A Statistical Model of Abstention under Compulsory Voting

Invalid voting and electoral absenteeism are two important sources of abstention in compulsory voting systems. Previous studies in this area have not considered the correlation between both variables and ignored the compositional nature of the data, potentially leading to unfeasible results and discarding helpful information from an inferential standpoint. In order to overcome these problems, this paper develops a statistical model that accounts for the compositional and hierarchical structure of the data and addresses robustness concerns raised by the use of small samples that are typical in the literature. The model is applied to analyze invalid voting and electoral absenteeism in Brazilian legislative elections between 1945 and 2006 via MCMC simulations. The results show considerable differences in the determinants of both forms of non-voting; while invalid voting was strongly positively related both to political protest and to the existence of important informational barriers to voting, the influence of these variables on absenteeism is less evident. Comparisons based on posterior simulations indicate that the model developed in this paper fits the dataset better than several alternative modeling approaches and leads to different substantive conclusions regarding the effect of different predictors on the both sources of abstention

Financial Stress, Family Conflict, and Youths' Successful Transition to Adult Roles

We analyze the effect of mothers' and youths' reports of family financial stress and conflict on youths' transitions into adult roles. We find that mothers’ reports of financial stresses and borrowing constraints are associated with earlier transitions to inactivity and public assistance, while youth reports of financial stresses are associated with earlier nest-leaving. Youths reporting conflict with parents leave school and move out earlier than their peers, while conflict between parents is associated with youth making later transitions. Overall, financial stress and conflict have independent effects on youths' transitions and youths' perspectives have different consequences to those of their mothers.youths, financial stress, family conflict

Volatility Dynamics in Foreign Exchange Rates: Further Evidence from the Malaysian Ringgit and Singapore Dollar

Singapore dollar are analyzed in this paper. Our approach can simultaneously capture the empirical regularities of persistent and asymmetric effects in volatility and timevarying correlations of financial time series. Consistent with the results of Tse and Tsui (1997), there is only some weak support for asymmetric volatility in the case of the Malaysian ringgit when the two currencies are measured against the US dollar. However, there is strong evidence that depreciation shocks have a greater impact on future volatility levels compared with appreciation shocks of the same magnitude when both currencies measured against the yen. Moreover, evidence of time-varying correlation is highly significant when both currencies are measured against the yen. Regardless of the choice of the numeraire currency and the volatility models, shocks to exchange rate volatility are found to be significantly persistent.Constant correlations; Exchange rate volatility; Fractional integration; Long memory; Bivariate asymmetric GARCH; Varying correlations

Does Beta React to Market Conditions? Estimates of Bull and Bear Betas using a Nonlinear Market Model with an Endogenous Threshold Parameter

We apply a logistic smooth transition market model (LSTM) to a sample of returns on Australian industry portfolios to investigate whether bull and bear market betas differ. Unlike other studies, our LSTM model allows for smooth transition between bull and bear states and allows the data to determine the threshold value. The estimated value of the smoothness parameter was very large for all industries implying that transition is abrupt. Therefore we estimated the threshold as a parameter along with the two betas in a dual beta market (DBM) framework using a sequential conditional least squares (SCLS) method. Using Lagrange Multiplier type tests of linearity, and the SCLS method our results indicate that for all but two industries the bull and bear betas are significantly different.Logistic Smooth Transition Market Model (LSTM); Sequential Conditional Least Squares (SCLS); Linearity Tests; Bull/Bear Betas

Financial Stress, Family Conflict, and Youths’ Successful Transition to Adult Roles

We analyze the effect of mothers’ and youths’ reports of family financial stress and conflict on youths’ transitions into adult roles. We find that mothers’ reports of financial stresses and borrowing constraints are associated with earlier transitions to inactivity and public assistance, while youth reports of financial stresses are associated with earlier nest-leaving. Youths reporting conflict with parents leave school and move out earlier than their peers, while conflict between parents is associated with youth making later transitions. Overall, financial stress and conflict have independent effects on youths’ transitions and youths’ perspectives have different consequences to those of their mothers.youths, financial stress, family conflict

Carry Trades: Betting Against Safe Haven

We examine contagion and flight-to-quality phenomena implied by carry strategies. More specifically, we analyze correlation dynamics between returns on a global equity index and returns on an investment strategy with a long position in high-yield and a short position in low-yield markets. Modeling information spillovers in a multivariate GARCH framework reveals that correlation increases considerably in response to a negative stock market shock. Moreover, a test for symmetry in exceedance correlation shows that correlation is indeed significantly larger for joint market downturns as opposed to joint market upturns. Our findings suggest that conditional correlation exposes carry traders to a severe diversification meltdown in times of global stock market crises.Carry trades, contagion, multivariate GARCH, exceedance correlation

Time-varying Beta Risk of Pan-European Sectors: A Comparison of Alternative Modeling Techniques

This paper investigates the time-varying behavior of systematic risk for eighteen pan-European industry portfolios. Using weekly data over the period 1987-2005, three different modeling techniques in addition to the standard constant coefficient model are employed: a bivariate t- GARCH(1,1) model, two Kalman filter based approaches as well as a bivariate stochastic volatility model estimated via the efficient Monte Carlo likelihood technique. A comparison of the different models' ex- ante forecast performances indicates that the random-walk process in connection with the Kalman filter is the preferred model to describe and forecast the time-varying behavior of sector betas in a European context.Time-varying beta risk; Kalman filter; bivariate t-GARCH; stochastic volatility; efficient Monte Carlo likelihood; European industry portfolios

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

On the Negative Relationship between Labor Income Uncertainty and Homeownership: Risk Aversion vs. Credit Constraints

In this paper we test for the first time whether the driving force behind the negative effect of income uncertainty on owner-occupancy propensities is risk aversion or credit constraints. To disentangle this puzzle we estimate reduced form equations using Italian data. Consistent with the previous empirical evidence in the US, our results confirm that in Italy both labor income uncertainty and credit constraints exert a significant negative effect on the probability of homeownership. However, our main findings indicate that the negative relationship between labor income uncertainty and homeownership is driven by households’ risk aversion.Homeownership,income uncertainty,credit constraints

Exchange Rate Uncertainty and Trade Growth - A Comparison of Linear and Nonlinear (Forecasting) Models

A huge body of empirical and theoretical literature has emerged on the relationship between foreign exchange (FX) uncertainty and international trade. Empirical findings about the impact of FX uncertainty on trade figures are at best weak and often ambiguous with respect to its direction. Almost all empirical contributions assume and estimate a linear relationship. Possible nonlinearity or state dependence of causal links between FX uncertainty and trade has been mostly ignored yet. In addition, widely used regression models have not been evaluated in terms of ex-ante forecasting. In this paper we analyze the impact of FX uncertainty on sectoral categories of multilateral exports and imports for 15 industrialized economies. We particularly provide a comparison of linear and nonlinear models with respect to ex-ante forecasting. In terms of average ranks of absolute forecast errors nonlinear models outperform both, a common linear model and some specification building on the assumption that FX uncertainty and trade growth are uncorrelated. Our results support the view that the relationship of interest might be nonlinear and, moreover, lacks of homogeneity across countries, economic sectors and when contrasting imports vs. exports.Exchange Rate Uncertainty, GARCH, Forecasting, International Trade, Nonlinear Models.