Nowcasting Swedish GDP with a large and unbalanced data set
(2019) In Empirical Economics 57. p.1351-1373- Abstract
- We evaluate pseudo-real-time out-of-sample nowcasts for Swedish GDP employing factor models and mixed-data sampling regressions with single predictor variables. These two model classes can handle the data irregularities of a ragged-edge sample and differing sampling frequencies. The results show that pooling of the nowcasts outperforms a simple benchmark, even though only very few of the underlying specifications achieve improved accuracy individually. Moreover, we assess the accuracy of the density forecasts, i.e., the uncertainty around the point forecasts. The post-crisis period after 2008 turns out to be a more difficult period to nowcast precisely. However, indicator variables prove more useful post-crisis as then the performance... (More)
- We evaluate pseudo-real-time out-of-sample nowcasts for Swedish GDP employing factor models and mixed-data sampling regressions with single predictor variables. These two model classes can handle the data irregularities of a ragged-edge sample and differing sampling frequencies. The results show that pooling of the nowcasts outperforms a simple benchmark, even though only very few of the underlying specifications achieve improved accuracy individually. Moreover, we assess the accuracy of the density forecasts, i.e., the uncertainty around the point forecasts. The post-crisis period after 2008 turns out to be a more difficult period to nowcast precisely. However, indicator variables prove more useful post-crisis as then the performance relative to univariate benchmarks improves. (Less)
- Abstract (Swedish)
- We evaluate pseudo-real-time out-of-sample nowcasts for Swedish GDP employing factor models and mixed-data sampling regressions with single predictor variables. These two model classes can handle the data irregularities of a ragged-edge sample and differing sampling frequencies. The results show that pooling of the nowcasts outperforms a simple benchmark, even though only very few of the underlying specifications achieve improved accuracy individually. Moreover, we assess the accuracy of the density forecasts, i.e., the uncertainty around the point forecasts. The post-crisis period after 2008 turns out to be a more difficult period to nowcast precisely. However, indicator variables prove more useful post-crisis as then the performance... (More)
- We evaluate pseudo-real-time out-of-sample nowcasts for Swedish GDP employing factor models and mixed-data sampling regressions with single predictor variables. These two model classes can handle the data irregularities of a ragged-edge sample and differing sampling frequencies. The results show that pooling of the nowcasts outperforms a simple benchmark, even though only very few of the underlying specifications achieve improved accuracy individually. Moreover, we assess the accuracy of the density forecasts, i.e., the uncertainty around the point forecasts. The post-crisis period after 2008 turns out to be a more difficult period to nowcast precisely. However, indicator variables prove more useful post-crisis as then the performance relative to univariate benchmarks improves. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/debc9741-a214-48a2-adf0-1f84d5d2d712
- author
- den Reijer, Ard and Johansson, Andreas LU
- publishing date
- 2019-07-25
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Empirical Economics
- volume
- 57
- pages
- 1351 - 1373
- publisher
- Physica Verlag
- external identifiers
-
- scopus:85050742232
- ISSN
- 0377-7332
- DOI
- 10.1007/s00181-018-1500-1
- language
- English
- LU publication?
- no
- id
- debc9741-a214-48a2-adf0-1f84d5d2d712
- date added to LUP
- 2024-11-22 12:55:12
- date last changed
- 2025-04-04 14:20:47
@article{debc9741-a214-48a2-adf0-1f84d5d2d712,
abstract = {{We evaluate pseudo-real-time out-of-sample nowcasts for Swedish GDP employing factor models and mixed-data sampling regressions with single predictor variables. These two model classes can handle the data irregularities of a ragged-edge sample and differing sampling frequencies. The results show that pooling of the nowcasts outperforms a simple benchmark, even though only very few of the underlying specifications achieve improved accuracy individually. Moreover, we assess the accuracy of the density forecasts, i.e., the uncertainty around the point forecasts. The post-crisis period after 2008 turns out to be a more difficult period to nowcast precisely. However, indicator variables prove more useful post-crisis as then the performance relative to univariate benchmarks improves.}},
author = {{den Reijer, Ard and Johansson, Andreas}},
issn = {{0377-7332}},
language = {{eng}},
month = {{07}},
pages = {{1351--1373}},
publisher = {{Physica Verlag}},
series = {{Empirical Economics}},
title = {{Nowcasting Swedish GDP with a large and unbalanced data set}},
url = {{http://dx.doi.org/10.1007/s00181-018-1500-1}},
doi = {{10.1007/s00181-018-1500-1}},
volume = {{57}},
year = {{2019}},
}