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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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|a Liu, Yang.
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|a Housing Boom and Headline Inflation :
|b Insights from Machine Learning /
|c Yang Liu, Di Yang, Yunhui Zhao.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2022.
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|a 1 online resource (45 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a Inflation has been rising during the pandemic against supply chain disruptions and a multi-year boom in global owner-occupied house prices. We present some stylized facts pointing to house prices as a leading indicator of headline inflation in the U.S. and eight other major economies with fast-rising house prices. We then apply machine learning methods to forecast inflation in two housing components (rent and owner-occupied housing cost) of the headline inflation and draw tentative inferences about inflationary impact. Our results suggest that for most of these countries, the housing components could have a relatively large and sustained contribution to headline inflation, as inflation is just starting to reflect the higher house prices. Methodologically, for the vast majority of countries we analyze, machine-learning models outperform the VAR model, suggesting some potential value for incorporating such models into inflation forecasting.
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|a Mode of access: Internet
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|a Econometric and Statistical Methods
|2 imf
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|a Forecasting and Other Model Applications
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|a Inflation
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|a Price Level
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|a Yang, Di.
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|a Zhao, Yunhui.
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|a IMF Working Papers; Working Paper ;
|v No. 2022/151
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|z Full text available on IMF
|u https://elibrary.imf.org/openurl?genre=journal&issn=1018-5941&volume=2022&issue=151
|z IMF e-Library
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