Table 3: <u>Fixed efects estimation results: net FDI</u> inflows

|**Variable**|**Coefci**|**ent**<br>**Std. error**|
|---|---|---|
|GDP|1.209<sup>_∗∗_</sup>|0.094|
|OPEN|0.651<sup>_∗∗_</sup>|0.118|
|GNIREL|-1.904<sup>_∗∗_</sup>|0.472|
|GDPGRO|0.444<sup>_∗_</sup>|0.177|
|CPIGRO|-0.028<sup>_∗∗_</sup>|0.006|
|WLDGRO|-1.235<sup>_∗∗_</sup>|0.467|
|WLDFDI|0.376<sup>_∗∗_</sup>|0.037|
|PTAGDP|0.002|0.004|
|PTAEXP|-0.031|0.061|
|N||2561|
|R<sup>2</sup>||0.645|
|F (150_,_2410)||161.21|
|Signifcance le<br>_Note:_ The dep<br>natural logarit|vels :<br>_†_ 10<br>endent varia<br>hms.|%<br>_∗_5%<br>_∗∗_1%<br>ble is expressed in|

test reveals the presence of cross-panel heteroscedasticity ( _χ_<sup>2</sup> = 239 _._ 38 _, p_ = 0 _._ 00), which, while leaving coefficient estimates unbiased, can significantly influence standard errors and therefore affect hypothesis testing. There are a number of statistical techniques that can address this problem (e.g. weighted least squares), but their applicability and implementation are less clear in a panel context. Therefore, most panel studies rely on the Huber-White sandwich method to calculate robust standard errors, which represent consistent estimates of the variance-covariance matrix in the presence of heteroscedasticity. In the context of estimates in Table 3, however, it turns out that heteroscedasticity is not a significant problem since robust estimation does not qualitatively change the results—even though the standard errors increase by as much as 100 percent in some cases, it is not enough to push the significance level of any of the local or global coefficients below the 5 percent threshold.<sup>37</sup> In addition to heteroscedasticity, the estimates in Table 3 are also affected by serial correlation. In particular, a Wooldridge test for autocorrelation in panel data rejects the null of no first order serial correlation ( _F_ (1 _,_ 138) = 103 _._ 032 _, p_ = 0 _._ 00). The consequences of autocorrelation are similar to heteroscedasticity, but the problems caused by the latter are usually more severe. OLS coefficient estimates remain consistent and unbiased in the presence of autocorrelation, but they are no longer best linear unbiased estimators (BLUE) or asymptotically efficient. Furthermore, autocorrelation causes standard errors to be biased. Corrections for autocorrelation range from OLS estimation with Newey-West standard errors to Cochrane-Orcutt or Prais-Winsten transformations, which generalize to two-step feasible generalized least squares (FGLS) in panel data. However, the latter become incomparable with the more simple OLS-based approaches, both because coefficient signs and magnitudes can change quite drastically and because the properties of the FGLS estimators are only known asymptotically (i.e. hypothesis testing is based on the _z_ rather than _t_ statistics). In addition, if both heteroscedasticity and autocorrelation are present in the data, a three-step FGLS approach is required, further complicating comparisons with OLS/LSDV-type models.<sup>38</sup>

> 37See Table 11 in Appendix B for estimates of equation (1) using the Huber-White robust standard errors.

> 38To illustrate this, Table 11 in Appendix B presents three-step FGLS estimates of equation (1). The results

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