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150128 ||| eng |
| 020 |
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|a 9781463922016
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| 100 |
1 |
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|a Kisinbay, Turgut
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| 245 |
0 |
0 |
|a Predicting Recessions
|b A New Approach for Identifying Leading Indicators and Forecast Combinations
|c Turgut Kisinbay, Chikako Baba
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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 30 pages
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| 651 |
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4 |
|a United States
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| 653 |
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|a Labor market
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a National accounts
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| 653 |
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|a Income economics
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| 653 |
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|a Labor
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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 Labor markets
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| 653 |
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|a Labour
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| 653 |
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|a Business cycles
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| 653 |
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|a Macroeconomics
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| 653 |
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|a Infrastructure
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| 653 |
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|a Economic growth
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| 653 |
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|a Capacity utilization
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| 653 |
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|a Multiple or Simultaneous Equation Models
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| 653 |
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|a Time-Series Models
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| 653 |
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|a Economic Development: Urban, Rural, Regional, and Transportation Analysis
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| 653 |
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|a Model Evaluation and Selection
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| 653 |
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|a Cycles
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| 653 |
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|a Production and Operations Management
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| 653 |
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|a Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data)
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| 653 |
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|a Production
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| 653 |
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|a Dynamic Quantile Regressions
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| 653 |
|
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|a Multiple Variables: General
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| 653 |
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|a State Space Models
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| 653 |
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|a Cyclical indicators
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| 653 |
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|a Business Fluctuations
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| 653 |
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|a Demand and Supply of Labor: General
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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 and investment
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| 653 |
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|a Macroeconomics: Production
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| 653 |
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|a Industrial capacity
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| 653 |
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|a Prices, Business Fluctuations, and Cycles: Forecasting and Simulation
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| 653 |
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|a Housing
|
| 700 |
1 |
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|a Baba, Chikako
|
| 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 10.5089/9781463922016.001
|
| 856 |
4 |
0 |
|u https://elibrary.imf.org/view/journals/001/2011/235/001.2011.issue-235-en.xml?cid=25288-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 study proposes a data-based algorithm to select a subset of indicators from a large data set with a focus on forecasting recessions. The algorithm selects leading indicators of recessions based on the forecast encompassing principle and combines the forecasts. An application to U.S. data shows that forecasts obtained from the algorithm are consistently among the best in a large comparative forecasting exercise at various forecasting horizons. In addition, the selected indicators are reasonable and consistent with the standard leading indicators followed by many observers of business cycles. The suggested algorithm has several advantages, including wide applicability and objective variable selection
|