This paper examines the impact of the recent global financial crisis on economic growth in developing economies and South Africa in particular. It explores whether the events experienced by developing countries conform to what would be anticipated from economic theory. This is done by firstly comparing country growth forecasts for 2012 captured in 2008 at the beginning of the crisis to actual 2012 GDP growth data. Secondly, panel data analysis is used to investigate three important transmission channels, namely those of Trade, Capital Flows and Exchange Rates for 25 developing economies. The results suggest that economic forecasters in 2008 on average overestimated GDP growth for 2012 by -21.6 percent (excluding Venezuela). The only important transmission channel identified using Trend analysis to explain this negative impact on growth was capital flows. However when using Panel regression analysis all three channels were found to explain the economic impact of the crisis on GDP growth for developing countries, conforming to economic theory. It was discovered that, contrary to what was initially expected, portfolio inflows actually increased for most developing countries during the crisis. This possibly can be explained by the impact of quantitative easing in the USA. South Africa was found to have been negatively impacted by the global financial crisis, but to a lesser extent when compared to most other developing countries. The findings are important for global investors looking for new investment opportunities. The extent to which individual economies are “decoupled” from developed economies’ performance provides possible opportunities for diversifying risk through a geographic spread of investor portfolios.

The maturity transformation of deposits is a primary driver of economic growth, as loans enable borrowers to spend funds, thereby growing the economy. However, if borrowers cannot repay their loans, the asset quality of banks deteriorate, resulting in non-performing loans or, worse, an economic crisis. An understanding of how macroeconomic and microeconomic determinants impact bank asset quality in South Africa can contribute to knowledge of the bank asset quality phenomenon in the African context. Due to the 2008/2009 global financial crisis, the introduction of new legislation and the value of gold exports, the South African economy presents an opportunity to make an original contribution to the knowledge of determinants that influence bank asset quality. In addition to studying bank asset quality determinants that are contested in research, this study also aims to determine whether a superior returns determinant of non-performing loans exists when comparing a bank’s profitability determinants, namely return on assets, return on equity and interest income on loans. This study applied panel data regression analysis, making use of a balanced panel approach, to study the determinants of bank asset quality. This approach recontextualises the existing bank asset quality theory for the South African financial sector. The results indicate that South Africa is not resilient against the impact of global financial crises trickling through international trade linkages and that regulatory changes do not instantly improve bank asset quality, and may even reduce the short-term asset quality. Moreover, bank asset quality in South Africa is sensitive to the total value of gold exports. It is evident from the profitability measures that the interest income on loans is the most suitable profitability measure of bank asset quality. This study provides an original contribution to bank asset quality determinants and recommends that regulators should pre-emptively determine the impact of new legislation on bank asset quality. Furthermore, interest income on loans as a profitability measure provides the most accurate results. Lastly, a single-country bank asset quality analysis is important, especially for economies that have commodity exports that significantly weigh in on the bank asset mix. / Die termyntransformasie rakende deposito's is die primêre dryfkrag vir groei in die ekonomie: Lenings maak dit vir leners moontlik om fondse te bestee, wat die ekonomie laat groei. Indien hierdie leners hul lenings egter nie kan terugbetaal nie, gaan die gehalte van bankbates agteruit, wat tot wanpresterende lenings of, nog erger, tot 'n ekonomiese krisis kan lei. As begryp kan word hoe makro-ekonomiese en mikro-ekonomiese bepalende faktore op die gehalte van bankbates in Suid-Afrika inwerk, kan dit bydra tot kennis van die verskynsel van bankbategehalte in die Afrika-konteks. In die lig van die 2008/2009 wêreldwye finansiële krisis, die uitvaardiging van nuwe wetgewing en die waarde van gouduitvoere bied die Suid-Afrikaanse ekonomie ’n geleentheid om ’n oorspronklike bydrae te lewer tot kennis van die bepalende faktore wat bankbategehalte beïnvloed. Benewens die bestudering van die bepalende faktore van die gehalte van bankbates wat in navorsing redelik omstrede is, het hierdie studie ten doel om, wanneer 'n bank se winsgewendheidsbepalers, naamlik opbrengs op bates, opbrengs op ekwiteit (eiekapitaal) en rente-inkomste op lenings, met mekaar vergelyk word, vas te stel of daar ’n superieure opbrengsbepaler van wanpresterende lenings bestaan. Vir hierdie studie is ’n regressieontleding van paneeldata uitgevoer, en daar is van ’n gebalanseerde paneelbenadering gebruik gemaak om die bepalende faktore van bankbategehalte te bestudeer. Hierdie benadering herkontekstualiseer die bestaande bankbategehalteteorie vir die Suid-Afrikaanse finansiële sektor. Die resultate van die studie dui daarop dat Suid-Afrika nie veerkragtig is om die uitwerking van wêreldwye finansiële krisisse teen te werk wat met internasionale handelskakelings deursyfer nie en dat reguleringsveranderinge nie dadelik die bankbategehalte verbeter nie; dit kan inteendeel die korttermynbategehalte verlaag. Bowendien is die bankbategehalte in Suid-Afrika gevoelig vir die totale waarde van gouduitvoere. Dit blyk uit die winsgewendheidsmaatstawwe dat die rente-inkomste op lenings die mees geskikte winsgewendheidsmaatstaf van bankbategehalte is. Hierdie studie lewer ’n oorspronklike bydrae tot die bepalers van bankbategehalte en beveel aan dat reguleerders vooruit reeds die uitwerking van nuwe wetgewing op bankbategehalte moet bepaal. Daarby voorsien rente-inkomste op lenings as winsgewendheidsmaatstaf die akkuraatste resultate. Laastens is ’n ontleding van ’n enkele land se bankbategehalte van belang, in die besonder vir ekonomieë met kommoditeitsuitvoere wat beduidend tot die samestelling van bankbates bydra. / Kadimo ya nako ye kopana ya ditipositi ke mokgwa wo bohlokwa wa kgolo ya ekonomi, ka ge dikadimo di dumelela baadimi go šomiša matlotlo, go realo e le go godiša ekonomi. Efela, ge baadimi ba sa kgone go lefela dikadimo tša bona, boleng bja thoto ya dipanka bo a phuhlama, go feleletša go e ba le dikadimo tše di sa šomego gabotse goba, go feta fao, phuhlamo ya ekonomi. Kwešišo ya ka fao ditaetšo tša makroekonomi le maekroekonomi di huetšago boleng bja thoto ya panka ka Afrika Borwa e ka ba le seabe go tsebo ya taba ya boleng bja thoto ya panka go ya ka seemo sa Afrika. Ka lebaka la mathata a ditšhelete a lefase a 2008/2009, tsebišo ya molao wo moswa le boleng bja dithomelontle tša gauta, ekonomi ya Afrika Borwa e fa sebaka seabe sa mathomo tsebong ya ditaetšo tšeo di huetšago boleng bja thoto ya panka. Go tlaleletša nyakišišong ya ditaetšo tša boleng bja thoto ya panka tšeo di ganetšwago nyakišišong, maikemišetšo a nyakišišo ye gape ke go laetša ge eba taetšo ya letseno le legolo la dikadimo tše di sa šomego gabotse di gona ge go bapetšwa ditaetšo tša poelo ya panka, e lego letseno la dithoto, letseno la dišere le letseno la dikadimo. Nyakišišo ye e šomišitše tshekatsheko ya poelomorago ya datha ya phanele, ya go šomiša mokgwa wa phanele wo o lekaneditšwego, go nyakišiša ditaetšo tša boleng bja thoto ya panka. Mokgwa wa go tšwetšapele gape teori ya boleng bja thoto ya panka ya lekala la Afrika Borwa la ditšhelete. Dipoelo di laetša gore Afrika Borwa ga e fokole kgahlanong le khuetšo ya mathata a ditšhelete a lefase ao a rothelago ka dikamanong tša kgwebišano ya boditšhabatšhaba le gore diphetogo tša taolo ga di kaonafatše boleng bja thoto ya panka ka lebelo, gomme di ka fokotša le boleng bja thoto bja paka ye kopana. Go feta fao, boleng bja thoto ya panka ka Afrika Borwa bo ela hloko boleng bja palomoka bja dithomelontle tša gauta. Go a bonagala go tšwa go dikgato tša tiro ya poelo gore letseno la tswala godimo ga dikadimo ke kgato ya poelo ye maleba gagolo ya boleng bja thoto ya panka. Nyakišišo ye e fa seabe sa mathomo ditaetšo tša boleng bja thoto ya panka gomme e šišinya gore balaodi ba swanela go laetša e sa le ka pela khuetšo ya molao wo moswa ka ga boleng bja thoto ya panka. Go feta fao, letseno la tswala godimo ga dikadimo bjalo ka kelo ya tiro ya poelo le go fa dipoelo tše di lebanego gabotse. Sa mafelelo, tshekatsheko ya boleng bja thoto ya panka ya naga e tee, kudu diekonomi tšeo di nago le dithomelontle tša ditšweletšwa tšeo gagolo di dumelelago motswako wa thoto ya panka. / Business Management / Ph. D. (Management Studies)

CHAPTER 1:<p>The default g-priors predominant in Bayesian Model Averaging tend to over-concentrate posterior mass on a tiny set of models - a feature we denote as 'supermodel effect'. To address it, we propose a 'hyper-g' prior specification, whose data-dependent shrinkage adapts posterior model distributions to data quality. We demonstrate the asymptotic consistency of the hyper-g prior, and its interpretation as a goodness-of-fit indicator. Moreover, we highlight the similarities between hyper-g and 'Empirical Bayes' priors, and introduce closed-form expressions essential to computationally feasibility. The robustness of the hyper-g prior is demonstrated via simulation analysis, and by comparing four vintages of economic growth data.<p><p>CHAPTER 2:<p>Ciccone and Jarocinski (2010) show that inference in Bayesian Model Averaging (BMA) can be highly sensitive to small data perturbations. In particular they demonstrate that the importance attributed to potential growth determinants varies tremendously over different revisions of international income data. They conclude that 'agnostic' priors appear too sensitive for this strand of growth empirics. In response, we show that the found instability owes much to a specific BMA set-up: First, comparing the same countries over data revisions improves robustness. Second, much of the remaining variation can be reduced by applying an evenly 'agnostic', but flexible prior.<p><p>CHAPTER 3:<p>This chapter explores the link between the leverage of the US financial sector, of households and of non-financial businesses, and real activity. We document that leverage is negatively correlated with the future growth of real activity, and positively linked to the conditional volatility of future real activity and of equity returns. <p>The joint information in sectoral leverage series is more relevant for predicting future real activity than the information contained in any individual leverage series. Using in-sample regressions and out-of sample forecasts, we show that the predictive power of leverage is roughly comparable to that of macro and financial predictors commonly used by forecasters. <p>Leverage information would not have allowed to predict the 'Great Recession' of 2008-2009 any better than conventional macro/financial predictors. <p><p>CHAPTER 4:<p>Model averaging has proven popular for inference with many potential predictors in small samples. However, it is frequently criticized for a lack of robustness with respect to prediction and inference. This chapter explores the reasons for such robustness problems and proposes to address them by transforming the subset of potential 'control' predictors into principal components in suitable datasets. A simulation analysis shows that this approach yields robustness advantages vs. both standard model averaging and principal component-augmented regression. Moreover, we devise a prior framework that extends model averaging to uncertainty over the set of principal components and show that it offers considerable improvements with respect to the robustness of estimates and inference about the importance of covariates. Finally, we empirically benchmark our approach with popular model averaging and PC-based techniques in evaluating financial indicators as alternatives to established macroeconomic predictors of real economic activity. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

The thesis, entitled "Essays on macroeconometrics and short-term forecasting",<p>is composed of three chapters. The first two chapters are on nowcasting,<p>a topic that has received an increasing attention both among practitioners and<p>the academics especially in conjunction and in the aftermath of the 2008-2009<p>economic crisis. At the heart of the two chapters is the idea of exploiting the<p>information from data published at a higher frequency for obtaining early estimates<p>of the macroeconomic variable of interest. The models used to compute<p>the nowcasts are dynamic models conceived for handling in an efficient way<p>the characteristics of the data used in a real-time context, like the fact that due to the different frequencies and the non-synchronicity of the releases<p>the time series have in general missing data at the end of the sample. While<p>the first chapter uses a small model like a VAR for nowcasting Italian GDP,<p>the second one makes use of a dynamic factor model, more suitable to handle<p>medium-large data sets, for providing early estimates of the employment in<p>the euro area. The third chapter develops a topic only marginally touched<p>by the second chapter, i.e. the estimation of dynamic factor models on data characterized by block-structures.<p>The firrst chapter assesses the accuracy of the Italian GDP nowcasts based<p>on a small information set consisting of GDP itself, the industrial production<p>index and the Economic Sentiment Indicator. The task is carried out by using<p>real-time vintages of data in an out-of-sample exercise over rolling windows<p>of data. Beside using real-time data, the real-time setting of the exercise is<p>also guaranteed by updating the nowcasts according to the historical release calendar. The model used to compute the nowcasts is a mixed-frequency Vector<p>Autoregressive (VAR) model, cast in state-space form and estimated by<p>maximum likelihood. The results show that the model can provide quite accurate<p>early estimates of the Italian GDP growth rates not only with respect<p>to a naive benchmark but also with respect to a bridge model based on the<p>same information set and a mixed-frequency VAR with only GDP and the industrial production index.<p>The chapter also analyzes with some attention the role of the Economic Sentiment<p>Indicator, and of soft information in general. The comparison of our<p>mixed-frequency VAR with one with only GDP and the industrial production<p>index clearly shows that using soft information helps obtaining more accurate<p>early estimates. Evidence is also found that the advantage from using soft<p>information goes beyond its timeliness.<p>In the second chapter we focus on nowcasting the quarterly national account<p>employment of the euro area making use of both country-specific and<p>area wide information. The relevance of anticipating Eurostat estimates of<p>employment rests on the fact that, despite it represents an important macroeconomic<p>variable, euro area employment is measured at a relatively low frequency<p>(quarterly) and published with a considerable delay (approximately<p>two months and a half). Obtaining an early estimate of this variable is possible<p>thanks to the fact that several Member States publish employment data and<p>employment-related statistics in advance with respect to the Eurostat release<p>of the euro area employment. Data availability represents, nevertheless, a<p>major limit as country-level time series are in general non homogeneous, have<p>different starting periods and, in some cases, are very short. We construct a<p>data set of monthly and quarterly time series consisting of both aggregate and<p>country-level data on Quarterly National Account employment, employment<p>expectations from business surveys and Labour Force Survey employment and<p>unemployment. In order to perform a real time out-of-sample exercise simulating<p>the (pseudo) real-time availability of the data, we construct an artificial<p>calendar of data releases based on the effective calendar observed during the first quarter of 2012. The model used to compute the nowcasts is a dynamic<p>factor model allowing for mixed-frequency data, missing data at the beginning<p>of the sample and ragged edges typical of non synchronous data releases. Our<p>results show that using country-specific information as soon as it is available<p>allows to obtain reasonably accurate estimates of the employment of the euro<p>area about fifteen days before the end of the quarter.<p>We also look at the nowcasts of employment of the four largest Member<p>States. We find that (with the exception of France) augmenting the dynamic<p>factor model with country-specific factors provides better results than those<p>obtained with the model without country-specific factors.<p>The third chapter of the thesis deals with dynamic factor models on data<p>characterized by local cross-correlation due to the presence of block-structures.<p>The latter is modeled by introducing block-specific factors, i.e. factors that<p>are specific to blocks of time series. We propose an algorithm to estimate the model by (quasi) maximum likelihood and use it to run Monte Carlo<p>simulations to evaluate the effects of modeling or not the block-structure on<p>the estimates of common factors. We find two main results: first, that in finite samples modeling the block-structure, beside being interesting per se, can help<p>reducing the model miss-specification and getting more accurate estimates<p>of the common factors; second, that imposing a wrong block-structure or<p>imposing a block-structure when it is not present does not have negative<p>effects on the estimates of the common factors. These two results allow us<p>to conclude that it is always recommendable to model the block-structure<p>especially if the characteristics of the data suggest that there is one. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished