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150128 ||| eng |
| 020 |
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|a 9781455220977
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| 100 |
1 |
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|a Zeng, Li
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| 245 |
0 |
0 |
|a Evaluating GDP Forecasting Models for Korea
|c Li Zeng
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2011
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| 300 |
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|a 23 pages
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| 651 |
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4 |
|a Korea, Republic of
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| 653 |
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|a Wealth
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| 653 |
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|a Economics
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| 653 |
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|a Private consumption
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| 653 |
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|a Dynamic Treatment Effect Models
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| 653 |
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|a Saving
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| 653 |
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|a Economic Forecasting
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a Economic forecasting
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| 653 |
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|a Time series analysis
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| 653 |
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|a Vector autoregression
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| 653 |
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|a National income
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| 653 |
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|a Time-Series Models
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| 653 |
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|a Forecasting
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| 653 |
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|a Forecasting and Other Model Applications
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| 653 |
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|a Consumption
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| 653 |
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|a Macroeconomics
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| 653 |
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|a Gdp forecasting
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| 653 |
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|a Macroeconomics: Consumption
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| 653 |
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|a Dynamic Quantile Regressions
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| 653 |
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|a Econometrics
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| 653 |
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|a Econometrics & economic statistics
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| 653 |
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|a State Space Models
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| 653 |
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|a Forecasting and Simulation: Models and Applications
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
0 |
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|a IMF Working Papers
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| 028 |
5 |
0 |
|a 10.5089/9781455220977.001
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| 856 |
4 |
0 |
|u https://elibrary.imf.org/view/journals/001/2011/053/001.2011.issue-053-en.xml?cid=24700-com-dsp-marc
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 330
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| 520 |
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|a This paper develops a new forecasting framework for GDP growth in Korea to complement and further enhance existing forecasting approaches. First, a range of forecast models, including indicator- and pure time-series models, are evaluated for their forecasting performance. Based on the evaluation results, a new forecasting framework is developed for GDP projections. The framework also generates a data-driven reference band for the projections, and is therefore convenient to update. The framework is applied to the current World Economic Outlook (WEO) forecast period and the Great Recession to compare its performance to past projections. Results show that the performance of the new framework often improves the forecasts, especially at quarterly frequency, and the forecasting exercise will be better informed by cross-checking with the new data-driven framework projections
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