The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model
Abstract
Financial institutions are ultimately exposed to macroeconomic fluctuations in the global economy. This article proposes and builds a compact global model capable of generating forecasts for a core set of macroeconomic factors (or variables) across a number of countries. The model explicitly allows for the interdependencies that exist between national and international factors. Individual region-specific vector error-correcting models are estimated in which the domestic variables are related to corresponding foreign variables constructed exclusively to match the international trade pattern of the country under consideration. The individual country models are then linked in a consistent and cohesive manner to generate forecasts for all of the variables in the world economy simultaneously. The global model is estimated for 25 countries grouped into 11 regions using quarterly data over 1979Q1–1999Q1. The degree of regional interdependencies is investigated via generalized impulse responses where the effects of shocks to a given variable in a given country on the rest of the world are provided. The model is then used to investigate the effects of various global risk scenarios on a bank's loan portfolio.
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Cites
STATISTICAL 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
Filders, R. (1992) · International Journal of Forecasting · doi:10.1016/0169-2070(92)90130-2 · Cited by 16,165
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Sims, C. A. (1980) · Econometrica · doi:10.2307/1912017 · Cited by 12,727
Pooled Mean Group Estimation of Dynamic Heterogeneous Panels
Pesaran, M. H., Shin, Y., & Smith, R. P. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10474156 · Cited by 6,683
Self-citationGeneralized impulse response analysis in linear multivariate models
Pesaran, H. H., & Shin, Y. (1998) · Economics Letters · doi:10.1016/s0165-1765(97)00214-0 · Cited by 6,097
Spurious regression and residual-based tests for cointegration in panel data
Kao, C. (1999) · Journal of Econometrics · doi:10.1016/s0304-4076(98)00023-2 · Cited by 5,763
Efficient Tests for an Autoregressive Unit Root
Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996) · Econometrica · doi:10.2307/2171846 · Cited by 5,627
Impulse response analysis in nonlinear multivariate models
Koop, G., Pesaran, M. H., & Potter, S. M. (1996) · Journal of Econometrics · doi:10.1016/0304-4076(95)01753-4 · Cited by 5,049
Self-citationExpectations and Exchange Rate Dynamics
Dornbusch, R. (1976) · Journal of Political Economy · doi:10.1086/260506 · Cited by 4,665
Cited by
General Diagnostic Tests for Cross Section Dependence in Panels
Pesaran, M. H. (2004) · doi:10.2139/ssrn.572504 · Cited by 8,738
Self-citationEstimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
Pesaran, M. H. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00692.x · Cited by 5,078
Self-citationTesting Weak Cross-Sectional Dependence in Large Panels
Pesaran, M. H. (2015) · Econometric Reviews · doi:10.1080/07474938.2014.956623 · Cited by 2,950
Self-citationBaltagi, B. H. (2008) · Journal of Forecasting · doi:10.1002/for.1047 · Cited by 574
Song, H., Qiu, R. T. R., & Park, J. (2019) · Annals of Tourism Research · doi:10.1016/j.annals.2018.12.001 · Cited by 523
Metadata
- Authors
- Pesaran, M. H., Schuermann, T., & Weiner, S. M.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2004-04
- Volume
- 22
- Issue
- 2
- Pages
- 129-162
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W3022367431
- Cited by
- 1,456
- References
- 28
- FWCI
- 42.38
- Type
- journal article
- Language
- en
- Open access
- closed
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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.