The recent general equilibrium theory of trade and multinationals emphasizes the importance of third countries and the complex integration strategies of multinationals. Little has been done to test this theory empirically. This paper attempts to rectify this situation by considering not only bilateral determinants, but also spatially weighted third-country determinants of foreign direct investment (FDI). Since the dependency among host markets is particularly related to multinationals' trade between them, we use trade costs (distances) as spatial weights. Using panel data on U.S. industries and host countries observed over the 1989-1999 period, we estimate a "complex FDI" version of the knowledge-capital model of U.S. outward FDI by various recently developed spatial panel data generalized moments (GM) estimators. We find that third-country effects are significant, lending support to the existence of various modes of complex FDI.

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We argue that the proper specification of a panel gravity model should include main (exporter, importer, and time) as well as time invariant exporter-by-importer (bilateral) interaction effects. In a panel of 11 APEC countries, the latter are highly significant and account for the largest part of variation. Copyright Springer-Verlag Berlin Heidelberg 2003Key words: Gravity equation; Panel econometrics, JEL classification: C33; F14; F15,

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper analyzes the effects of distance as a common determinant of exports and FDI in a three factors New Trade Theory model assuming that distance affects both pure trade costs and plant set-up costs. Exports and FDI are not necessarily substitutes with respect to distance since the predicted sign depends on its importance for fixed plant set-up costs relative to transportation costs. For the empirical specification, we suggest that the impact of of time-invariant variables such as distance is most appropriately analyzed in a Hausman-Taylor SUR model. In our application, outward FDI is negatively affected by distance while its effect on exports is insignificant. Exports and outward FDI are complementary with respect to the time-invariant unobserved factors and also with respect to the majority of the exogenous observed determinants. Terms of use: Documents in EconStor may

The recent general equilibrium theory of trade and multinationals emphasizes the importance of third countries and the complex integration strategies of multinationals. Little has been done to test this theory empirically. This paper attempts to rectify this situation by considering not only bilateral determinants, but also spatially weighted third-country determinants of foreign direct investment (FDI). Since the dependency among host markets is particularly related to multinationals' trade between them, we use trade costs (distances) as spatial weights. Using panel data on U.S. industries and host countries observed over the 1989-1999 period, we estimate a "complex FDI" version of the knowledge-capital model of U.S. outward FDI by various recently developed spatial panel data generalized moments (GM) estimators. We find that third-country effects are significant, lending support to the existence of various modes of complex FDI.

Summary  This paper studies the random effects model and the fixed effects model for spatial panel data. The model includes a Cliff and Ord type spatial lag of the dependent variable as well as a spatially lagged one‐way error component structure, accounting for both heterogeneity and spatial correlation across units. We discuss instrumental variable estimation under both the fixed and the random effects specifications and propose a spatial Hausman test which compares these two models accounting for spatial autocorrelation in the disturbances. We derive the large sample properties of our estimation procedures and show that the test statistic is asymptotically chi‐square distributed. A small Monte Carlo study demonstrates that this test works well even in small panels.

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This paper proposes a generalized panel data model with random effects and first-order spatially autocorrelated residuals that encompasses two previously suggested specifications. The first one is described in Anselin's (1988) book and the second one by Kapoor, Kelejian, and Prucha (2007). Our encompassing specification allows us to test for these models as restricted specifications. In particular, we derive three LM and LR tests that restrict our generalized model to obtain (i) the Anselin model, (ii) the Kapoor, Kelejian, and Prucha model, and (iii) the simple random effects model that ignores the spatial correlation in the residuals. For two of these three tests, we obtain closed form solutions and we derive their large sample distributions. Our Monte Carlo results show that the suggested tests are powerful in testing for these restricted specifications even in small and medium sized samples.