Published Article / This article presents the results of a business survey conducted in the Great Karoo town of Graaff-Reinet. The survey solicited the views of business owners on a range of economic issues. The findings also draw on a number of in-depth Midlands-Karoo studies, carried out in the early 1970s, in order to add nuance to the prevailing understanding of the factors that influence local economic development (LED) in small towns. It is argued that LED is a multi-facetted phenomenon. It requires a holistic approach that recognises its inherent complexity, involving factors such as local leadership, diversification, the local skills base, in-migration, corporate investment, and entrepreneurship.

This thesis deals with the business proposal, which we intend to implement from the 2nd quarter of 2018 and which should serve to implement a tackle shop. The work characterizes basic business concepts, makes the analysis of the current situation on the market and of the secured place for the performance of business activities including the proposed modifications of the interior and describes the anticipated costs and benefits of the project. In the last chapter there is a summary timetable for the implementation of the project, so that nothing was forgotten and the shop could be opened.

Laut dem Weltklimarat ist es zweifelsfrei, dass sich das Klima global ändert. Ein sich veränderndes Klima wirkt sich jedoch nicht nur auf die Umwelt, sondern auch auf Unternehmen aus. Diese Auswirkungen können sowohl positiver als auch negativer Art sein. Um auf diese Auswirkungen adäquat zu reagieren, ist es zunächst von Bedeutung, die positiven als auch negativen Effekte zu identifizieren und diese entsprechend zu interpretieren. Ziel der Befragung ist es daher, zu erfassen, wie sich Unternehmen vom Klimawandel betroffen fühlen und wie diese darauf reagieren. Des Weiteren wird untersucht, welche Faktoren einen Einfluss auf den Anpassungsprozess haben. Für die Befragung wurden im Juni 2012 über 2.000 Handwerksbetriebe angeschrieben, wovon insgesamt 207 Unternehmen antworteten. Zwei Drittel dieser Unternehmen nimmt den Klimawandel wahr. Allerdings fühlte sich in der Vergangenheit die Mehrheit der Unternehmen von Extremwetterereignissen nicht betroffen. Die größten negativen Einflüsse werden gegenwärtig als auch in der Zukunft bei den Kältewellen gesehen. Innerhalb des Unternehmens sind insbesondere die Logistik und der Einkauf in Zukunft negativ betroffen; positive Wirkungen werden sich hingegen beim Absatz erhofft. Insgesamt erwarten die Unternehmen eher negative als positive Effekte aus dem Klimawandel, wobei insbesondere das Nahrungsmittelgewerbe und das KFZ-Gewerbe mit negativen Auswirkungen auf ihren Betrieb rechnen. Da sich nur wenige Unternehmen von Extremwetterereignissen bzw. dem Klimawandel betroffen fühlen, verwundert es nicht, dass fast drei Viertel der Unternehmen keine Anpassungsmaßnahmen planen und der Teil, der Anpassungsmaßnahmen umsetzt(e) bzw. beabsichtigt umzusetzen, eher einen geringen Anteil ausmacht. Dies könnte darauf zurückgeführt werden, dass in etwa jedes zweite Unternehmen die Auswirkungen des Klimawandels gegenwärtig nicht finanziell spürt. Darüber hinaus fehlen finanzielle Eigenmittel, private Finanzierungsmöglichkeiten und öffentliche Fördermöglichkeiten. Des Weiteren besteht eine hohe Unsicherheit, ob Extremwettereignisse überhaupt auftreten, und welche Anpassungsmaßnahmen möglich wären. Basierend auf den Befragungsergebnissen ist zu empfehlen, Unternehmen mit Informationen zur Thematik Klimawandelfolgen und Anpassungsoptionen zu versorgen, Unternehmen zu Risikoanalysen zu motivieren und mögliche finanzielle Unterstützung im Rahmen der Anpassung an die Auswirkungen des Klimawandels anzubieten.

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