vary across countries—world growth and world FDI—it is not appropriate to include year fixed effects in the model. This approach also implies a certain degree of imprecision in our results, since any year-specific shocks will be to some extent reflected in the coefficient estimates of our global variables. On the other hand, rather than sweeping away all country-invariant time series heterogeneity into the year fixed effects, this approach allows us to identify the impact of certain global shocks on individual country FDI inflows.

Based on the discussion above, we begin by estimating equation (1) with country fixed effects, which is also the approach of Lederman et al. (2005). The estimated coefficients, excluding the constant and individual fixed effects, are shown in Table 3. This specification appears to do a good job of explaining the variation in the dependent variable: the overall _R_<sup>2</sup> is high,<sup>36</sup> the _F_ statistic is significant at the 1 percent level, and a Wald test ( _F_ (141 _,_ 2410) = 15 _._ 03 _, p_ = 0 _._ 00) shows that the fixed effects are significant as a group. Furthermore, the coefficients on all of the model’s control variables are significantly different from zero at the 5 percent level or higher, are correctly signed in accordance with our expectations, and imply reasonable elasticity values. Due to the differences in data sources, number of countries considered, and the vector of independent variables, it is not possible to make exact quantitative comparisons of coefficient values of our model with the estimates of Lederman et al. (2005). However, our results are also qualitatively different from theirs: for example, both the GDP level and inflation are highly significant. The former is consistent with a wide majority of FDI studies mentioned above, and the latter appears to confirm our earlier argument with respect to macroeconomic instability. With respect to the global variables, Lederman et al. (2005) estimate that world FDI has a much larger impact than world growth, while we find the opposite. Perhaps the most important difference, however, rests not with the model’s control variables but with the two variables measuring preferential liberalization— _PTAGDP_ and _PTAEXP_ . Neither of these variables is significantly different from zero, while Lederman et al. (2005) find them significant at 5 and 1 percent levels, respectively.

The results in Table 3 imply that we cannot establish a statistically significant link between preferential liberalization and net FDI inflows using the specification of equation (1). One possibility is that such a relationship does not exist once we broaden our scope to most of the countries and PTAs in the world. However, it also possible that the relationship is present but the model in equation (1) is not capable of correctly capturing it due to several potential problems, including model misspecification and the use of inappropriate estimation methods. In the following discussion, we will identify some of these issues and consider how they might be addressed. This will set the stage for the next section, which will present an augmented model that takes account of these problems and becomes the baseline specification for this paper. The issues we will focus on include crosspanel heteroscedasticity and within-panel serial correlation, omitted variables, multicollinearity, and endogeneity. We address each of these in turn below.

_Heteroscedasticity and autocorrelation_ . Empirical tests reveal that the estimates in Table 3 do not account for several important patterns in the model residuals. First, a Breusch-Pagan

> the model, since the magnitude of the fixed effect can be scaled in either direction.

> If we implement the fixed effects methodology using the within estimator, the outcome is essentially the same. Recall that in order to avoid the loss in degrees of freedom from specifying a large number of dummies, the within estimator relies on a simple transformation of the dependent variable ( _yit − y_  ̄ _i_ ). When the same procedure is applied to the right hand side of the estimated equation, all time-invariant variables disappear, since _zit − z_  ̄ _i_ = _zi − zi_ = 0 (see chapter 2 of Baltagi (2001) for more details). The same reasoning applies for estimating models with country-invariant variables under time fixed effects, and panels with more than one set of fixed effects.

> 36Although the within _R_ 2, arguably a better measure of model performance under the fixed effects approach, is much more modest at 37.6 precent.

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