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Sustainability and Equity Challenges

Sustainability and Equity Challenges »

Source: Sustainability and Equity Challenges : Some Arithmetic on Lebanon's Pension System

Volume/Issue: 2016/46

Series: IMF Working Papers

Author(s): Mariusz Jarmuzek , and Najla Nakhle

Publisher: INTERNATIONAL MONETARY FUND

Publication Date: 02 March 2016

ISBN: 9781513541488

Keywords: demographics, equity, pension reform, sustainability, pension, pension expenditure, pension system, retirement, employees, Social Security and Public Pensions

Reform of Lebanon's pension system is indispensable. The country already faces fiscal sustainability risks, which will be compounded in the future by significantly higher pensionrelated spending and liabilities, ma...

Seeing in the Dark

Seeing in the Dark »

Source: Seeing in the Dark : A Machine-Learning Approach to Nowcasting in Lebanon

Volume/Issue: 2016/56

Series: IMF Working Papers

Author(s): Andrew Tiffin

Publisher: INTERNATIONAL MONETARY FUND

Publication Date: 08 March 2016

ISBN: 9781513568089

Keywords: Macroeconomic Forecasts, Nowcasting, Random Forests, Elastic Net, LASSO, Statistical Learning, Cross Validation, Ensemble, Variable Selection, gdp

Macroeconomic analysis in Lebanon presents a distinct challenge. For example, long delays in the publication of GDP data mean that our analysis often relies on proxy variables, and resembles an extended version of...

Sustainability and Equity Challenges
			: Some Arithmetic on Lebanon's Pension System

Sustainability and Equity Challenges : Some Arithmetic on Lebanon's Pension System »

Volume/Issue: 2016/46

Series: IMF Working Papers

Author(s): Mariusz Jarmuzek , and Najla Nakhle

Publisher: INTERNATIONAL MONETARY FUND

Publication Date: 02 March 2016

DOI: http://dx.doi.org/10.5089/9781513541488.001

ISBN: 9781513541488

Keywords: demographics, equity, pension reform, sustainability, pension, pension expenditure, pension system, retirement, employees, Social Security and Public Pensions

Reform of Lebanon's pension system is indispensable. The country already faces fiscal sustainability risks, which will be compounded in the future by significantly higher pensionrelated spending and liabilities, ma...

Seeing in the Dark
			: A Machine-Learning Approach to Nowcasting in Lebanon

Seeing in the Dark : A Machine-Learning Approach to Nowcasting in Lebanon »

Volume/Issue: 2016/56

Series: IMF Working Papers

Author(s): Andrew Tiffin

Publisher: INTERNATIONAL MONETARY FUND

Publication Date: 08 March 2016

DOI: http://dx.doi.org/10.5089/9781513568089.001

ISBN: 9781513568089

Keywords: Macroeconomic Forecasts, Nowcasting, Random Forests, Elastic Net, LASSO, Statistical Learning, Cross Validation, Ensemble, Variable Selection, gdp

Macroeconomic analysis in Lebanon presents a distinct challenge. For example, long delays in the publication of GDP data mean that our analysis often relies on proxy variables, and resembles an extended version of...