Factores afectivos en el aprendizaje de los futuros traductores

The estimation method is selected to fulfill the aims and objectives outlined in section 3.1 and to enable us to empirically test if the theoretical expectations derived in the previous section hold for the Macedonian banking sector. We have also taken into account the specific nature of the cross-sectional units and the data series used. Unit root tests have been conducted (Augmented Dickey-Fuller (ADF) on the data series used in model 3.1, utilising the Akaike (AIC) and Schwarz (SIC) lag length selection criteria, Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) presented in appendix 3.3. Regarding the interaction terms that we use in model 3.1 (see appendix 3.3.a), the ADF and PP unit root tests reject the null hypothesis of a unit root for all variables that enter in model 3.1 in nearly all cases at the 1% level. In all other cases but two, there is rejection at the 5% level. For the variables dmbksmatmisub3 and dmbksrellenging3 there is rejection at the only 10% level in the ADF tests, but rejection is at the 1% level in the PP test for these variables. The KPSS tests do not give sufficient evidence to reject the null hypothesis of no unit root at 10% level. Regarding the unit root tests for the first differences of the interest rate series, i.e. banks‟ lending rates and money market rate (see appendix 3.3b), we have also conducted the ADF, PP and KPSS tests. In selecting the lag length for the ADF test, we chose the number of lags by the information criteria (AIC and

Variable: Expected Sign Variable: Expected Sign

'Cost of funds' rate (dmbks) + Operational efficiency * MBKS (dmbksoperef) -

Bank size * MBKS (dmbkslassets) + / - Portfolio diversification * MBKS (dmbksportdiv) -

Liquidity * MBKS (dmbksliquidity) - Inflation * MBKS (dmbksinfl) + Capital * MBKS (dmbkscapital) - Economic growth * MBKS (dmbksipi) + / − NPL ratio * MBKS (dmbksNPLratio) + / - HHI * MBKS (dmbkslhhi) - /+ Maturity-mismatch * MBKS (dmbksmatmisub) +

Relationship lending * MBKS (dmbksrellending) -

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135 SIC) that indicated less lags due to the relatively short time span of the data. In the majority of the cases we have selected the number of lags indicated by the AIC criteria because it pointed to less lags compared to the SIC criteria. For only two interest rate series (dlendrateden16 and dlendrateden27), do AIC and SIC suggest the same number of lags. Hence, the number of lags indicated by the information criteria ranges from four lags for the money market rate (dmbks) up to ten lags for the lending rate for bank 5 (dlendrateden5) from a total of ninety-five available observations for each interest rate series. The ADF test results suggest the rejection of the null hypothesis of a unit root at 5% for all interest rate series, whereas the PP test indicates to rejection of the null hypothesis of a unit root at 1% level. The KPSS test does not give sufficient evidence to reject the null hypothesis of no unit root at the 10% level for all interest rate series. These test results suggest that all variables used in model 3.1 can be treated as stationary. Moreover, given the requirement for the error term to be white noise in the ADF test for the first differences of the interest rate series, we additionally check the diagnostic statistics (see appendix 3.3.b), in particular that for serial correlation. Namely, we have checked the diagnostic tests of the ADF such as Breusch-Pagan- Godfrey test for autocorrelation, Ramsey RESET for the functional form and Koenker-Bassett test for heteroscedasticity of the first differences of the interest rate series (see appendix 3.3.b). The diagnostic tests conducted give non-rejection of the null hypotheses of no autocorrelation, correct functional form and homoskedasticity at the 10% level (see appendix 3.3.b). Thus it is reasonable to proceed on the basis that the errors are white noise. Consequently, given the aims and objectives of this chapter, we needed to select a method that is able to estimate the determinants of banks‟ short-run lending rate adjustment to changes in the „cost of funds‟ rate. We also had to select a method that enables different slope coefficient estimates for each cross-sectional unit that will allow us to test if those coefficients statistically differ between the units. We aim to test for this since the existing literature does not currently provide a clear answer on this issue and because, as mentioned in section 3.2, we have some arguments on a priori

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136 Regarding the specific nature of the data and the cross-sectional units (banks), we have considered the number of observations for each bank, the time span of the data as well as their interrelatedness that may cause contemporaneous cross-sectional dependence among the disturbances. Banks in Macedonia are interrelated because they borrow between each other in the same money market and the same macroeconomic and financial factors and the same regulatory requirements affect all of the banks. Although we include variables, such as inflation and the industrial production index to account for some of these factors, these are not expected to fully do so. In selecting the estimation method we were also bounded by the limitations of the data such as, with relatively small cross- sectional sample (only 18 banks), methodological changes in data collection giving a relatively short-time span of 8 years and the limited interest rate series available.

Various estimation techniques were critically assessed in section 2.3, such as the two-stage and one-stage estimation methods that estimate the determinants of size and speed of adjustment coefficients (see section 2.3.1), and the single equation approach based on static panel data models estimated with FE, RE and/or GLS (see sections 2.3.2 and 2.3.3).

Some weaknesses and limitations were identified in the two-stage estimation methods and thus this option is not pursued. More precisely, regarding the first stage of the estimation process, i.e. when the size and speed of pass- through coefficients are estimated with time series methods for each cross- sectional unit separately or by panel methods, we identified difficulties in applying these methods. For example, majority of the studies in the first stage of the estimation process attempt to estimate the size of the pass-through by employing an ECM, but do not conduct unit root test(s) for the stationarity of the variables employed and the residuals from the long-run relationship in order to investigate if the variables are cointegrated. They only assume that there may exist a cointegrating relationship (see sections 2.3.1 and 2.3.5), although the theory is not clear on this (see section 2.2.5). Moreover, the majority of the empirical studies that have conducted a unit root tests for the stationarity of the

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137 residuals from the long-run relationship failed to find a cointegrating relationship. Hence, they proceed to estimate the pass-through coefficients by using first differences of the variables (Sander and Kleimer, 2004a, b; de Graeve et al., 2004 and Egert et al., 2007). Nonetheless, regardless whether the pass-through coefficients are estimated with or without an ECM for each cross-sectional unit separately, the results in majority of those studies may be inefficient because of the contemporaneous cross-sectional correlation between the units, which is expected in this case, and is not controlled for. Moreover, regarding our model specification, another technical reason that may complicate the possibility of employing an ECM is that we have interaction terms composed of two continuous variables. The time-series cointegration methods are not designed for inclusion of interaction terms of two continuous variables due to statistical reasons. More precisely by multiplying two I(1) variables, I(1) and I(0) or I(2) and I(1) or I(0); what will be the order of integration of the newly constructed interaction term is unclear. Due to the all of the afore-mentioned reasons in this paragraph we preclude the possibility of using cointegration and ECM methods.

Regarding the second stage of the estimation process, things may become even more „complicated‟. Usually in the empirical literature (as considered in section 2.3.1), the procedure is to regress the estimated pass-through coefficients on a set of structural variables in a cross-sectional regression (Cottarelli and Kourelis, 1994; Sander and Kleimeier, 2004a, b; Sorensen and Werner, 2006 and de Graeve et al., 2004) or in a panel data model by dividing the sample into separate time-periods, i.e. sub-periods of 3 to 5 years (Mojon, 2000). In order to conduct the second stage of the estimation process with both, cross-sectional regression and panel data models, the authors use the average values of the independent structural variables over the years. By averaging the values of the variables over the whole or part of the time span, the fluctuations in the variables are reduced and hence, the time dimension of the data is omitted. In this way the changes that may have occurred during the analysed period may be disregarded. This method may be more appropriate for the developed economies that many of which have had, until recently, a more stable macroeconomic and financial

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138 environment over the last 10 years than transition economies, and thus we argue that this method may be inappropriate for our investigation. Moreover, even if this method is deemed suitable, it is still not applicable in our case because we have a small cross-sectional sample of only 18 units. Some authors such as Sorensen and Werner (2006) have attempted to solve this problem by applying “….. a wild- bootstrap method for the computation of p-values….” (p.26), but applying this method on a cross-sectional sample of only 18 observations would seem problematic.

One stage estimation methods based on dynamic panel data models that use either “difference” GMM estimator or panel ECM method are also problematic to use here. Dynamic panel data models that use the “difference” and/or “system” GMM estimator are ruled out in our case, given data limitations. Namely, as explained in section 5.4, they are designed for “large N and small T” samples. This assumption is not satisfied with our data because T is substantially greater than N (see section 3.4), and if we had proceeded with this estimation method, we expect to have the problem of the creation of „too many‟ instruments and therefore, a low power of the diagnostic tests (see sections 5.4 and 5.5). Carrying out the same procedure as in chapter 5, i.e. using annual data in order to reduce the number of T observations, would mean in this case that the time variation in the data could not be investigated appropriately. Namely, the main area of interest here is to model the time variations in interest rate series and not the adjustment in stocks, as is the case with the model in chapter 5. Regarding the use of a panel ECM method, we also reject it because it requires an even larger cross-sectional sample than the GMM estimator. The authors that use these methods (see section 2.3.1) first group the banks according to their specific characteristics and then, for each group of banks, they estimate the pass-through coefficients and thus, compare the differences between the estimates. With a cross-sectional sample of 18 banks we argue that this option is not feasible. Another reason for precluding the possibility of using panel ECM and panel cointegration methods is that they do not control for the cross-sectional correlation among the units. Namely, panel cointegration tests “…..are based on

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139 the assumption that there is no correlation and no cointegration between the sections.” (Sorensen and Werner, 2006, p.18). Moreover, in using panel cointegration method, as discussed previously, we have interaction terms composed of two continuous variables that may additionally complicate the whole estimation process.

Regarding the single equation approach based on static panel data models estimated with FE, RE and/or GLS, we argue that there are problems in using these estimators because they provide average estimates for the level of interest rates set by banks and their size of adjustment to changes in the „cost of funds‟ rate. Additionally, these models assume equal slope coefficients for each cross- sectional unit on a priori grounds and do not allow for the possibility of testing if the slope coefficients statistically differ among the units, which we have argued should be examined with our model. Moreover, FE and RE estimators by de-fault, do not control for the possible cross-sectional contemporaneous correlation among the disturbances, unless you additionally do take care of it.

Thus, given to the assessment of the applicability of the various estimation methods, the specific nature of the data series and the possible phenomenon of contemporaneous correlation among the banks, we have selected Zellner‟s (1962 and 1963) Seemingly Unrelated Regression (SUR) model. The rationale for selecting this model is based upon several reasons. Firstly, in the case when there is contemporaneous correlation among the disturbances that are by nature heteroskedastic, then the SUR model based on a Feasible Generalised Least Squares (FGLS) estimator provides more efficient estimates compared to OLS, by using the information of the variance-covariance matrix of the error terms. Thus, when the correlation among the error terms of each cross-sectional unit is higher, then FGLS estimator is able to use more information from the variance- covariance matrix of the error terms and hence, the efficiency gain by employing the SUR model will be higher (Baum 2006, Greene 2008 and Vogelvang 2005).

Secondly, it is designed for samples with large time dimension (T) and small or

finite cross sectional dimension (N) where one of the major requirements is T to be substantially greater than N, which is the case with our data (T=96; N=15).

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140

Thirdly, it may estimate different slope coefficients for each cross-sectional unit that allows testing for their cross-sectional equality. This then enables us to investigate whether there is a heterogeneous size of adjustment among banks in Macedonia and what the major determinants are. This option will actually enable us to test if the slope coefficients statistically differ among the cross-sectional units. Fourthly, as another advantage of employing the SUR model is that in the case when the repeated iterations in calculating the coefficients and their variances for each cross-sectional unit converge, then the FGLS estimator becomes equal to the Maximum Likelihood Estimator (MLE). This may provide some additional efficiency gains, under the condition that the normality assumption about error terms is fulfilled (Greene, 2008 and Moon and Perron, 2006). However, as discussed in Greene (2008), whether MLE provides some efficiency gains in small samples is uncertain.

The general form of the SUR model can be presented with the following system of equations: Y1t = β’1x1t + u1t Y2t = β’2x2t + u2t . . Ynt = β’nxnt + unt (3.3)

Where: Y is the dependent variable, β’ is a vector of coefficients; X is a matrix of independent variables, u are the error terms and n and t are cross-sectional and time specific subscripts.

The above equations can be stacked as a system and can be presented more compactly as follows:

Yt = Xt’β+ ut (3.4)

Where: Yt is TN x 1 matrix of dependent variables; x’ is a TN x K matrix of independent variables; β is a K x 1 matrix of coefficients; u is TN x 1 matrix of error terms; T and N are the number of time and cross-sectional observations, respectively and K is the number of independent variables.

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141 Nonetheless, the SUR model has some limitations and requires certain assumptions to be fulfilled. The main assumptions are the exogeneity of the regressors and a normal distribution of the residuals; the latter assumption is mainly for MLE but not for FGLS. As Zellner (1963) argues: “….even when normality is not present, the estimation procedure is applicable and will yield consistent coefficient estimators which are asymptotically normally distributed.” (p.988). In respect of the exogeneity assumption, the strongest form is the strict exogeneity assumption where all regressors from each equation are uncorrelated with the respective equation‟s error terms for all time periods:

E = (ut | x1, x2, x3, ……, xt) = 0 (3.5)

However, Wooldridge (2002) argues that this assumption may be relaxed by assuming a contemporaneous exogeneity, i.e. no correlation between the regressors and the error terms in the same time period, presented below:

E = (ut | xt) = 0 (3.6)

The major limitation of the SUR model is that it does not properly deal with non-stationary variables because cointegration methods are not developed within the framework, while dynamic specifications are still in the process of development. Another limitation is that if any of the system equations is miss- specified, then all coefficients in each equation will be inconsistently estimated. Therefore, for a consistency check, it is argued that the results should be compared with the ones estimated with the OLS conducted on equation-by- equation basis (Moon and Perron 2006).

Regarding the issue whether the SUR model provides an appropriate estimator for dynamic models, we have considered if the recent work in the field of dynamic SUR is applicable in our case. More precisely, we have assessed the estimation method applied in Sorensen and Werner (2006) who employ Dynamic SUR (DSUR) for estimating the long-run relationship among the variables and the ECM for SUR (SURECM) for estimating the short-run dynamics. The recently

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142 developed DSUR method by Mark et al. (2005) and Moon and Perron (2005) is based on the dynamic ordinary least squares (DOLS) method by Stock and Watson (1993) that controls for the possible endogeneity of the regressors. Namely, “System DOLS is distinguished from ordinary DOLS in that endogeneity in equation i is corrected by introducing leads and lags of the first difference not only of the regressors of equation i but also of the regressors from all other equations in the system.” (Mark et al., 2005, p.798). However, one of the weaknesses of the DSUR method is that it is designed for samples that have substantially larger T than N, or as it is discussed in Mark et al. (2005) it is works well for data series with T larger than 100 and N smaller than 8. Estimating DSUR on a system with greater N than 8 will absorb too many degrees of freedom due to the large number of leads and lags that have to be included in each equation, which suggests that this method is not applicable given our data set. Another weakness of this method is that it does not test for a co-integrating relationship among the variables, but cointegration is assumed according to the empirically tested theory. For instance, Moon and Perron (2005) empirically test the purchasing power parity (PPP) theory, by assuming that the PPP theory in the long-run holds and thus, assume that the residuals from the DSUR model are stationary and variables cointegrated. Regarding the interest rate pass-through, the