The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
On the Casual Relationship between Government Expenditures and National Income
Abstract
A research strategy is suggested that separates the issue of inference from the problems of prediction or of quantitative policy analysis of an empirical parametric model and illustrates a new methodology that enables this in a test for prima facie causality. Unlike the conventional parametric test, the more powerful multiple rank F test is invariant to monotonic transformations of the variables and independent of the error distribution. Employing this test, the Wagnerian hypothesis, supported by conventional parametric analysis, is rejected and the conventional Keynesian theory is accepted. Copyright 1990 by MIT Press.
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Citations
Cites
An analysis of variance test for normality (complete samples)
SHAPIRO, S. S., & WILK, M. B. (1965) · Biometrika · doi:10.1093/biomet/52.3-4.591 · Cited by 23,480
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989
Engle, R. F. (1982) · Econometrica · doi:10.2307/1912773 · Cited by 20,795
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293
The Use of the Rank Transform in Regression
Iman, R. L., & Conover, W. J. (1979) · Technometrics · doi:10.1080/00401706.1979.10489820 · Cited by 514
Cited by
A new framework for testing the effect of government spending on growth and productivity
Hansson, P., & Henrekson, M. (1994) · Public Choice · doi:10.1007/bf01053239 · Cited by 288
Metadata
- Authors
- Holmes, J. M., & Hutton, P. A.
- Venue
- The Review of Economics and Statistics
- Publisher
- JSTOR
- Published
- 1990-02
- Volume
- 72
- Issue
- 1
- Pages
- 87
- ISSN
- 0034-6535 (ISSN-L)
- OpenAlex
- W1992739189
- Cited by
- 176
- References
- 5
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
