"Tobit Model with Covariate Dependent Thresholds"

Tobit models are extended to allow threshold values which depend on individuals' characteristics. In such models, the parameters are subject to as many inequality constraints as the number of observations, and the maximum likelihood estimation which requires the numerical maximisation of the likelihood is often difficult to be implemented. Using a Bayesian approach, a Gibbs sampler algorithm is proposed and, further, the convergence to the posterior distribution is accelerated by introducing an additional scale transformation step. The procedure is illustrated using the simulated data, wage data and prime rate changes data.

Dynamic Field Experiments in Development Economics: Risk Valuation in Morocco, Kenya, and Peru

The effective design and implementation of interventions that reduce vulnerability and poverty require a solid understanding of underlying poverty dynamics and associated behavioral responses. Stochastic and dynamic benefit streams can make it difficult for the poor to learn the value of such interventions to them. We explore how dynamic field experiments can help (i) intended beneficiaries to learn and understand these complicated benefit streams, and (ii) researchers to better understand how the poor respond to risk when faced with nonlinear welfare dynamics. We discuss and analyze dynamic risk valuation experiments in Morocco, Peru, and Kenya.poverty, risk and uncertainty, dynamics, experiments, Kenya, Morocco, Peru, International Development, Research Methods/ Statistical Methods, Risk and Uncertainty,

BAYES' ESTIMATES OF THE DOUBLE HURDLE MODEL IN THE PRESENCE OF FIXED COSTS

We present a model of market adoption (participation) where the presence of non-negligible fixed costs leads to non-zero censoring of the traditional double-hurdle regression. Fixed costs arise due to household resources that must be devoted a priori to the decision to participate in the market. These costs-usually a cost of time-motivate two-step decision-making and focus attentions on the minimum-efficient scale of operations (the minimum amount of milk sales) at which market entry becomes viable. This focus, in turn, motivates a non-zero-censored Tobit regression estimated through routine application of Markov chain Monte Carlo Methods.market participation, fixed costs, double-hurdle model, censored regression., Financial Economics, O1, O11, C34, O13, Q16, D1,

Foreign Exchange Intervention in China

This thesis investigates the behaviour of foreign exchange intervention in China and its effects on the RMB’s exchange rate levels and volatility. The research first examines what drives Chinese central bank’s intervention through buying and selling foreign exchange (the CB intervention) in a bivariate probit model and shows that intervention is driven by an array of factors including exchange rate deviations, conditional volatility, national economic conditions, interest rate differentials. The PBOC conducts intervention in a leaning-against-the-wind fashion in the medium term, while leaning-with-the-wind intervention is used in the short term. The thesis next focuses on the intervention in the central parity rate (the CPR intervention). Evidence from a Bayes Tobit model shows that the CPR intervention is determined by the market price (proxied by the proposed price by designated market makers), broad currency index and the yield curve spread. The PBOC adopts a leaning-against-the-wind strategy for the intervention in that when the market price appreciates (depreciates), the PBOC sets a higher (lower) central parity rate to dampen or even reverse the appreciation (depreciation). To what extent the CB and CPR interventions are effective is then estimated in threshold GARCH models. Results show that while CPR intervention focuses on combating appreciation, intervention by the central bank’s purchase or sale operations (CB intervention) impacts on exchange rate levels when the RMB depreciates. While interventions would move exchange rate levels to the direction desired by the authorities, they tend to increase exchange rate volatility. Finally, event study methodology is deployed to explore the properties and impacts of China’s oral intervention. The estimation adopts four criteria (event, direction, reversal and smoothing) to test to what extent oral intervention is effective. Evidence indicates oral intervention through exchange rate communications can influence exchange rate levels and the RMB exchange rate is responsive to international pressure. Furthermore, sequential oral interventions can reduce exchange rate volatility

Sustainable land use pathway ranking and selection

This paper presents methodology for ranking and selecting sustainable ‘land-use pathways,’ arguing that the methodology is central to sustainable-land-use-policy prescriptions, providing essential innovation to assessments hitherto devoid of probabilistic foundation. Demonstrating routine implementation of Markov-Chain, Monte-Carlo procedure, ranking-and-selection enactment is widely disseminable and potentially valuable to land-use policy prescription. Application to a sample of Ethiopian-highlands, land-dependent households highlights empirical gains compared to conventional methodology. Applications and extensions that profit future land-use sustainability are discussed (68 words)

tobit models in strategy research critical issues and applications

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Tobit models in strategy research:Critical issues and applications

Research Summary: Tobit models have been used to address several questions in management research. Reviewing existing practices and applications, we discuss three challenges: (a) assumptions about the nature of data, (b) apparent interchangeability between censoring and selection bias, and (c) potential violations of key assumptions in the distribution of residuals. Empirically analyzing the relationship between import competition and industry diversification, we contrast Tobit models with results from other estimators and show the conditions that make Tobit a suitable empirical approach. Finally, we offer suggestions and guidelines on how to use Tobit models to deal with censored data in strategy research. Managerial Summary: Data on strategic decisions often exhibit certain features, such as excess zeros and values bounded within a given range, which complicate the use of linear econometric techniques. Deriving statistical evidence in such instances may suffer from biases that undermine managerial applications. Our study presents an extensive comparison of different econometric models to deal with censored data in strategic management showing the strengths and weaknesses of each model. We also conduct an application to the context of import penetration and industry diversification to highlight how the relationship between these two variables changes depending on the econometric model used for the analysis. In conclusion, we provide a set of recommendations for scholars interested in censored data

Natural disaster accounting bias and its equivalence across genetic resource stocks

Two sources of bias arise in conventional loss predictions in the wake of natural disasters. One source of bias stems from neglect of accounting for animal genetic resource loss. A second source of bias stems from failure to identify, in addition to the direct effects of such loss, the indirect effects arising from implications impacting animal-human interactions. We argue that, in some contexts, the magnitude of bias imputed by neglecting animal genetic resource stocks is substantial. We show, in addition, and contrary to popular belief, that the biases attributable to losses in distinct genetic resource stocks are very likely to be the same. We derive the formal equivalence across the distinct resource stocks by deriving an envelope result in a model that forms the mainstay of enquiry in subsistence farming and we validate the theory, empirically, in a World-Society-for-the-Protection-of-Animals applicatio

Estimation of a regression spline sample selection model

It is often the case that an outcome of interest is observed for a restricted non-randomly selected sample of the population. In such a situation, standard statistical analysis yields biased results. This issue can be addressed using sample selection models which are based on the estimation of two regressions: a binary selection equation determining whether a particular statistical unit will be available in the outcome equation. Classic sample selection models assume a priori that continuous regressors have a pre-specified linear or non-linear relationship to the outcome, which can lead to erroneous conclusions. In the case of continuous response, methods in which covariate effects are modeled flexibly have been previously proposed, the most recent being based on a Bayesian Markov chain Monte Carlo approach. A frequentist counterpart which has the advantage of being computationally fast is introduced. The proposed algorithm is based on the penalized likelihood estimation framework. The construction of confidence intervals is also discussed. The empirical properties of the existing and proposed methods are studied through a simulation study. The approaches are finally illustrated by analyzing data from the RAND Health Insurance Experiment on annual health expenditures