AVM and high dimensional data: Do ridge, the lasso or the elastic net provide an "automated" solution?
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DOI: 10.18452/21263
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- Jens Kolbe & Rainer Schulz & Martin Wersing & Axel Werwatz, 2012.
"Location, Location, Location: Extracting Location Value from House Prices,"
Discussion Papers of DIW Berlin
1216, DIW Berlin, German Institute for Economic Research.
- Kolbe, Jens & Schulz, Rainer & Wersing, Martin & Werwatz, Axel, 2013. "Location, location, location: Extracting location value from house prices," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79732, Verein für Socialpolitik / German Economic Association.
- Kolbe, Jens & Schulz, Rainer & Wersing, Martin & Werwatz, Axel, 2012. "Location, location, location: Extracting location value from house prices," SFB 649 Discussion Papers 2012-040, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
- Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
- Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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More about this item
Keywords
Automated valuation; Machine learning; Elastic Net; Forecastperformance;All these keywords.
JEL classification:
- R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2021-01-25 (Big Data)
- NEP-CMP-2021-01-25 (Computational Economics)
- NEP-ORE-2021-01-25 (Operations Research)
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