Causality analysis is an approach to time series analysis that is being used increasingly to investigate neuroimaging data. The reason for its popularity is the useful perspective it provides in describing the ordered operations of various brain regions using indirectly and passively measured neurophysiological signals. Although there are numerous frameworks with which causality analysis can be performed, one concept in particular – termed Granger causality (GC) – is receiving much of the attention because of its ease of implementation and interpretability. GC makes use of the fact that a predictive relationship between the history of one signal and the future of another signal provides evidence for there being a causal relationship between the two signals, and as a result, the physical events underlying those signals. If such a relationship can be established across neural time series, causal dependencies between neural pathways can be inferred and their contribution to brain function can be studied. Several analysis frameworks exist for applying GC to neurophysiological questions but many of these frameworks have deficiencies that impede their application to large and highly multivariate neuroimaging datasets. To address some of these concerns, this study develops the theory and methods for a novel neural time series classification procedure – referred to as GC classification – based on concepts in GC analysis. Validation of this method in neuroimaging research is provided by showing that it can be applied to heterogeneous datasets, that it makes use of many parallel sources of information about causal relationships, and that it can be adapted to different types of preprocessing steps to uncover causal relationships in multivariate neural time series data. Application of this analysis method to human behavioural MEG data revealed that, during a cued button-pressing task, distinct causal relationships exist between sensory cortices and their downstream targets preceding the initiation of actions that differ by whether or not they were the result of a decision being made. These results provide evidence that the GC classification procedure is a useful and robust technique for studying causal relationships in neurophysiological time series.

Causality analysis is an approach to time series analysis that is being used increasingly to investigate neuroimaging data. The reason for its popularity is the useful perspective it provides in describing the ordered operations of various brain regions using indirectly and passively measured neurophysiological signals. Although there are numerous frameworks with which causality analysis can be performed, one concept in particular – termed Granger causality (GC) – is receiving much of the attention because of its ease of implementation and interpretability. GC makes use of the fact that a predictive relationship between the history of one signal and the future of another signal provides evidence for there being a causal relationship between the two signals, and as a result, the physical events underlying those signals. If such a relationship can be established across neural time series, causal dependencies between neural pathways can be inferred and their contribution to brain function can be studied. Several analysis frameworks exist for applying GC to neurophysiological questions but many of these frameworks have deficiencies that impede their application to large and highly multivariate neuroimaging datasets. To address some of these concerns, this study develops the theory and methods for a novel neural time series classification procedure – referred to as GC classification – based on concepts in GC analysis. Validation of this method in neuroimaging research is provided by showing that it can be applied to heterogeneous datasets, that it makes use of many parallel sources of information about causal relationships, and that it can be adapted to different types of preprocessing steps to uncover causal relationships in multivariate neural time series data. Application of this analysis method to human behavioural MEG data revealed that, during a cued button-pressing task, distinct causal relationships exist between sensory cortices and their downstream targets preceding the initiation of actions that differ by whether or not they were the result of a decision being made. These results provide evidence that the GC classification procedure is a useful and robust technique for studying causal relationships in neurophysiological time series.

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The global food supply is being stressed by climate change, a growing population, and harmful diseases. One risk to vital cereal crops such as wheat and barley is Fusarium head blight (FHB), caused by the fungal plant pathogen Fusarium graminearum. Ascospores of the fungus are released from perithecia on the residues of corn and small grains and can be transported long distances (>500 m) through the atmosphere. The overall objective of this work was to assess the influence of meteorological conditions and perithecial maturity on ascospore release. The research focuses on F. graminearum because of its damaging impact to staple crops and the global ubiquity of FHB. The first specific objective was to apply state-of-the-science techniques to identify causal meteorological variables of ascospore release. We analyzed field measurements of airborne ascospores against meteorological conditions at Virginia Tech's Kentland Farm, Blacksburg, Virginia, USA and used convergent cross mapping and multivariate state space reconstruction to identify significant causal agents within this complicated natural and dynamic system. We identified relative humidity, solar radiation, wind speed, and air temperature as predictors of ascospore release. Our second research objective was to understand the impact of varying meteorological conditions on ascospore release under controlled environmental conditions. We assessed ascospore release in a chamber with controlled temperature (15°C and 25°C) and relative humidity (60%, 75%, and 95%). Ascospores released from ascospore-producing structures (perithecia) were captured on microscope slides placed inside of 3D-printed ascospore discharge devices. Results showed the sensitivity of ascospore release to relative humidity and temperature, with cool temperature and high relative humidity resulting in greater quantities of ascospores released. Our third research objective was to determine the relationship between the maturity, the number of ascospores, and the hardness of perithecia. A mechanical compression testing instrument was used to investigate the hardness of perithecia at various stages of maturity, producing a mean perithecium compression constant quantifying the uniaxial compression force required to rupture a perithecium. Results indicated that old perithecia contain the greatest amount of ascospores and exhibit increased resiliency, requiring greater forces to rupture, compared to young perithecia. This research has illustrated the complexities of F. graminearum ascospore release by describing the impact of several meteorological conditions and perithecial maturity on the timing and quantity of released ascospores. Collectively, our results may inform wheat growers on the nature and timing of ascospore release, which could help inform FHB management decisions in the future. / Ph. D.

In this paper we estimate the short-term and long-term relationship between stock prices and exchange rates for the sample of US and Asian markets during the period 2004 – 2014. Monetary variables include money supply, interest rates, foreign exchange rates, and the consumer price index. All the data are monthly indices and have been examined using multivariate co integration analysis and Granger causality analysis. The empirical analysis employed provides evidence of a positive co-integrating short- run relationship between these variable with Granger causality found to run from stock prices to the exchange rate during the sample period in Japan. For US, significant relationships were not been established. The results for Japan confirm the conclusion of other studies that stock returns are significant predictors of short – run exchange rate movements especially in period of financial crisis. We also apply LS model in order to estimate a linear regression. / Στην εργασία αυτή θα εκτιμηθεί η βραχυπρόθεσμη και μακροπρόθεσμη σχέση μεταξύ των τιμών των μετοχών και των συναλλαγματικών ισοτιμιών για το δείγμα των αμερικανικών και ασιατικών αγορών κατά την περίοδο 2004-2014. Νομισματικές μεταβλητές περιλαμβάνουν την προσφορά χρήματος, τα επιτόκια, τις συναλλαγματικές ισοτιμίες και τον δείκτη τιμών καταναλωτή. Όλα τα στοιχεία είναι μηνιαία και έχουν εξεταστεί σύμφωνα με πολυπαραγοντική ανάλυση και την ανάλυση της αιτιότητας. Η εμπειρική ανάλυση που χρησιμοποιείται παρέχει απόδειξη της θετικής σχέσης μεταξύ αυτών των μεταβλητών με Granger αιτιότητα από τις τιμές των μετοχών προς την συναλλαγματική ισοτιμία κατά τη διάρκεια της περιόδου του δείγματος στην Ιαπωνία. Για την Αμερική, σημαντικές σχέσεις δεν έχουν τεκμηριωθεί. Τα αποτελέσματα για την Ιαπωνία επιβεβαιώνουν το συμπέρασμα άλλων μελέτών ότι οι αποδόσεις των μετοχών είναι σημαντικοί παράγοντες πρόβλεψης των βραχυπροθεσμων διακυμανσεων των συναλλαγματικών ισοτιμιών,ιδίως σε περίοδο οικονομικής κρίσης. Μπορούμε επίσης να εφαρμόσουμε το μοντέλο LS, προκειμένου να εκτιμηθεί μια γραμμική παλινδρόμηση.

This thesis employs the Autoregressive Distributed Lag model (ARDL), Toda-Yamamoto causality analysis, and ordinary least square (OLS for robust estimation) techniques to empirically investigate the impact of economic growth and energy consumption on the environment in Nigeria from 1980 to 2020. The results of cointegration demonstrate a long-term link between the model's input variables. The outcome of the first objective of the study shows that trade and economic development in Nigeria worsen the state of the environment. Environmental quality is accelerated by financial development; nevertheless, FDI is proven to be insignificant in predicting environmental quality. The result demonstrates that FDI and energy use both have the potential to significantly speed up the rate of environmental degradation. Nevertheless, trade has a negligible impact on the environment in the country, and financial development slows down environmental deterioration. The study also finds that the combination between energy and economic development improves Nigeria's environmental quality. The outcome of the fourth objective shows that economic expansion and energy consumption have a favorable impact on the environment. Additionally, environmental degradation, energy use, and economic growth are all causally related. Moreover, the outcome of the robust estimation reveals a positive and significant relationship between economic growth and energy consumption in the environment. Therefore, the study suggests economic policies with environmental control measures. This could be through an emphasis on the use of other alternatives of low-emission energy, that will mitigate the level of C02 and enhance energy utilization for a better environment in the nation.

Submitted by Eliene Soares da Silva (eliene.silva@fgv.br) on 2013-02-14T11:32:36Z No. of bitstreams: 1 MPGI MasterThesis Mattia Ambrosini 645094.pdf: 1221533 bytes, checksum: 1117aaa68ec08569df715c16c4e0e0d0 (MD5) / Approved for entry into archive by Eliene Soares da Silva (eliene.silva@fgv.br) on 2013-02-14T11:44:31Z (GMT) No. of bitstreams: 1 MPGI MasterThesis Mattia Ambrosini 645094.pdf: 1221533 bytes, checksum: 1117aaa68ec08569df715c16c4e0e0d0 (MD5) / Made available in DSpace on 2013-02-14T12:08:26Z (GMT). No. of bitstreams: 1 MPGI MasterThesis Mattia Ambrosini 645094.pdf: 1221533 bytes, checksum: 1117aaa68ec08569df715c16c4e0e0d0 (MD5) Previous issue date: 2012-12-20 / The thesis at hand adds to the existing literature by investigating the relationship between economic growth and outward foreign direct investments (OFDI) on a set of 16 emerging countries. Two different econometric techniques are employed: a panel data regression analysis and a time-series causality analysis. Results from the regression analysis indicate a positive and significant correlation between OFDI and economic growth. Additionally, the coefficient for the OFDI variable is robust in the sense specified by the Extreme Bound Analysis (EBA). On the other hand, the findings of the causality analysis are particularly heterogeneous. The vector autoregression (VAR) and the vector error correction model (VECM) approaches identify unidirectional Granger causality running either from OFDI to GDP or from GDP to OFDI in six countries. In four economies causality among the two variables is bidirectional, whereas in five countries no causality relationship between OFDI and GDP seems to be present.