The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Testing for Common Features
Abstract
This article introduces a class of statistical tests for the hypothesis that some feature that is present in each of several variables is common to them. Features are data properties such as serial correlation, trends, seasonality, heteroscedasticity, autoregressive conditional hetero-scedasticity, and excess kurtosis. A feature is detected by a hypothesis test taking no feature as the null, and a common feature is detected by a test that finds linear combinations of variables with no feature. Often, an exact asymptotic critical value can be obtained that is simply a test of overidentifying restrictions in an instrumental variable regression. This article tests for a common international business cycle.
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Citations
Cites
Co-integration and error correction: representation, estimation and testing
Engle, R. F., & Granger, C. W. J. (1987) · Econometrica · doi:10.2307/1913236 · Cited by 32,007
Self-citationEngle, R. F. (1982) · Econometrica · doi:10.2307/1912773 · Cited by 20,795
Self-citationSTATISTICAL ANALYSIS OF COINTEGRATION VECTORS
Johansen, S. (1988) · Journal of Economic Dynamics and Control · doi:10.1016/0165-1889(88)90041-3 · Cited by 17,027
Metadata
- Authors
- Engle, R. F., & Kozicki, S.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 1993-10
- Volume
- 11
- Issue
- 4
- Pages
- 369-380
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W4205444576
- Cited by
- 195
- References
- 3
- FWCI
- 11.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- Engle, R. F., & Kozicki, S. (1993). Testing for Common Features. Journal of Business & Economic Statistics. https://doi.org/10.1080/07350015.1993.10509966
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
