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Key Economic Indicators as an Early Warning System of Recessions.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Key Economic Indicators as an Early Warning System of Recessions./
Author:	Djermouni, Anis.
Description:	1 online resource (65 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
Subject:	Finance. -
Online resource:	click for full text (PQDT)
ISBN:	9780355390681

Key Economic Indicators as an Early Warning System of Recessions.
Djermouni, Anis.

Key Economic Indicators as an Early Warning System of Recessions. - 1 online resource (65 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.S.)

Includes bibliographical references

Forecasting economic recessions is of strategic importance to all participants in the economy. All stakeholders are impacted to different degrees by recessions, thus early warning systems (EWS) of recession are of critical importance in order for them to take preemptive measures and policies to lessen its impact on society. The main sources of data to create these EWS models are economic indicators. These economic variables can be processed through statistical methods, in order to analyze and forecast potential recessions. These methods have the potential to reveal trends and signal recessions at different degrees of horizons, consistency and accuracy. These indicators can be selected in terms of how leading they are in terms of GDP fluctuation in order to have this early warning ability. Other key indicators can be also used if they are rich in information and are very reflective of the economic activity. This research employs eight US key economic indicators, most of which are leading indicators of economic activity. They will be used in order to cluster their volatilities which in this research will be done through the General Auto Regressive Conditional Heteroskedasticity (GARCH) regression analysis. These indicators will then be indexed using the Principal Component Analysis (PCA). Forecasting based upon these indicators will also be performed using the Mixed Data Sampling (MIDAS) method introduced by Ghysels et al (2004). The US indicators used in this research have a horizon of fifty years, from 1960 to 2016. They will be tested and converted accordingly, in order to make sure that stationarity is observed, which is important for the purpose of this study. The conditional variance of the GARCH regression for each indicators suggested that the most performing indicators were the bond spread and the share price index. This was assessed through their accuracy and horizon of prediction of recessions. The signals behaved better for these indicators with a longer horizon for bond spreads. Their conditional variances were indexed in order to model a more performing EWS.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355390681Subjects--Topical Terms:

559073
Finance.
Index Terms--Genre/Form:

554714
Electronic books.

Key Economic Indicators as an Early Warning System of Recessions.
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245 1 0 $a Key Economic Indicators as an Early Warning System of Recessions.
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500 $a Source: Masters Abstracts International, Volume: 57-02.
500 $a Advisers: Nikolaos Antonakakis; Ioannis Chatziantoniou.
502 $a Thesis (M.S.) $c Webster University $d 2017.
504 $a Includes bibliographical references
520 $a Forecasting economic recessions is of strategic importance to all participants in the economy. All stakeholders are impacted to different degrees by recessions, thus early warning systems (EWS) of recession are of critical importance in order for them to take preemptive measures and policies to lessen its impact on society. The main sources of data to create these EWS models are economic indicators. These economic variables can be processed through statistical methods, in order to analyze and forecast potential recessions. These methods have the potential to reveal trends and signal recessions at different degrees of horizons, consistency and accuracy. These indicators can be selected in terms of how leading they are in terms of GDP fluctuation in order to have this early warning ability. Other key indicators can be also used if they are rich in information and are very reflective of the economic activity. This research employs eight US key economic indicators, most of which are leading indicators of economic activity. They will be used in order to cluster their volatilities which in this research will be done through the General Auto Regressive Conditional Heteroskedasticity (GARCH) regression analysis. These indicators will then be indexed using the Principal Component Analysis (PCA). Forecasting based upon these indicators will also be performed using the Mixed Data Sampling (MIDAS) method introduced by Ghysels et al (2004). The US indicators used in this research have a horizon of fifty years, from 1960 to 2016. They will be tested and converted accordingly, in order to make sure that stationarity is observed, which is important for the purpose of this study. The conditional variance of the GARCH regression for each indicators suggested that the most performing indicators were the bond spread and the share price index. This was assessed through their accuracy and horizon of prediction of recessions. The signals behaved better for these indicators with a longer horizon for bond spreads. Their conditional variances were indexed in order to model a more performing EWS.
520 $a The marginal effects extracted showed that the CLI has a bigger impact and effect on the recession variable. The CLI has better EWS attributes but still displays false signals. The key indicators can be also used for forecasting purposes and trends can interpreted to foresee recessions. The MIDAS regression based on these key indicators showed that significant accuracy can be reached when using these variables. This confirms and comforts the idea that macro-economic data is of crucial importance to all stakeholders as they enable researchers to construct EWS of recessions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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650 4 $a Statistics. $3 556824
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773 0 $t Masters Abstracts International $g 57-02(E).
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