Seeing in the Dark : A Machine-Learning Approach to Nowcasting in Lebanon /
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 the 'nowcasting' challenge familiar to many central banks. Addressing this p...
| প্রধান লেখক: | |
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| বিন্যাস: | পত্রিকা |
| ভাষা: | English |
| প্রকাশিত: |
Washington, D.C. :
International Monetary Fund,
2016.
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| মালা: | IMF Working Papers; Working Paper ;
No. 2016/056 |
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Full text available on IMF |
| সংক্ষিপ্ত: | 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 the 'nowcasting' challenge familiar to many central banks. Addressing this problem-and mindful of the pitfalls of extracting information from a large number of correlated proxies-we explore some recent techniques from the machine learning literature. We focus on two popular techniques (Elastic Net regression and Random Forests) and provide an estimation procedure that is intuitively familiar and well suited to the challenging features of Lebanon's data. |
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| উপাদানের বিবরণ: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| দৈহিক বর্ননা: | 1 online resource (20 pages) |
| বিন্যাস: | Mode of access: Internet |
| আইএসএসএন: | 1018-5941 |
| প্রবেশাধিকার: | Electronic access restricted to authorized BRAC University faculty, staff and students |