Lyocsa, Stefan (2015): Predicting changes in the output of OECD countries: An international network perspective.
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Abstract
We use a simple linear regression framework to present evidence, that complex relationships between stock markets and economies may be used to predict changes in the output of 27 OECD countries. We construct new unidirectional return co-exceedance networks to account for complex relationships between stock market returns, and between real economic growths. Although there is heterogeneity between individual country level results, overall our data and analysis provides evidence that topological properties of our networks are useful for in-sample prediction of next quarter changes in the output.
Item Type: | MPRA Paper |
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Original Title: | Predicting changes in the output of OECD countries: An international network perspective |
English Title: | Predicting changes in the output of OECD countries: An international network perspective |
Language: | English |
Keywords: | harmonic centrality centralization networks co-exceedance economic growth |
Subjects: | E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E44 - Financial Markets and the Macroeconomy G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O40 - General |
Item ID: | 65774 |
Depositing User: | Stefan Lyocsa |
Date Deposited: | 28 Jul 2015 20:06 |
Last Modified: | 30 Sep 2019 14:49 |
References: | Baur, D., Schulze, N. 2005. Coexceedances in financial markets—a quantile regression analysis of contagion. Emerging markets review, 6 (1), 21–43. Billio, M., Getmansky, M., Lo, A. W., and Pelizzon, L. 2012. Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104 (3), 535–559. Boldi, P., Vigna, S. 2014. Axioms of Centrality. Internet Mathematics, 10 (3-4), 222–262. Canova, F., De Nicolo, G. 1995. Stock returns and real activity: A structural approach. European Economic Review, 39 (5), 981–1015. Cribari-Neto, F. 2004. Asymptotic Inference under heteroscedasticity of unknown form. Computational Statistics & Data Analysis, 45 (2), 215–233. Cribari-Neto, F., Zarkos, S.G. 1999. Bootstrap methods for heteroskedastic regression models: evidence on estimation and testing. Econometric Reviews, 18 (2), 211–228. Lin, J.W., McLeod, I. A. 2006. Improved Peňa–Rodriguez portmanteau test. Computational Statistics & Data Analysis, 51(3), 1731–1738. Mantegna, R. N. 1999. Hierarchical structure in financial markets. The European Physical Journal B – Condensed Matter and Complex Systems, 11 (1), 193–197. Newey, W. K., West, K. D. 1994. Automatic Lag Selection in Covariance Matrix Estimation. The Review of Economic Studies, 61 (4), 631–653. Peña, D., Rodríguez, J. 2006. The log of the determination of the autocorrelation matrix for testing goodness of fit in time series. Journal of Statistical Planning and Inference, 136 (8), 2706–2718. Pesaran, H.M., Smith, R. Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68 (1), 79–113. Prim, R. C. 1957. Shortest connection networks and some generalizations. Bell System Technical Journal, 36 (6), 1389–1401. White, H. 1980. A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48 (4), 817–838. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/65774 |