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Η παρούσα διατριβή ασχολείται με δύο ιδιαιτέρως σημαντικά και διαχρονικά επίκαιρα ζητήματα στην ανάλυση χρονολογικών σειρών, τα οποία εντάσσονται, υπό ευρεία έννοια, στο πεδίο της Μακροοικονομετρίας. Ειδικότερα, μελετώνται θέματα και μεθοδολογίες ή τεχνικές ιδιαίτερα χρήσιμες για εκείνους τους ερευνητές, οι οποίοι επικεντρώνονται στην ανάλυση της συμπεριφοράς των συναθροιστικών (aggregate) μεγεθών της οικονομίας, βασιζόμενοι στη χρήση δεδομένων χρονοσειρών ή πιο απλά χρονοσειρές (time series). Το πρώτο ζήτημα αφορά στη μελέτη της δυναμικής αλληλεξάρτησης ανάμεσα σε μακροοικονομικές μεταβλητές κάτω από την υιοθέτηση ενός πολλαπλού πλαισίου ανάλυσης χρονοσειρών. Το ενδιαφέρον εστιάζεται κυρίως στην γενικευμένη ή εκτεταμένη έννοια της αιτιότητας κατά Granger, δηλαδή στην επέκταση της τυπικής έννοιας της αιτιότητας κατά Granger σε μεγαλύτερους του ενός ή σε πολλαπλούς ορίζοντες πρόβλεψης. Το δεύτερο ζήτημα αφορά στην παρουσία μη-γραμμικών χαρακτηριστικών σε μακροοικονομικές χρονοσειρές, καθώς και την υποδειγματοποίηση της μη-γραμμικότητας με τη χρήση μη-γραμμικών οικονομετρικών μοντέλων. Επικεντρώνεται δε ιδιαίτερα στον έλεγχο μοναδιαίας ρίζας κάτω από την εναλλακτική υπόθεση της στασιμότητας γύρω από μη-γραμμικές τάσεις της μορφής τάσεων ομαλής μετάβασης (smooth transition trends) στις μακροοικονομικές χρονοσειρές. Ουσιαστικά, η διατριβή διακρίνεται σε δύο κεφάλαια. Στο Κεφάλαιο 1 παρουσιάζεται η τυπική έννοια της αιτιότητας κατά Granger, καθώς και η γενικευμένη ή εκτεταμένη έννοια της αιτιότητας ή η αιτιότητα σε πολλαπλούς ορίζοντες (multi-horizon causality), στο πλαίσιο των διανυσματικών αυτοπαλίνδρομων υποδειγμάτων (VAR). Η τυπική έννοια της αιτιότητας κατά Granger περιορίζεται στη βελτίωση της προβλεψιμότητας σε ορίζοντα πρόβλεψης μίας περιόδου (one-step ahead), ενώ λαμβάνει υπ'όψιν μόνο τις άμεσες ροές πληροφόρησης μεταξύ των μεταβλητών ενδιαφέροντος (direct causality). Ωστόσο, σε υποδείγματα VAR με περισσότερες από δύο μεταβλητές η τυπική έννοια της αιτιότητας μπορεί να επεκταθεί με την μελέτη της βελτίωσης της προβλεψιμότητας σε μεγαλύτερους του ενός ορίζοντες πρόβλεψης. Σε μία περίπτωση όπως η τελευταία, πλην της άμεσης αιτιότητας, δύνανται να μελετηθούν και οι έμμεσες σχέσεις αιτιότητας (indirect causality) που ενδέχεται να προκύψουν μέσω των πρόσθετων μεταβλητών του συστήματος. Το θεωρητικό πλαίσιο της γενικευμένης έννοιας της αιτιότητας που παρουσιάζει η παρούσα διατριβή έχει αναπτυχθεί από τους Dufour and Renault (1998). Παράλληλα, δίνεται ιδιαίτερη βαρύτητα σε δύο πρόσφατες μεθόδους στατιστικής επαγωγής αιτιωδών σχέσεων κατά Granger σε πολλαπλούς ορίζοντες, οι οποίες παρέχουν πρόσθετη πληροφόρηση σχετικά με τη δυναμική αλληλεξάρτηση οικονομικών χρονοσειρών, και πιο συγκεκριμένα σχετικά με τον άμεσο ή έμμεσο χαρακτήρα των αιτιωδών σχέσεων, το διαχωρισμό μεταξύ βραχυχρόνιας και μακροχρόνιας (μη)-αιτιότητας, καθώς και τις πιθανές χρονικές υστερήσεις της αιτιότητας. Τέλος, στα πλαίσια του Κεφαλαίου 1, ερευνάται η δυνατότητα εφαρμογής των μεθόδων αυτών μέσω εμπειρικών εφαρμογών πάνω σε δύο διαχρονικά ζητήματα αιτιωδών σχέσεων ανάμεσα σε οικονομικές μεταβλητές. Στο Κεφάλαιο 2 παρουσιάζονται υποδείγματα ομαλής μετάβασης, καθώς και έλεγχοι μοναδιαίας ρίζας οι οποίοι επιτρέπουν την στασιμότητα γύρω από ομαλές ή βαθμιαίες μεταβάσεις κάτω από την εναλλακτική υπόθεση. Κύριο χαρακτηριστικό των υποδειγμάτων ομαλής μετάβασης είναι η παρουσία μη-γραμμικών τάσεων στη διαχρονική εξέλιξη των χρονοσειρών. Κεντρικό ρόλο στα υποδείγματα αυτά κατέχουν οι διαρθρωτικές μεταβολές (structural changes) στην προσδιοριστική τάση, οι οποίες, δεδομένου ότι αντιπροσωπεύουν μεταβολές της συναθροιστικής συμπεριφοράς, υποδειγματοποιούνται με τη χρήση ενός προσδιοριστικού στοιχείου το οποίο επιτρέπει την βαθμιαία αντί της στιγμιαίας προσαρμογής. Οι έλεγχοι μοναδιαίας ρίζας, οι οποίοι επιτρέπουν περισσότερη ευελιξία στην συνάρτηση της τάσης σε σχέση με την γραμμική εξειδίκευση της προσδιοριστικής τάσης που χρησιμοποιούν οι τυπικοί έλεγχοι μοναδιαίας ρίζας, αποτελούν το επίκεντρο μελέτης του Κεφαλαίου 2 της διατριβής. Η αναγκαιότητα υιοθέτησης πρόσθετων ελέγχων μοναδιαίας ρίζας, όπως οι έλεγχοι μοναδιαίας ρίζας οι οποίοι επιτρέπουν στασιμότητα γύρω από ομαλές μεταβάσεις κάτω από την εναλλακτική υπόθεση, ισχυροποιείται από τα αποτελέσματα της εφαρμογής των ελέγχων αυτών σε ένα σύνολο οικονομικών χρονοσειρών. / This thesis discusses two central research topics in applied time series econometrics that generally belong in the field of Macroeconometrics. In particular, we investigate issues and methods which are of interest to those researchers who want to analyze economic problems or economic aggregates by means of time series data. The first topic deals with the dynamic interrelationships between sets of theory related variables in a multiple time series context. Research interest is primarily focused on the generalized or extended notion of Granger causality, that is the extension of the standard Granger causality concept to higher forecast horizons. The second topic deals with nonlinear behavior of macroeconomic time series, as well as the modelling of nonlinearities in economic time series using nonlinear econometric models. Specific attention is paid to unit root tests that allow stationarity around nonlinear trends in the form of smooth transitions under the alternative. The dissertation consists of two chapters. The first chapter presents the standard concept of Granger causality, along with the generalized or extended notion of causality, also known as multiple-horizon causality, in the vector autoregressive (VAR) framework. The standard notion of Granger causality restricts prediction improvement to a forecast horizon of one period, while it considers only direct flows of information between the variables of interest. However, in VAR models with more than two variables, the concept of standard Granger causality can be extended by studying prediction improvement at forecast horizons greater than one. If this is the case, then, except for direct causality, indirect flows of information might be revealed through the additional variables of the system. The theoretical framework of the extended concept of causality which is presented in the present dissertation has been developed by Dufour and Renault (1998). In addition, special attention is paid to two recent methods for testing hypothesis of non-causality at various horizons which can provide further information on the dynamic interaction of time series, and more specifically on the direct or indirect nature of causal effects, the distinction between short-run and long-run (non)-causality, as wells as the possibility of causal delays. Finally, the potential implementation of these methods is examined through empirical applications on causality relations among different sets of economic variables. Chapter 2 presents smooth transition (STR) trend models, as well as unit root tests that allow stationarity around smooth transitions under the alternative. Smooth transition regression models presume the presence of nonlinear trends in the long-run evolution of time series. A key feature of these models is the presence of structural changes in the deterministic trend which, given that they represent changes in aggregate behavior (economic aggregates), are modelled through a deterministic component that permits gradual rather than instantaneous adjustment between regimes. Unit root tests that permit a more versatile trend function in the unit root procedure, rather than the standard linear trends, are the main concern of Chapter 2. The necessity of employing additional unit root tests, such as unit root tests that allow stationarity around smooth transitions under the alternative, becomes evident through the unit root test results that are observed in an application in a set of economic time series.

Esta tese examina o problema de detecção de conectividade entre séries temporais no sentido de Granger no caso em que a natureza não linear das interações não permite sua determinação por meio de modelos auto-regressivos lineares vetoriais. Mostra-se que é possível realizar esta detecção com auxílio dos chamados métodos de Kernel, que se tornaram populares em aprendizado por máquina (\'machine learning\') já que tais métodos permitem definir formas generalizadas de teste de Granger, coerência parcial direcionada e função de transferência direcionada. Usando simulações, mostram-se alguns exemplos de detecção nos quais fica também evidente que resultados assintóticos deduzidos originalmente para estimadores lineares podem ser generalizados de modo análogo, mostrando-se válidos no presente contexto kernelizado. / This work examines the connectivity detection problem between time series in the Granger sense when the nonlinear nature of interactions determination is impossible via linear vector autoregressive models, but is, nonetheless, feasible with the aid of the so-called Kernel methods that are popular in machine learning. The kernelization approach allows defining generalised versions for Granger tests, partial directed coherence and directed transfer function, which the simulation of some examples shows that the asymptotic detection results originally deducted for linear estimators, can also be employed under kernelization if suitably adapted.

Esta tese examina o problema de detecção de conectividade entre séries temporais no sentido de Granger no caso em que a natureza não linear das interações não permite sua determinação por meio de modelos auto-regressivos lineares vetoriais. Mostra-se que é possível realizar esta detecção com auxílio dos chamados métodos de Kernel, que se tornaram populares em aprendizado por máquina (\'machine learning\') já que tais métodos permitem definir formas generalizadas de teste de Granger, coerência parcial direcionada e função de transferência direcionada. Usando simulações, mostram-se alguns exemplos de detecção nos quais fica também evidente que resultados assintóticos deduzidos originalmente para estimadores lineares podem ser generalizados de modo análogo, mostrando-se válidos no presente contexto kernelizado. / This work examines the connectivity detection problem between time series in the Granger sense when the nonlinear nature of interactions determination is impossible via linear vector autoregressive models, but is, nonetheless, feasible with the aid of the so-called Kernel methods that are popular in machine learning. The kernelization approach allows defining generalised versions for Granger tests, partial directed coherence and directed transfer function, which the simulation of some examples shows that the asymptotic detection results originally deducted for linear estimators, can also be employed under kernelization if suitably adapted.

In this study, linear and nonlinear Granger causality tests are used to examine the dynamics, including return to return and volume to volume relationships, between warrants and their underlying stocks in Taiwan Stock Exchange (TSEC). Results of previous studies are mixed and they only focus on linear relationship between the two markets. Here we take nonlinear relationship into consideration to assist in investigating what the direction of information flow is. We use intraday five-minute high frequency data and the result tells that, overall, for both return to return and volume to volume relations, there is bidirectional but asymmetry linear causality and weak unidirectional nonlinear causality from stock to warrant market between these two markets. Combining the linear and nonlinear results we conclude that the direction of information flow is mainly from stock market to warrant market.

Les TIC sont un facteur clé de performance dans les pays développés. Cette thèse s’intéresse à l’adoption des TIC et leur impact sur la performance des PME manufacturières d’un pays en développement. A la suite d’une première partie qui présente le cadre théorique et conceptuel, le reste de la thèse est organisé en trois études empiriques. La première étude propose une modélisation Probit afin d’identifier les déterminants d’adoption des TIC. Le capital humain est la variable explicative la plus significative. Se basant sur la régression linéaire à variables muettes, la causalité de Granger, le test de Kruskal-Wallis et le test de l’ANOVA de Welch, suivis des tests post-hoc correspondants, la deuxième étude met en évidence l’existence d’un fort lien statistique significatif entre le niveau d’adoption des TIC et la rentabilité. Dans une troisième étude, plusieurs modélisations Probit (simple, ordonné et multivarié) ont été testées sur différentes mesures de performance. Nous montrons, premièrement, que les TIC ont un impact positif sur la productivité, la rentabilité et la compétitivité. Deuxièmement, les TIC, le capital humain et la formation sont les déterminants de la performance globale. Enfin, la contribution des TIC à la performance globale est forte lorsqu’elles sont combinées au capital humain qualifié. En définitive, nos résultats empiriques ont montré un effet positif des TIC, du capital humain et du changement organisationnel sur la performance des PME. / ICT is a key performance factor in developed countries. This PhD thesis focuses on the adoption of ICTs and their impact on the performance of manufacturing SMEs in a developing country. Following a first part covering the theoretical and conceptual framework, the rest of the thesis is organized in three empirical studies. The first study uses a Probit model in order to identify the determinants of ICT adoption. Human capital seems to be the most significant explanatory variable. Based on linear regression of dummy variables, Granger causality, Kruskal-Wallis test, ANOVA test of Welch, followed by corresponding post-hoc tests, the second study highlights the existence of a strong statistically significant relationship between the level of ICT adoption and profitability. In a third study, many Probit models (simple, ordered and multivariate) were tested on different measures of performance. Firstly, we show that ICT have a positive impact on productivity, profitability and competitiveness of SMEs. Secondly, ICT, human capital and training are determinants of firm overall performance. Thirdly, when combined together, ICT and highly skilled human resources have an important contribution to the global performance. In conclusion, our empirical results demonstrate a positive impact of ICT, human capital and organizational change on firm performance.

Exponential smoothing algorithms are very attractive for the practical world such as in industry. When considering bivariate exponential smoothing methods, in addition to the properties of univariate methods, additional properties give insight to relationships between the two components of a process, and also to the overall structure of the model. It is important to study these properties, but even with the merits the bivariate exponential smoothing algorithms have, exponential smoothing algorithms are nonstatistical/nonstochastic and to study the properties within exponential smoothing may be worthless. As an alternative approach, the (bivariate) ARIMA and the structural models which are classes of statistical models, are shown to generalize the exponential smoothing algorithms. We study these properties within these classes as they will have implications on exponential smoothing algorithms. Forecast properties are studied using the state space model and the Kalman filter. Comparison of ARIMA and structural model completes the study. / Mathematical Sciences / M. Sc. (Statistics)

In the literature there are mixed results on the link between credit and agricultural output growth. Some authors argue that credit leads to growth in agricultural output. Others view growth as one of the factors that influence credit supply, thus growth leads and credit follows. By and large, studies have not endeavoured to establish the short-run impact of agricultural credit on output. They are generally limited in establishing the long-run relationship between credit and agricultural output and thus present a research gap in this respect. This study contributes to the existing body of literature by focusing on the finance-growth nexus at sectoral level as a departure from extant literature that has focused on the macroeconomic level. Using South African data, the study investigated the causal relationship between the supply of credit and agricultural output as well as whether the two are cointegrated and have a short-run relationship. The study found that bank credit and agricultural output are cointegrated. Using the error correction model (ECM), the results showed that, in the short-run, bank credit has a negative impact on agricultural output, reflecting the uncertainties of institutional credit in South Africa. However, the ECM coefficient shows that the supply of agricultural credit rapidly adjusts to short-term disturbances, indicating that there is no room for tardiness in the agricultural sector. The absence of institutional credit will immediately be replaced by availability of other credit facilities from non-institutional sources. Conventional Granger causality tests show unidirectional causality from (1) bank credit to agricultural output growth, (2) agricultural output to capital formation, (3) agricultural output to labour, (4) capital formation to credit, and (5) capital formation to labour, and a bi-directional causality between credit and labour. Noteworthy and significant for South Africa is that for the agricultural sector, the direction of causality is from finance to growth, in other words supply-leading, whereas at the macroeconomic level, the direction of causality is from economic growth to finance, in other words, demand-leading. Applying a structural equation modelling approach to survey data of smallholder farmers, the positive relationship between bank credit and agricultural output observed from analysis of secondary data was confirmed. / Business Management / DCOM (Business Management)

This study provides evidence of the relationship between bank-granted credit and business cycles in South Africa. The study is conducted in three phases, namely qualitative research (Phase I), quantitative research (Phase II) and econometric analysis (Phase III). A sequential (connected data) mixed methodology (Phase I and II) is used to collect and analyze primary data from market participants. The qualitative research (Phase I) involves structured interviews with influential or well informed people on the subject matter. Phase I of the study is used to understand the key determinants of bank credit in South Africa and to appreciate how each of the credit aggregates behaves during alternate business cycles. Qualitative survey results suggest key determinants of commercial bank credit in South Africa as economic growth, collateral value, bank competition, money supply, deposit liabilities, capital requirements, bank lending rates and inflation. The qualitative results are used to formulate questions of the structured survey questionnaire (Quantitative research- Phase II). The ANOVA and Pearman’s product correlation analysis techniques are used to assess relationship between variables. The quantitative results show that there is direct and positive relationship between bank lending behavior and credit aggregates namely economic growth, collateral value, bank competition and money supply. On the other hand, the results show that there is a negative relationship between credit growth and bank capital and lending rates. Overall, the quantitative findings show that bank lending in South Africa is procyclical. The survey results indicate that the case for demand-following hypothesis is stronger than supply-leading hypothesis in South Africa. The econometric methodology is used to augment results of the survey study. Phase III of the study re-examines econometric relationship between bank lending and business cycles. The study employs cointegration and vector error correction model (VECM) techniques in order to test for existence of long-run relationship between the selected variables. Granger causality test technique is applied to the variables of interest to test for direction of causation between variables. The study uses quarterly data for the period of 1980:Q1 to 2013:Q4. Business cycles are determined and measured by Gross Domestic Product at market prices while bank-granted credit is proxied by credit extension to the private sector. The econometric test results show that there is a significant long-run relationship between economic growth and bank credit extension. The Granger causality test provides evidence of unidirectional causal relationship with direction from economic growth to credit extension for South Africa. The study results indicate that the case for demand-following hypothesis is stronger than supply-leading hypothesis in South Africa. Economic growth spurs credit market development in South Africa. Overall, the results show that there is a stable long-run relationship between macroeconomic business cycles and real credit growth in South Africa. The results show that economic growth significantly causes and stimulates bank credit. The study, therefore, recommends that South Africa needs to give policy priority to promotion and development of the real sector of the economy to propel and accelerate credit extension. Economic growth is considered as the significant policy variable to stimulate credit extension. The findings therefore hold important implications for both theory and policy. / Business Management / D.B.L.

Trade liberalization in Kenya started in the early 1980s with the structural adjustment programmes, and continued under the multilateral framework of the WTO. During the same period, the incidence of poverty and level of inequality also worsened. The government’s focus on trade negotiations has been to ensure that there is policy space for the daily running of the economy even though welfare impacts are also important. Non-state actors have argued that trade liberalization has negatively affected the poor; particularly the farmers, since they cannot compete with the developed countries whose farmers enjoy significant government support through subsidies, making their products much cheaper in the world market. Government officials, on the other hand, contend that trade liberalization is good as it brings in competition and transfer of technology which is good for an economy. It is important to examine how trade liberalization has affected household’s welfare in Kenya, given that this kind of analysis has not been conducted in Kenya. This study is unique because it does not assume the existence of a trade liberalization– poverty relationship, unlike most studies. It uses a multi-method approach to first test the hypothesis that there is no statistically significant relationship between trade liberalization and poverty, it further tests for multiplier effects of trade liberalization on poverty determinants. Trade Liberalization and poverty is found to have a stochastic relationship, furthermore investments and capital stock were found to significantly affect poverty determinants in the stochastic model. Due to unavailability of household welfare measure data in time series, a CGE model was used to establish the dynamics of trade liberalization on poverty at a point in time using the 2003 Social Accounting Matrix Data for Kenya. Overall, trade liberalization accompanied by FDI had the greatest impact on household welfare. Trade liberalization had a positive impact on household welfare since household incomes and consumption increased. Micro simulations results, based on changes in consumption, also showed that poverty incidence reduced for all households, even though the urban households experienced higher decreases. The study found that there was little difference in protecting sensitive products and not protecting them; secondly, trade liberalization accompanied by foreign direct investment had greater impact on improving the household welfare. Consumption and incomes increased, resulting in overall poverty reduction. The welfare of urban households was much higher than rural households in terms of income and consumption increases. However, income inequality was much higher in urban than rural areas. / Economics / D. Litt. et Phil. (Economics)