B) RENTA PRESUNTIVA FINAL:
2. COSTO DE LOS ACTIVOS FIJOS.
In the same way to the impulse response functions, to identify the {∈y1t} and {∈y 2t}
sequences, matrix A1 must have some restrictions, this is done with the Cholesky
decomposition. To calculate the variance decomposition we used the same restricted system described in Section 5.5 (a VAR with 8 lags).
In the following three tables, we present the results of the variance decomposition for a horizon of 35 periods, only eight periods are reported (1, 5, 10,15, 20, 25, 30 and 35) since there is not a strong variation in the percentage distribution of each variable. From the estimations, it is evident that each variable explains most of its own forecast error variance, especially during the first periods. Table 5.16 shows the proportions of the forecast error variance of DLGDP explained by each variable. For example, in the first period the total variance was 0.00054, 82.7% of it was explained by a shock in its own innovation, 16.6% by a shock in DLFDI and 0.57% by a shock in DLEX.
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Table 5.16 Variance decomposition of an error variance in DLGDP
Variance Decomposition (%)
Period Variance SE DLGDP DLFDI DLEX
1 0.00054 0.0232 82.75 16.68 0.57 5 0.00061 0.0248 36.79 12.57 50.64 10 0.00071 0.0266 32.09 13.17 54.74 15 0.00075 0.0275 30.05 14.84 55.11 20 0.00077 0.0278 29.30 14.55 56.14 25 0.00078 0.0280 29.01 14.53 56.46 30 0.00079 0.0281 28.70 14.45 56.86 35 0.00079 0.0282 28.60 14.41 56.99
Cholesky Ordering: DLGDP, DLFDI, DLEX.
This trend does not persist for long though. After the first period, the variance of DLGDP is explained mainly by a shock occurring in DLEX, which also tends to grow over time. On the other hand, GDP’s explanation of its variance drops to 36.8%, while DFDI suffers no drastic reduction. These findings support the results previously shown about the strong relationship that exists between GDP and exports.
As would be expected, the case of DLFDI supports the previous findings about the negligible influence from DLEX and DLGDP to explain FDI’s variance. Around 98% of its forecast error variance is due to a shock in its own innovation and at a lesser extent, exports (see table 5.17). These proportions remain almost the same for the whole horizon.
Table 5.17 Variance decomposition of an error variance in DLFDI.
Variance Decomposition of DLFDI (%)
Period Variance SE DLGDP DLFDI DLEX
1 0.03237 0.1799 0.00 99.83 0.17 5 0.02026 0.1423 0.00 99.85 0.15 10 0.02080 0.1442 0.01 98.28 1.71 15 0.02169 0.1473 0.01 98.21 1.78 20 0.02187 0.1479 0.01 98.20 1.79 25 0.02191 0.1480 0.01 98.19 1.80 30 0.02192 0.1480 0.01 98.18 1.81 35 0.02192 0.1480 0.01 98.18 1.81
In the same way, we can see in Table 5.18 that DLEX explains a large percentage of its own forecast error variance. Although at the beginning, DLEX explains 100% of its error variance eventually, DLFDI explains a reasonable 20% of this variation. On the other hand, DLGDP explains less than 0.4%.
Table 5.18 Variance Decomposition of an error variance in DLEX.
Variance Decomposition (%)
Period Variance SE DLGDP DLFDI DLEX
1 0.01043 0.1021 0.00 0.00 100.00 5 0.00724 0.0851 0.30 20.81 78.89 10 0.00854 0.0924 0.29 19.31 80.41 15 0.00896 0.0947 0.33 20.11 79.55 20 0.00931 0.0965 0.33 19.80 79.87 25 0.00951 0.0975 0.35 19.45 80.20 30 0.00958 0.0979 0.35 19.43 80.22 35 0.00961 0.0980 0.35 19.37 80.27
Cholesky Ordering: DLGDP, DLFDI, DLEX
The following figures show graphically the variance decomposition for a horizon of 35 periods. The vertical axis represents the percentage explanation of the forecast error variance of DLGDP, DLFDI and DLEX.
Figure 5.11 0 10 20 30 40 50 60 70 80 90 5 10 15 20 25 30 35 DLGDP DLFDI DLEX Variance decomposition of DLGDP
Figure 5.12 0 20 40 60 80 100 5 10 15 20 25 30 35 DLGDP DLFDI DLEX
Variance decomposition of DLFDI
Figure 5.13 0 20 40 60 80 100 5 10 15 20 25 30 35 DLGDP DLFDI DLEX
Variance decomposition of DLEX
5.7 Conclusions
The purpose of this chapter was to investigate (using a multivariate framework) if exports and FDI have been crucial elements to explain economic growth in Mexico. Granger causality tests showed that the explanation of output changes improves with the inclusion of past changes in exports. In this context, it could be taken as empirical evidence that the export-led growth paradigm applies to the Mexican case. Trade liberalisation through its positive effect on exports has improved economic growth.
On the contrary, no Granger causality was found from GDP and FDI to exports. A possible reason could be the limitation in the number of variables in the system, it might be necessary to explain the performance of exports growth in terms of other variables besides FDI and GDP so we can capture indirect effects and improve the estimates. A different result emerged when we considered the influence of NAFTA on the relationship between the variables. This trade agreement brought immediate and gradual significant reductions in trade tariffs, quotas between Mexico, the US and Canada. It also became a stimulus for foreign capitals to take advantage of low production costs in Mexico and the reduction or elimination of tariffs in North America. Under these considerations, we expected to find a significant difference in the results before and after NAFTA. The estimations confirmed that this was the case. Trade liberalisation appeared to generate a significant effect on export that was transmitted to the economy. On the other hand, the negligible effect of FDI on the other variables seems to be consistent independently of the sample size, whether or not NAFTA occurs, no Granger causality effect was found. A tentative explanation is that especially during the 1980s and 1990s a large proportion of FDI concentrated in low capital intensive activities (i.e. Maquiladoras) that create few spillovers to the economy. However, this could
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also be the result of excluding relevant variables that could capture in a better way the interrelationship between the variables.
It is also important to note that the explanation of changes in FDI improved considerably when NAFTA was considered. When the free trade agreement was accommodated, both exports and GDP improved the explanation of FDI growth in Mexico. This result provides support to the hypothesis that a more open economy attracts more foreign capital. It also shows that it is important the inclusion of control variables that contribute to explain better how one variable affects the others.
Impulse-response functions were estimated to track down the time path of exports, FDI and GDP due to shocks in their own and other variables innovations. These functions are useful tools to understand how policy changes might influence the response and patterns of the variables under scrutiny. In general, the impulse-response functions provide additional evidence to the previous findings with Granger causality tests. For example, the response of DLGDP to a shock in DLEX is more intense and positively signed than its response to a shock in DLFDI. DLGDP’s response was even stronger than its reaction to a shock in its own innovation.
DLFDI responds strongly and positively to shocks in DLEX’s innovations. This result indicates that a policy that promotes exports production and facilitates a positive environment to international trade has the potential to improve FDI growth. Finally, DLEX showed a poor response to shocks in DLGDP, which only confirms in part the previous findings about the negligible influence on this variable.
Regarding variance decomposition of the forecast error, this analysis provided information about the relative importance of each innovation to affect the variables in the system. The results indicate that most of the forecast error variance is explained by the
variables own innovations. The proportion and reduction of their share in explaining the total variance are also an indication on how much a variable responds to shocks from other variables. For example, except for the first period, most of GDP variance is explained (around 55%) by a shock in exports, which again supports the relevant role of exports as an explanatory variable of GDP. At the same time, it was found that 98% of DLFDI’s error variance is explained by its own shock. Finally, around 20% of exports’ variance is explained by a shock in FDI. Although the results from impulse response functions and variance decomposition support the results with Granger causality tests, they should be taken with caution as the standard errors of the estimations remained relatively high despite the restrictions made to the system.