Browsing by Subject "Panel data"

Now showing 1 - 6 of 6
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    Investor beliefs and their impact on financial markets
    (2021) Hartmann, Carolin; Burghof, Hans-Peter
    The idea of this thesis is to use new data sources to approximate investor beliefs. It investigates whether the approximation improves the measurement of return and volatility in existing model frameworks. The findings are that differences in implied volatility, Google Search volume and Twitter Volume can be proxy variables for investor beliefs. They have an impact on financial market indicators and on the prediction of future market movements. Comparison of the trading behaviour of individual and institutional investors to predict market movements The first approach is to create a new sentiment index which compares the difference between retail investor behaviour at the Stuttgart Stock Exchange (SSE) and professional investors at the Frankfurt Stock Exchange (FSE). The measure is a comparison between the implied volatility measures for the DAX at the FSE (VDAX and VDAX-NEW) and a newly created implied volatility index (VSSE) for the SSE. The sentiment index is significant in predicting the daily returns on a size-based long-short portfolio over a four-year period. The analysis shows the persistent inconsistence between prices of structured products for retail investors on the SSE and option prices of professional investors on the FSE. The results provide empirical evidence that there are significant persistent behavioural differences between the two investor types which is reflected in persistent mispricing. Measurability of investor beliefs and their impact on financial markets The second approach is to measure individual investor beliefs with Google search volume (GSV) and Twitter volume (TV) to analyse their impact on financial markets. The basis is a daily panel of 29 Dow Jones Industrial average index (DJIA) stocks over a time period of 3.5 years in a panel data set-up. The impact on trading activity measured by turnover, is positive for GSV and TV on the same day and the next day which indicates their predictive power. The impact on realized volatility (RV), indicating the share of noise traders on the market, is only positive and significant for TV. It is significant on the same day and the next day. The impact of GSV is not significant. The results support the idea that GSV and TV capture the beliefs of individual investors. Although they suggest that the impact of TV on financial markets is more important than the impact of GSV. Predictive power of Google and Twitter The third approach is to use GSV and TV as a proxy for investor attention and investor sentiment, to assess their predictive power on the RV of the DJIA. The basis is a time-series set-up with a vector autoregression (VAR) model over a period of 2.5 years. The findings show that GSV and TV granger cause RV, controlling for macroeconomic and financial factors. Again, the effect of TV on RV is more important than the effect of GSV. In-sample, the linear prediction model with GSV and TV outperforms a standard AR (1) process. Out-of-sample the AR (1) process outperforms the standard model with GSV and TV. Clustering for high and low volatility groups, the analysis shows that the effect of GSV and TV on RV changes. Especially in times of high and low RV, GSV and TV seem to contain new information, as they improve the model fit compared to a standard AR (1) process. However, the results are not persistent in- and out-of-sample. This underlines that the results of GSV and TV are not generally persistent but depend on the selected criteria. Overall, the results of this thesis show that investor beliefs have an impact on financial markets. The measures, such as a sentiment index based on implied volatility, GSV and TV are proxy variables for investor beliefs. Future research should further improve the comprehension of investor beliefs to improve causality and economic significance in the long term.
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    North-South trade agreements and the quality of institutions: panel data evidence
    (2018) Schneider, Sophie Therese
    Since 1990, not only the number of signed preferential trade agreements (PTAs) has increased, but also their depth. That means, PTAs include comprehensive rules, which go way beyond tariff reductions, such as property rights, competition or investment provisions. This paper argues that especially in North-South agreements there is a diffusion of institutional quality from developed to developing countries. First, a PTA may affect institutions because it can serve as a network for political exchange and second, the regulations and commitments stipulated in it may affect local institutions in the South. I empirically investigate if there are positive effects of being a member in a PTA on the quality of institutions in developing countries by accounting for the number and the depth of PTAs using the Design of Trade Agreements (DESTA) database, established by Dür, Baccini and Elsig (2014). I create a large panel data set covering 32 years to account for endogeneity of several controls. The results support the hypothesis that deep PTAs lead to an improved quality of institutions in the South. The results differ with respect to the type of agreement and region.
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    The impact of agricultural innovations on poverty, vulnerability and resilience to food insecurity of smallholders in Ethiopia
    (2022) Biru, Wubneshe Dessalegn; Zeller, Manfred
    Ethiopia has adopted agriculture centered growth strategies over the last three decades that give more emphasis on improving agricultural production and productivity with the ultimate goal to transform the country’s economy. The strategies have mainly aimed at improving smallholder agriculture through introducing improved technologies intended to boost agricultural production and thus alleviate poverty and food insecurity. Although agriculture centered growth strategies contributed to sustained growth in the country over the last two decades, the benefits of growth have not been evenly distributed with observed rising income inequality and a still significant proportion of smallholders remaining under the poverty line. Similarly, despite considerable yield progress over the last three decades due to the introduction of improved inputs Ethiopian farmers’ yield gap compared with other developing countries is quite high. Moreover, the frequent occurrences of shocks such as drought and flooding adversely affect smallholders substantially and thereby exacerbate the existing poverty and food insecurity problems in the country. This thesis applied different econometric techniques to analyze the impact of the adoption of multiple agricultural technologies on crop yield, poverty, vulnerability, and resilience to food insecurity in Ethiopia. The study uses four rounds of household level panel data collected between 2012 and 2019 to assess the link between the adoption of the different combinations of five productivity-enhancing technologies: chemical fertilizer, improved seed, pesticide, and soil and water conservation practices: terracing and contour ploughing on consumption, poverty, vulnerability, and yields of smallholders. To solve the endogeneity problem in the regression models, we applied two-stage multinomial endogenous switching regression model combined with the Mundlak approach. Additionally, the thesis examines the role of the adoption of chemical fertilizer and improved seeds on household resilience to food insecurity amid the occurrence of adverse shocks. The findings are presented in three chapters of the cumulative thesis (Chapters two to four). Chapter two analyses the effect of productivity enhancing technologies and soil and water conservation measures and their possible combinations on consumption, poverty, and vulnerability to poverty. Per capita consumption expenditure for food and other essential non-food items, such as clothing and footwear, is used as a proxy variable to measure poverty. Using the national poverty line in 2011 prices, sample households are grouped into poor and non-poor households and the movement of sample households in and out of poverty between 2012 and 2016 is analyzed using a poverty transition matrix. By employing the ordered logit model, the study additionally examined the dynamics of poverty and vulnerability as well as their drivers. The results show that the adoption of the different combinations of agricultural technology sets including single technology adoption has considerable impacts on consumption expenditure and the greatest impact is attained when farmers combine multiple complementary inputs. Similarly, we find that the likelihood of households remaining poor or vulnerable decreased with adoption. In addition, the study revealed that poorer households are the least adopters of the technology combinations considered in the study, thereby being the least to benefit from adoption. We, therefore, conclude that the adoption of multiple complementary technologies has substantial dynamic benefits that improve the poverty and vulnerability status of households, and given the observed low level of adoption rates, we suggest that much more intervention is warranted, with a special focus on poorer and vulnerable households, to ensure smallholders get support to improve their input use. Chapter three assesses the impacts of multiple technology adoption on the yield of Ethiopia’s four staple crops, namely teff, wheat, maize and barley. Regarding the empirical estimation, we specified yield equations for each of the four crops and five to six possible input combinations that are included in the analysis indicating the presence of slope effect of technology choice other than the intercept of the outcome equations. The findings suggest that the application of two or more complementary inputs is considerably linked with higher maize, teff, barley, and wheat yield. Specifically, barley yield is highest for farmers who have adopted a combination of at least three of the technologies. Maize producers are the largest beneficiaries of the technologies. The impact of the technology choice sets tends to have an inconclusive effect on wheat and teff yields. However, a significant yield gap in all of the four crops was observed. Socio-economic characteristics of the household head such as age and gender as well as the household’s access to infrastructure and spatial characteristics of the household are other important determinants of crop yield. The implications are that more publicly funded efforts could be worthwhile for easing adoption constraints, which would in turn help smallholders to increase their crop yields that indirectly improve their livelihood. Chapter four aims to identify the determinants of household resilience to food insecurity which is the household’s ability to absorb or cope with the negative effects of shocks and bounce back to at least their initial livelihood status and assess its role on future household food security when hit by adverse shocks. Furthermore, the study analyzes the role of single or joint adoption of chemical fertilizer and improved seed on household food security. The household food security indicators used in the analysis are dietary diversity and per capita food consumption and uses data from the last three waves out of our four survey rounds. In terms of empirical estimation, the household resilience capacity index is estimated by combining factor analysis and structural equation modeling. Then different regression models are executed to assess the causal link between technology adoption and resilience capacity and household food security indicators in the face of adverse shocks. Our findings reveal that the most important pillars contributing to the building of household resilience capacity are assets followed by access to basic services. We find that the initial level of the household resilience score is significantly and positively associated with future household food security status. Moreover, the results reveal that the adoption of chemical fertilizer and improved seed is significantly and positively associated with household resilience capacity index, dietary diversity, and food consumption over time. Shocks such as drought appear to be significant contributors to the loss of household food security. Overall, it is revealed that the adoption of improved inputs significantly and positively increases household food security. However, the results show no evidence that supports the current level of adoption that helps households to shield themselves from the adverse effects of shocks. Finally, this study gives insights on examining the impacts and impact pathways of adoption of improved technologies on smallholder welfare which guide decision-makers for intervention as well as pave a way for future research that contributes to the fight against rural poverty and food insecurity. This thesis also concludes that public intervention in terms of investment in providing improved agricultural practices is crucial in improving rural livelihood, but it has to be inclusive and provide opportunities for the poor and vulnerable.
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    Trade effects of the Europe agreements
    (2006) Spies, Julia; Marques, Helena
    The eastern enlargement of the European Union (EU) brought and will bring full membership to countries whose trade barriers with the EU had to a large extent already been removed under Free Trade Agreements (FTAs) during the 1990s. We employ a theory-based new version of a gravity equation, whose specification allows for an assessment of the impact of the arrangements on extra- and intra-group imports. We find robust evidence that the agreements have substantially increased intra-group trade, in the case of the Czech and Slovak Republic at the expense of the Rest of the World (ROW).
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    Using panel data to estimate the effect of rainfall shocks on smallholders food security and vulnerability in rural Ethiopia
    (2009) Zeller, Manfred; Demeke, Abera Birhanu
    Ethiopia's agriculture is predominantly rainfed and hence any irregularity in weather conditions has adverse welfare implications. Using panel data, this paper analyzes the effect of rainfall shocks on Ethiopian rural households' food security and vulnerability over time while controlling for a range of other factors. To this end, we generated a time-variant household food security index which is developed by principal components analysis. Based on the scores of the index, households were classified into relative food security groups and their socioeconomic differences were assessed. The exploratory results show that compared to the less secured households, the more secured ones have male and literate household heads, tend to have a greater number of economically active household members, own more livestock, experience better rainfall outcome, participate in equb (a local savings group), and use chemical fertilizer. Fixed effects regression was used to identify the factors which affect the score's variability and the results indicate that rainfall shock is an important factor affecting households' food security over time. It is also noted that household size, head's age, participation in equb, off-farm activities, use of fertilizer, and livestock ownership positively and significantly affect the food security score. Results from multinomial logistic regression model reinforce the fixed effects regression results by showing the strong association of persistent food insecurity and vulnerability with adverse rainfall shock. A number of conclusions can be drawn from the results which are useful for policymakers as well as for agencies that engage in areas of risk and food security.
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    You can't always get what you want? Estimator choice and the speed of convergence
    (2016) Kufenko, Vadim; Prettner, Klaus
    We propose theory-based Monte Carlo simulations to quantify the extent to which the estimated speed of convergence depends on the underlying econometric techniques. Based on a theoretical growth model as the data generating process, we find that, given a true speed of convergence of around 5%, the estimated values range from 0.2% to 7.72%. This corresponds to a range of the half life of a given gap from around 9 years up to several hundred years. With the exception of the (very inefficient) system GMM estimator with the collapsed matrix of instruments, the true speed of convergence is outside of the 95% confidence intervals of all investigated state-of-the-art estimators. In terms of the squared percent error, the between estimator and the system GMM estimator with the non-collapsed matrix of instruments perform worst, while the system GMM estimator with the collapsed matrix of instruments and the corrected least squares dummy variable estimator perform best. Based on these results we argue that it is not a good strategy to rely on only one or two different estimators when assessing the speed of convergence, even if these estimators are seen as suitable for the given sources of biases and inefficiencies. Instead one should compare the outcomes of different estimators carefully in light of the results of Monte Carlo simulation studies.