Sticky credit spreads, macroeconomic activity and equity market volatility

We uncover high persistence in credit spread series that can obscure the relationship between the theoretical determinants of credit risk and observed credit spreads. We use a Markovswitching model, which also captures the stability (low frequency changes) of credit ratings, to show why credit spreads may continue to respond to past levels of credit risk, even though the state of the economy has changed. A bivariate model of credit spreads and either macroeconomic activity or equity market volatility detects large and significant correlations that are consistent with theory but have not been observed in previous studies. © 2010 Nova Science Publishers, Inc. All rights reserved.

Konjunktúraingadozások az Egyesült Államokban 1970-2010 - Értelmezési nehézségek = Business cycles and recession patterns in the US economy and in the G7

Lehet-e beszélni a 2011-ig felgyülemlett empirikus tapasztalatok tükrében egy egységes válságlefolyásról, amely a fejlett ipari országok egészére általában jellemző, és a meghatározó országok esetében is megragadható? Megállapíthatók-e olyan univerzális változások a kibocsátás, a munkapiacok, a fogyasztás, valamint a beruházás tekintetében, amelyek jól illeszkednek a korábbi tapasztalatokhoz, nem kevésbé az ismert makromodellek predikcióihoz? A válasz – legalábbis jelen sorok írásakor – nemleges: sem a válság lefolyásának jellegzetességeiben és a makrogazdasági teljesítmények romlásának ütemében, sem a visszacsúszás mértékében és időbeli kiterjedésében sincsenek jól azonosítható közös jegyek, olyanok, amelyek a meglévő elméleti keretekbe jól beilleszthetők. A tanulmány áttekinti a válsággal és a makrogazdasági sokkokkal foglalkozó empirikus irodalom – a pénzügyi globalizáció értelmezései nyomán – relevánsnak tartott munkáit. Ezt követően egy 60 év távlatát átfogó vizsgálatban próbáljuk megítélni a recessziós időszakokban az amerikai gazdaság teljesítményét azzal a célkitűzéssel, hogy az elmúlt válság súlyosságának megítélése kellően objektív lehessen, legalább a fontosabb makrováltozók elmozdulásának nagyságrendje tekintetében. / === / Based on the empirical evidence accumulated until 2011, using official statistics from the OECD data bank and the US Commerce Department, the article addresses the question whether one can, or cannot, speak about generally observable recession/crisis patterns, such that were to be universally recognized in all major industrial countries (the G7). The answer to this question is a firm no. Changes and volatility in most major macroeconomic indicators such as output-gap, labor market distortions and large deviations from trend in consumption and in investment did all, respectively, exhibit wide differences in depth and width across the G7 countries. The large deviations in output-gaps and especially strong distortions in labor market inputs and hours per capita worked over the crisis months can hardly be explained by the existing model classes of DSGE and those of the real business cycle. Especially bothering are the difficulties in fitting the data into any established model whether business cycle or some other types, in which financial distress reduces economic activity. It is argued that standard business cycle models with financial market imperfections have no mechanism for generating deviation from standard theory, thus they do not shed light on the key factors underlying the 2007–2009 recession. That does not imply that the financial crisis is unimportant in understanding the recession, but it does indicate however, that we do not fully understand the channels through which financial distress reduced labor input. Long historical trends on the privately held portion of the federal debt in the US economy indicate that the standard macro proposition of public debt crowding out private investment and thus inhibiting growth, can be strongly challenged in so far as this ratio is neither a direct indicator of growth slowing down, nor for recession.

US Screening and Evaluation System of Inventions Utilized in Hungary

The commercialization of inventions is very complex and challenging therefore it requires the collaboration of several actors in an economy. Even when an invention possesses significant added value, its successful commercialization could only be executed in a stable macroeconomic and innovation environment and also if proper innovation management expertise is provided. ValDeal Innovations Zrt. was established to foster the commercialization of Hungarian, high business potential inventions by providing its business expertise. The company used an – already in various markets and countries probed – US innovation management method consisting of the tasks of technology evaluation as well as the commercialization of inventions. There were major changes necessary while probing the US method residing in the different macroeconomic circumstances and the attitudes for innovation in Hungary. The article details the above mentioned issues together with the conclusions the members of ValDeal have drawn during the innovation management process.

Glycemic control: Risk factors, quality of life, workforce participation, and mortality among US adults with type 2 diabetes

Despite research showing the benefits of glycemic control, it remains suboptimal among adults with diabetes in the United States. Possible reasons include unaddressed risk factors as well as lack of awareness of its immediate and long term consequences. The objectives of this study were to, using cross-sectional data, (1) ascertain the association between suboptimal (Hemoglobin A1c (HbA1c) .7%), borderline (HbA1c 7-8.9%), and poor (HbA1c .9%) glycemic control and potentially new risk factors (e.g. work characteristics), and (2) assess whether aspects of poor health and well-being such as poor health related quality of life (HRQOL), unemployment, and missed-work are associated with glycemic control; and (3) using prospective data, assess the relationship between mortality risk and glycemic control in US adults with type 2 diabetes. Data from the 1988-1994 and 1999-2004 National Health and Nutrition Examination Surveys were used. HbA1c values were used to create dichotomous glycemic control indicators. Binary logistic regression models were used to assess relationships between risk factors, employment status and glycemic control. Multinomial logistic regression analyses were conducted to assess relationships between glycemic control and HRQOL variables. Zero-inflated Poisson regression models were used to assess relationships between missed work days and glycemic control. Cox-proportional hazard models were used to assess effects of glycemic control on mortality risk. Using STATA software, analyses were weighted to account for complex survey design and non-response. Multivariable models adjusted for socio-demographics, body mass index, among other variables. Results revealed that being a farm worker and working over 40 hours/week were risk factors for suboptimal glycemic control. Having greater days of poor mental was associated with suboptimal, borderline, and poor glycemic control. Having greater days of inactivity was associated with poor glycemic control while having greater days of poor physical health was associated with borderline glycemic control. There were no statistically significant relationships between glycemic control, self-reported general health, employment, and missed work. Finally, having an HbA1c value less than 6.5% was protective against mortality. The findings suggest that work-related factors are important in a person’s ability to reach optimal diabetes management levels. Poor glycemic control appears to have significant detrimental effects on HRQOL.^

Glycemic Control: Risk Factors, Quality of Life, Workforce Participation, and Mortality Among US Adults with Type 2 Diabetes

Despite research showing the benefits of glycemic control, it remains suboptimal among adults with diabetes in the United States. Possible reasons include unaddressed risk factors as well as lack of awareness of its immediate and long term consequences. The objectives of this study were to, using cross-sectional data, 1) ascertain the association between suboptimal (Hemoglobin A1c (HbA1c) ≥7%), borderline (HbA1c 7-8.9%), and poor (HbA1c ≥9%) glycemic control and potentially new risk factors (e.g. work characteristics), and 2) assess whether aspects of poor health and well-being such as poor health related quality of life (HRQOL), unemployment, and missed-work are associated with glycemic control; and 3) using prospective data, assess the relationship between mortality risk and glycemic control in US adults with type 2 diabetes. Data from the 1988-1994 and 1999-2004 National Health and Nutrition Examination Surveys were used. HbA1c values were used to create dichotomous glycemic control indicators. Binary logistic regression models were used to assess relationships between risk factors, employment status and glycemic control. Multinomial logistic regression analyses were conducted to assess relationships between glycemic control and HRQOL variables. Zero-inflated Poisson regression models were used to assess relationships between missed work days and glycemic control. Cox-proportional hazard models were used to assess effects of glycemic control on mortality risk. Using STATA software, analyses were weighted to account for complex survey design and non-response. Multivariable models adjusted for socio-demographics, body mass index, among other variables. Results revealed that being a farm worker and working over 40 hours/week were risk factors for suboptimal glycemic control. Having greater days of poor mental was associated with suboptimal, borderline, and poor glycemic control. Having greater days of inactivity was associated with poor glycemic control while having greater days of poor physical health was associated with borderline glycemic control. There were no statistically significant relationships between glycemic control, self-reported general health, employment, and missed work. Finally, having an HbA1c value less than 6.5% was protective against mortality. The findings suggest that work-related factors are important in a person’s ability to reach optimal diabetes management levels. Poor glycemic control appears to have significant detrimental effects on HRQOL.

The issue of macroeconomic closure revisited and extended

Léon Walras (1874) already had realized that his neo-classical general equilibrium model could not accommodate autonomous investment. Sen analysed the same issue in a simple, one-sector macroeconomic model of a closed economy. He showed that fixing investment in the model, built strictly on neo-classical assumptions, would make the system overdetermined, thus, one should loosen some neo-classical condition of competitive equilibrium. He analysed three not neo-classical “closure options”, which could make the model well determined in the case of fixed investment. Others later extended his list and it showed that the closure dilemma arises in the more complex computable general equilibrium (CGE) models as well, as does the choice of adjustment mechanism assumed to bring about equilibrium at the macro level. By means of numerical models, it was also illustrated that the adopted closure rule can significantly affect the results of policy simulations based on a CGE model. Despite these warnings, the issue of macro closure is often neglected in policy simulations. It is, therefore, worth revisiting the issue and demonstrating by further examples its importance, as well as pointing out that the closure problem in the CGE models extends well beyond the problem of how to incorporate autonomous investment into a CGE model. Several closure rules are discussed in this paper and their diverse outcomes are illustrated by numerical models calibrated on statistical data. First, the analyses is done in a one-sector model, similar to Sen’s, but extended into a model of an open economy. Next, the same analyses are repeated using a fully-fledged multisectoral CGE model, calibrated on the same statistical data. Comparing the results obtained by the two models it is shown that although, using the same closure option, they generate quite similar results in terms of the direction and – to a somewhat lesser extent – of the magnitude of change in the main macro variables, the predictions of the multi-sectoral CGE model are clearly more realistic and balanced.

What's in it for us? A case study research about micro sense making processes through the eyes of front line employees, during culture change implementation

Abstract How employees make sense of change is a very complex process. Recently, academics have neglected to research sense making activities in a micro culture implementation context, through the eyes of front line employees. In contrast to a macro view, a micro perspective limits researchers to only look at an individual, departmental or group level. By doing so, we can zoom in on the details of sense making processes that employees use in their daily work life. A macro (organisational) view is based on the notion that there is a general integrated culture that can be found in all organisational units and departments. It is assumed that culture can be researched by using the entire organisation as one single research entity. This thesis challenges this assumption. In case of planned change it is usually the management community who are in charge of the change intervention. Because of their formal hierarchical position, they have the power to abort or initiate change programs. It is perhaps therefore that researchers tend to be focused on the management community rather than on lower level organisational members, such as front line employees. Apart from the micro view, scholars also neglected to research culture change implementation through the eyes of front line employees. This thesis is an attempt to fill these two gaps that currently exists in academic change management publications. The main research question is therefore: From a micro point of view how do front-line employees make sense of the impact of culture change, during the implementation phase? This thesis starts with a literature review which exposes the two main gaps. The most important outcome of this review is that only 2% of the research articles dealt with culture implementation, through the eyes of front line employees. A conceptual research model is built on the integrated sense making theory of Weber and Manning (2001) and the micro variables of Raelin and Cataldo (2011). These theories emphasize elements of sense making in a daily working context. It is likely that front line employees can identify themselves with research elements such as tasks, skills practices, involvement and behaviour. Front line employees were selected, because as lower level organisational members they are usually the change recipients. They are further away from the change initiating scene (usually the management of an organisation) and form a potential sense making ‘hotspot’ that could provide new academic insights. In order to carry out the primary research, two case organisations were selected in the leisure industry. A participative case study research method was chosen. This meant that the researcher worked in the concerning departments of the case organisations. The goal was to observe and interview front line employees, while they were performing their jobs. The most important advantage of this approach is that the researcher temporarily becomes one with the organisation and is therefore able to acquire both formal and informal narratives that front line employees use during sense making activities. It was found that front line employees make sense of organisational change by using a practical approach. They make sense of the change program by carrying out new tasks, developing new skills and sharing best practices. The most noticeable conclusion was that sense making activities predominantly take place at an individual level in relation to change acceptance. Organisational members tend to create a mental equation in order to weigh the advantages against the disadvantages. They evaluate whether the concerning change program is beneficial to them or not. For future research a sense making scheme model is suggested that is based on two methods: an introspection and an action method.

Bayesian Dependence Tests for Continuous, Binary and Mixed Continuous-Binary Variables

Tests for dependence of continuous, discrete and mixed continuous-discrete variables are ubiquitous in science. The goal of this paper is to derive Bayesian alternatives to frequentist null hypothesis significance tests for dependence. In particular, we will present three Bayesian tests for dependence of binary, continuous and mixed variables. These tests are nonparametric and based on the Dirichlet Process, which allows us to use the same prior model for all of them. Therefore, the tests are “consistent” among each other, in the sense that the probabilities that variables are dependent computed with these tests are commensurable across the different types of variables being tested. By means of simulations with artificial data, we show the effectiveness of the new tests.

Méthodes de Bootstrap pour les modèles à facteurs

Cette thèse développe des méthodes bootstrap pour les modèles à facteurs qui sont couram- ment utilisés pour générer des prévisions depuis l'article pionnier de Stock et Watson (2002) sur les indices de diffusion. Ces modèles tolèrent l'inclusion d'un grand nombre de variables macroéconomiques et financières comme prédicteurs, une caractéristique utile pour inclure di- verses informations disponibles aux agents économiques. Ma thèse propose donc des outils éco- nométriques qui améliorent l'inférence dans les modèles à facteurs utilisant des facteurs latents extraits d'un large panel de prédicteurs observés. Il est subdivisé en trois chapitres complémen- taires dont les deux premiers en collaboration avec Sílvia Gonçalves et Benoit Perron. Dans le premier article, nous étudions comment les méthodes bootstrap peuvent être utilisées pour faire de l'inférence dans les modèles de prévision pour un horizon de h périodes dans le futur. Pour ce faire, il examine l'inférence bootstrap dans un contexte de régression augmentée de facteurs où les erreurs pourraient être autocorrélées. Il généralise les résultats de Gonçalves et Perron (2014) et propose puis justifie deux approches basées sur les résidus : le block wild bootstrap et le dependent wild bootstrap. Nos simulations montrent une amélioration des taux de couverture des intervalles de confiance des coefficients estimés en utilisant ces approches comparativement à la théorie asymptotique et au wild bootstrap en présence de corrélation sérielle dans les erreurs de régression. Le deuxième chapitre propose des méthodes bootstrap pour la construction des intervalles de prévision permettant de relâcher l'hypothèse de normalité des innovations. Nous y propo- sons des intervalles de prédiction bootstrap pour une observation h périodes dans le futur et sa moyenne conditionnelle. Nous supposons que ces prévisions sont faites en utilisant un ensemble de facteurs extraits d'un large panel de variables. Parce que nous traitons ces facteurs comme latents, nos prévisions dépendent à la fois des facteurs estimés et les coefficients de régres- sion estimés. Sous des conditions de régularité, Bai et Ng (2006) ont proposé la construction d'intervalles asymptotiques sous l'hypothèse de Gaussianité des innovations. Le bootstrap nous permet de relâcher cette hypothèse et de construire des intervalles de prédiction valides sous des hypothèses plus générales. En outre, même en supposant la Gaussianité, le bootstrap conduit à des intervalles plus précis dans les cas où la dimension transversale est relativement faible car il prend en considération le biais de l'estimateur des moindres carrés ordinaires comme le montre une étude récente de Gonçalves et Perron (2014). Dans le troisième chapitre, nous suggérons des procédures de sélection convergentes pour les regressions augmentées de facteurs en échantillons finis. Nous démontrons premièrement que la méthode de validation croisée usuelle est non-convergente mais que sa généralisation, la validation croisée «leave-d-out» sélectionne le plus petit ensemble de facteurs estimés pour l'espace généré par les vraies facteurs. Le deuxième critère dont nous montrons également la validité généralise l'approximation bootstrap de Shao (1996) pour les regressions augmentées de facteurs. Les simulations montrent une amélioration de la probabilité de sélectionner par- cimonieusement les facteurs estimés comparativement aux méthodes de sélection disponibles. L'application empirique revisite la relation entre les facteurs macroéconomiques et financiers, et l'excès de rendement sur le marché boursier américain. Parmi les facteurs estimés à partir d'un large panel de données macroéconomiques et financières des États Unis, les facteurs fortement correlés aux écarts de taux d'intérêt et les facteurs de Fama-French ont un bon pouvoir prédictif pour les excès de rendement.

Méthodes de Bootstrap pour les modèles à facteurs

Cette thèse développe des méthodes bootstrap pour les modèles à facteurs qui sont couram- ment utilisés pour générer des prévisions depuis l'article pionnier de Stock et Watson (2002) sur les indices de diffusion. Ces modèles tolèrent l'inclusion d'un grand nombre de variables macroéconomiques et financières comme prédicteurs, une caractéristique utile pour inclure di- verses informations disponibles aux agents économiques. Ma thèse propose donc des outils éco- nométriques qui améliorent l'inférence dans les modèles à facteurs utilisant des facteurs latents extraits d'un large panel de prédicteurs observés. Il est subdivisé en trois chapitres complémen- taires dont les deux premiers en collaboration avec Sílvia Gonçalves et Benoit Perron. Dans le premier article, nous étudions comment les méthodes bootstrap peuvent être utilisées pour faire de l'inférence dans les modèles de prévision pour un horizon de h périodes dans le futur. Pour ce faire, il examine l'inférence bootstrap dans un contexte de régression augmentée de facteurs où les erreurs pourraient être autocorrélées. Il généralise les résultats de Gonçalves et Perron (2014) et propose puis justifie deux approches basées sur les résidus : le block wild bootstrap et le dependent wild bootstrap. Nos simulations montrent une amélioration des taux de couverture des intervalles de confiance des coefficients estimés en utilisant ces approches comparativement à la théorie asymptotique et au wild bootstrap en présence de corrélation sérielle dans les erreurs de régression. Le deuxième chapitre propose des méthodes bootstrap pour la construction des intervalles de prévision permettant de relâcher l'hypothèse de normalité des innovations. Nous y propo- sons des intervalles de prédiction bootstrap pour une observation h périodes dans le futur et sa moyenne conditionnelle. Nous supposons que ces prévisions sont faites en utilisant un ensemble de facteurs extraits d'un large panel de variables. Parce que nous traitons ces facteurs comme latents, nos prévisions dépendent à la fois des facteurs estimés et les coefficients de régres- sion estimés. Sous des conditions de régularité, Bai et Ng (2006) ont proposé la construction d'intervalles asymptotiques sous l'hypothèse de Gaussianité des innovations. Le bootstrap nous permet de relâcher cette hypothèse et de construire des intervalles de prédiction valides sous des hypothèses plus générales. En outre, même en supposant la Gaussianité, le bootstrap conduit à des intervalles plus précis dans les cas où la dimension transversale est relativement faible car il prend en considération le biais de l'estimateur des moindres carrés ordinaires comme le montre une étude récente de Gonçalves et Perron (2014). Dans le troisième chapitre, nous suggérons des procédures de sélection convergentes pour les regressions augmentées de facteurs en échantillons finis. Nous démontrons premièrement que la méthode de validation croisée usuelle est non-convergente mais que sa généralisation, la validation croisée «leave-d-out» sélectionne le plus petit ensemble de facteurs estimés pour l'espace généré par les vraies facteurs. Le deuxième critère dont nous montrons également la validité généralise l'approximation bootstrap de Shao (1996) pour les regressions augmentées de facteurs. Les simulations montrent une amélioration de la probabilité de sélectionner par- cimonieusement les facteurs estimés comparativement aux méthodes de sélection disponibles. L'application empirique revisite la relation entre les facteurs macroéconomiques et financiers, et l'excès de rendement sur le marché boursier américain. Parmi les facteurs estimés à partir d'un large panel de données macroéconomiques et financières des États Unis, les facteurs fortement correlés aux écarts de taux d'intérêt et les facteurs de Fama-French ont un bon pouvoir prédictif pour les excès de rendement.

Essays on mixed-frequency data : forecasting and unit root testing

Doutoramento em Economia

Macroeconomic determinants of contingent protection:the case of the European Union

Contingent Protection has grown to become an important trade restricting device. In the European Union, protection instruments like antidumping are used extensively. This paper analyses whether macroeconomic pressures may contribute to explain the variations in the intensity of antidumping protectionism in the EU. The empirical analysis uses count data models, applying various specification tests to derive the most appropriate specification. Our results suggest that the filing activity is inversely related to the macroeconomic conditions. Moreover, they confirm existing evidence for the US suggesting that domestic macroeconomic pressures are a more important determinant of contingent protection policy than external pressures.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.