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Economic Infrastructure, Private Capital Formation, and FDI Inflows to Hungary: A Unit Root and Cointegration Analysis with Structural Breaks

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Abstract

This paper investigates the important question of what relationship, if any, exists between economic infrastructure, gross fixed capital formation, and FDI inflows to Hungary during the 1995–2012 period. Although this question has great significance from an economic policy standpoint, there has been little to no empirical analysis undertaken so far in the case of transition economies such as Hungary. Utilizing single-break unit root and cointegration analysis, this study finds a stable long-run relationship among the included variables, thus an error correction model is developed to capture both the short-and long-run behavior of the variables. In the short run, lagged changes in economic infrastructure, as well as lagged changes in private capital formation are positively associated with changes in FDI inflows; a dummy variable to capture the 2008 financial crisis and euro crisis has a negative and highly significant effect. In the long run, however, FDI inflows and private capital formation are substitutes for one another, while economic infrastructure crowds in private capital formation. The real effective exchange rate is positively correlated with FDI inflows in the long run, but not in the short run. The VEC model leads to the general conclusion that FDI flows and real GFCF have a significant short-run adjustment mechanism, while economic infrastructure and the real exchange rate can be treated as weakly exogenous.

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Notes

  1. However, the short-and long-run effects of FDI can differ from each other and might even be negative, if it leads to the elimination of domestic firms and/or substantial reverse flows of profits and dividends to the parent company that divert resources away from financing domestic capital formation (see Ram and Zhang 2002).

  2. The FDI series used in this paper includes capital in transit flows and the operation of special purpose entities (SPE).

  3. Other lag specifications were tried but the results were not altered.

  4. Similar significant results were obtained for the G-H test with eight and nine lags. (The detailed results and the log file are available upon request).

  5. The Johansen test was also normalized on the log of real GFCF and the results are consistent with those reported in the text and are available upon request.

  6. The length of the lags in the EC model was determined by the AIC and SBC criteria.

  7. Model 3 is chosen because, as indicated in Section 4.2, the trend variable in Model 4 was not statistically significant. The VECM was also estimated with a dummy variable for the 2008–09 crisis and the ongoing eurocrisis and the results were significantly better and more significant. (Detailed results are available upon request).

  8. VECM/VAR modeling is especially useful for building strong forecasting models on the basis of R-squared and SBC/AIC criteria. In this study we focus on the relationship among the four variables and a forecast model is not estimated.

  9. Detailed Results are available upon request.

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Correspondence to Miguel D. Ramirez.

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Ramirez, M.D., Kőműves, Z. Economic Infrastructure, Private Capital Formation, and FDI Inflows to Hungary: A Unit Root and Cointegration Analysis with Structural Breaks. Atl Econ J 42, 367–382 (2014). https://doi.org/10.1007/s11293-014-9436-0

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