that incorporates three indicators: lagging, leadi...
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my.usim-83752015-12-31T03:08:21Z Predicting the Global Crisis Recovery Period: Lessons from the 1997 Crisis J, Duasa, N, Ahmad, Indicator forecasting crisis recovery Malaysia The objective of this paper is to identify the best indicator in forecasting the recovery period from the current global crisis for Malaysia. Initially, to determine the best indicator for the recovery period, we construct a simple forecasting model that incorporates three indicators: lagging, leading and coincidence indices, with two proxies of economic performance, macroeconomic and financial variables. We estimate a two-variable vector error correction model (VECM) using monthly and quarterly data covering the period 1980 to 2000. We alternate between the three indicators and we evaluate each model using out-of-sample forecast. Using the results of the initial process of analysis, we predict the recovery period of Malaysian economy from the current global economic crisis. It is found that lagging index is the best indicator of financial performance of the economy. From the half-life calculation base on error correction term, the study found that Malaysia was able to recover from the previous 1997 crisis within a two to four year period after the crisis. Given that the current crisis environment is similar to the previous 1997 crisis, a similar time period could apply to the current global crisis recovery. 2015-06-15T07:47:25Z 2015-06-15T07:47:25Z 2010 Article 1226-508X http://ddms.usim.edu.my/handle/123456789/8375 en Routledge Journals, Taylor & Francis Ltd |
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Indicator forecasting crisis recovery Malaysia J, Duasa, N, Ahmad, Predicting the Global Crisis Recovery Period: Lessons from the 1997 Crisis |
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The objective of this paper is to identify the best indicator in forecasting the recovery period from the current global crisis for Malaysia. Initially, to determine the best indicator for the recovery period, we construct a simple forecasting model that incorporates three indicators: lagging, leading and coincidence indices, with two proxies of economic performance, macroeconomic and financial variables. We estimate a two-variable vector error correction model (VECM) using monthly and quarterly data covering the period 1980 to 2000. We alternate between the three indicators and we evaluate each model using out-of-sample forecast. Using the results of the initial process of analysis, we predict the recovery period of Malaysian economy from the current global economic crisis. It is found that lagging index is the best indicator of financial performance of the economy. From the half-life calculation base on error correction term, the study found that Malaysia was able to recover from the previous 1997 crisis within a two to four year period after the crisis. Given that the current crisis environment is similar to the previous 1997 crisis, a similar time period could apply to the current global crisis recovery. |
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Article |
author |
J, Duasa, N, Ahmad, |
author_facet |
J, Duasa, N, Ahmad, |
author_sort |
J, Duasa, |
title |
Predicting the Global Crisis Recovery Period: Lessons from the 1997 Crisis |
title_short |
Predicting the Global Crisis Recovery Period: Lessons from the 1997 Crisis |
title_full |
Predicting the Global Crisis Recovery Period: Lessons from the 1997 Crisis |
title_fullStr |
Predicting the Global Crisis Recovery Period: Lessons from the 1997 Crisis |
title_full_unstemmed |
Predicting the Global Crisis Recovery Period: Lessons from the 1997 Crisis |
title_sort |
predicting the global crisis recovery period: lessons from the 1997 crisis |
publisher |
Routledge Journals, Taylor & Francis Ltd |
publishDate |
2015 |
url |
http://ddms.usim.edu.my/handle/123456789/8375 |
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1645152404924006400 |
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13.222552 |