LA PINTURA TABULAR EN EL ECUADOR

Before proceeding to the regression analysis and discussion of the results, it is instructive to perform diagnostic checks to ensure that the data meet all necessary regression assumptions and conditions. Failure to do this may lead to misleading results. Thus, we conducted/considered a number of regression diagnostics for our data before using them in formal model estimation. These diagnostics involved the following. First, we checked for outliers and removed all outlying observations.

Second, we checked for normality to ensure that data for all variables included in

the model are normally distributed. As a result of this, we performed some necessary transformations of some variables. This involved mainly transforming data for some variables into natural logarithms. Descriptive statistics of data after

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removing outliers and performing necessary transformations are summarised in Table 6.1 below.

Table 6.1: Descriptive statistics

Variable Observations Mean Standard Deviation

Minimum Maximum

GDP growth (GDP) 184 4.29 2.76 -3.34 11.40

Population growth (POP) 186 2.22 0.696 0.19 3.82

Degree of openness (OPEN) 183 4.018 0.52 2.37 5.35

Rate of Inflation (INFL) 179 2.39 0.85 -0.59 4.57

Total investment (INV) 181 3.05 0.28 2.26 3.86

Money Supply (M2) 182 3.48 0.47 2.23 4.52

Total Fiscal Deficit (DFCT) 183 3.48 3.58 -4.40 14.32

Domestic Financing (DFIN) 172 2.42 2.94 -3.13 11.23

External Financing (EXTFIN) 172 1.42 1.88 -2.24 7.56

Total Government expenditure (GOVEXP) 181 3.13 0.37 2.09 3.91

Government Current expenditure (CURREXP) 176 2.89 0.39 1.91 3.60

Government Capital Expenditure (CAPEXP) 175 1.49 0.67 -0.29 2.88

Expenditure on Public Service (PSEXP) 164 0.84 0.75 -0.67 2.44

Defence expenditure (DEFEXP) 147 .63 .78 -1.52 2.18

Spending on Education (EDUEXP) 164 1.15 0.65 -1.40 2.16

Spending on Health (HLTHEXP) 164 0.23 .74 -1.49 1.61

Spending on Economic Services (ECONEXP) 163 1.51 0.62 -0.11 2.82

Total tax Revenue (TAX) 182 16.37 5.77 5.02 32.61

Grants (GRANTS) 139 -.976 1.98 -7.33 3.34

Source: Author’s calculations based on data from World Development Indicators CD-ROM and IMF’s Government Finance Statistics CD-ROM and Yearbooks (Various issues)

Note: - All figures are calculated based on five-year averages

- All the variables are in natural logarithm except GDP, GDP-1, DFCT, DFIN, EXTFIN and TAX

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Third, we checked that multicollinearity is not existent in our data. It should be

noted that when there is a perfect or near perfect linear relationship between two or more explanatory variables, the estimates for a regression model cannot be uniquely calculated (Hsiao, 2003; Baltagi, 2008; Gujarati, 2009; Asterious and Hall, 2011 – among others). On this basis, therefore, we checked for multicollinearity using the correlation matrix presented in Table 6.2 below. Generally, most of the respective pairwise correlations are reasonably low to suggest that the problem of multicollinearity is not worrisome in our data. Another useful form of information we get from the correlation matrix below is that correlations between GDP growth and most of the explanatory variables are not very strong. This suggests that the relationship between GDP growth and most of the right-hand side variables in our model might not be strongly significant. In addition to correlation analysis, we estimated variance inflation factors (VIFs) in each of the regression models we performed. Results of VIFs, which are reported later in the discussion, also suggest that multicollinearity, although existent, is not a serious problem.

In addition to performing the above-discussed diagnostic tests, it is important to note that we are aware of the recent developments in the literature in relation to diagnostic testing for unit roots (nonstationarity) in panel data studies, see Maddala and Wu (1999), Phillips and Moon (2000), Choi (2001), Levin et al. (2002), Hsiao,

2003, Baltagi, 2008 – among others. Following these developments, some empirical studies employing panel data have attempted to test for unit roots, depending on the time series structure of the panel data and econometric methods used in these studies (Phillips and Moon, 2000; Bond et al., 2001).

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Table 6.2: Correlation Matrix

Variables GDP GDP-1 POP OPEN INFL INV M2 DFCT DFIN EXTFIN GOVEXP CURREXP CAPEXP PSEXP DEFEXP EDUEXP HLTHEXP ECONEXP TAXEXP GRANTS

GDP 1.00 GDP-1 0.14 1.00 POP -0.09 -0.05 1.00 OPEN -0.02 -0.01 -0.30 1.00 INFL -0.13 -0.21 0.21 -0.21 1.00 INV 0.47 0.33 -0.30 0.30 -0.24 1.00 M2 0.26 0.32 -0.37 0.25 -0.50 0.41 1.00 DFCT -0.04 0.05 0.09 -0.10 0.08 -0.01 0.10 1.00 DFIN 0.02 0.08 0.07 -0.22 -0.05 -0.10 0.31 0.81 1.00 EXTFIN -0.13 0.01 -0.00 0.13 0.23 0.25 -0.25 0.47 0.04 1.00 GOVEXP -0.10 -0.08 -0.11 0.50 -0.05 0.18 0.34 0.45 0.32 0.42 1.00 CURREXP -0.14 -0.16 -0.03 0.42 -0.09 0.02 0.33 0.42 0.36 0.30 0.92 1.00 CAPEXP 0.13 0.18 -0.22 0.39 0.05 0.55 0.23 0.27 0.01 0.55 0.58 0.28 1.00 PSEXP -0.10 -0.22 0.09 0.45 0.05 -0.02 -0.26 0.15 -0.16 0.44 0.52 0.49 0.25 1.00 DEFEXP 0.08 0.07 0.27 -0.26 0.02 0.06 0.05 0.38 0.38 0.21 0.33 0.35 0.11 -0.05 1.00 EDUEXP -0.18 -0.07 0.05 0.72 -0.06 0.07 0.16 0.05 -0.09 0.22 0.70 0.68 0.38 0.49 0.10 1.00 HLTHEXP -0.29 -0.26 -0.12 0.75 -0.04 0.03 0.04 0.05 -0.15 0.29 0.62 0.60 0.31 0.53 -0.14 0.83 1.00 ECONEXP 0.05 0.10 -0.04 0.35 -0.18 0.46 0.15 0.33 0.14 0.51 0.65 0.46 0.72 0.40 0.21 0.42 0.43 1.00 TAX -0.03 -0.09 -0.17 0.56 0.01 0.20 0.27 0.22 0.06 0.40 0.85 0.81 0.49 0.53 0.18 0.75 0.71 0.53 1.00 GRANTS -0.02 -0.25 -0.21 0.25 0.13 0.03 -0.02 0.19 0.10 0.24 0.52 0.51 0.24 0.29 0.15 0.31 0.32 0.35 0.40 1.00 Source: Author’s calculations based on data from World Development Indicators CD-ROM and IMF’s Government Finance Statistics CD-ROM and Yearbooks (Various issues)

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In relation to our study, note that the use of five-year average data and Arellano and Bond (1991) GMM estimator that first-differences the data, helps to deal with any potential problems of nonstationarity in the data. Besides, as Baltagi (2008) argues, using panel data can help to avoid the problem of spurious regression, which can be caused by nonstationarity, among other factors. In the words of Baltagi (2008):

“Unlike the single time-series spurious regression literature, the panel data spurious regression estimates give a consistent estimate of the true value of the parameter as both N and T tend to. This is because the panel estimator averages across individuals and the information in the independent cross-section data in panel leads to a stronger overall signal than the pure time-series case” (Baltagi, 2008: pp. 273-274).

According to Baltagi (2008), therefore, one does not need to worry about nonstationarity of the variables in a panel-data setting, as the regression estimates will give consistent results. Phillips and Moon (2000) seem to support Baltagi (2008) by showing that in a panel-data analysis the regression stops being spurious and consistently estimates what is actually there – i.e., if there is a relation, it will estimate the relation, and on the other hand, if there is no relation, it will estimate zero.

On the basis of the above discussion, therefore, we argue that the use of five-year average data, the first-differenced Arellano and Bond estimator, and dynamic panel data, all helps to deal with any potential problems of nonstationarity of variables, and the problem of spurious regression.

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Having examined our data using the regression diagnostics discussed above, we were satisfied that our data meet all necessary regressions assumptions. Thus, we moved on to perform formal model estimation and discussion of the results as presented in the following sections. Note also that other important diagnostic testing such as tests for autocorrelation are performed as we carry out these formal model estimations.