USING CONTINGENT VALUATION WITH RESPONDENT UNCERTAINTY TO ESTIMATE THE COSTS OF CLIMATE CHANGE PROGRAMS: AN APPLICATION TO CANADIAN LANDOWNERS

Using a survey of western Canadian agricultural landowners, we examine the cost and viability of two distinct afforestation options for carbon-uptake purposes. Responses to two separate, but most-likely related willingness to accept compensation questions are elicited using the contingent valuation method. Respondents then select the level of certainty with which they believe their responses were given. This paper provides a framework for estimation of the bivariate model with certainty and a modification of the model to incorporate uncertainty based on Li and Mattson's approach to preference uncertainty. While highly preliminary results are given for the bivariate model with certainty, applications of both models will be presented at the 2003 AAEA Meetings.Environmental Economics and Policy, Resource /Energy Economics and Policy,

An original and additional mathematical model characterizing a Bayesian approach to decision theory

We propose an original mathematical model according to a Bayesian approach explaining uncertainty from a point of view connected with vector spaces. A parameter space can be represented by means of random quantities by accepting the principles of the theory of concordance into the domain of subjective probability. We observe that metric properties of the notion of -product mathematically fulfill the ones of a coherent prevision of a bivariate random quantity. We introduce fundamental metric expressions connected with transformed random quantities representing changes of origin. We obtain a posterior probability law by applying the Bayes’ theorem into a geometric context connected with a two-dimensional parameter space

How do households respond to uncertainty shocks?

Economic disruptions generally coincide with heightened uncertainty. In the United States, uncertainty increased sharply with the recent housing market crash, financial crisis, deep recession, and uneven recovery. In July 2010 Congressional testimony, Federal Reserve Chairman Bernanke described conditions as "unusually uncertain." The uncertain landscape was also cited as a factor in the slow recovery from the 2001 recession, when the March 2003 Federal Open Market Committee statement highlighted the "unusually large uncertainties" at the time. ; Uncertainty is a standard feature of most macroeconomic models, in which consumers and firms make decisions today based on expectations of an unknown (and hence uncertain) future. But in light of real-world events, economists have begun to think more critically about the role of uncertainty in the economy. Recent research has allowed the degree of uncertainty to vary over time and examined how these fluctuations affect business activity. The results have been mixed thus far, with some authors finding that fluctuations in uncertainty are a key factor in the business cycle, while others have found little such evidence. ; Knotek and Khan take a similar approach in studying levels of uncertainty that can vary over time, but they focus on household responses to changes in uncertainty. Because uncertainty can take many forms, they consider two measures of uncertainty, one based on references to uncertainty in newspaper articles and another derived from the stock market. ; While economic theory predicts sudden, sharp pullbacks of household purchases following increases in uncertainty, the empirical results suggest that household spending reductions are modest and may only appear after a considerable time has passed. In addition, movements in uncertainty account for only a small portion of the total fluctuations in household spending. These results suggest that variations in the amount of uncertainty--at least as they are commonly captured--do not appear to be a key factor driving household spending decisions and, in turn, economic weakness.

Family and parenting characteristics associated with marijuana use by Chilean adolescents

OBJECTIVE: Family involvement and several characteristics of parenting have been suggested to be protective factors for adolescent substance use. Some parenting behaviors may have stronger relationships with adolescent behavior while others may have associations with undesirable behavior among youth. Although it is generally acknowledged that families play an important role in the lives of Chilean adolescents, scant research exists on how different family and parenting factors may be associated with marijuana use and related problems in this population which has one of the highest rates of drug use in Latin America. METHODS: Using logistic regression and negative binomial regression, we examined whether a large number of family and parenting variables were associated with the possibility of Chilean adolescents ever using marijuana, and with marijuana-related problems. Analyses controlled for a number of demographic and peer-related variables. RESULTS: Controlling for other parenting and family variables, adolescent reports of parental marijuana use showed a significant and positive association with adolescent marijuana use. The multivariate models also revealed that harsh parenting by fathers was the only family variable associated with the number of marijuana-related problems youth experienced. CONCLUSION: Of all the family and parenting variables studied, perceptions of parental use of marijuana and harsh parenting by fathers were predictors for marijuana use, and the experience of marijuana-related problems. Prevention interventions need to continue emphasizing the critical socializing role that parental behavior plays in their children's development and potential use of marijuana.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3109755/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3109755/Accepted manuscrip

Real effects of inflation uncertainty in the US

We empirically investigate the effects of inflation uncertainty on output growth for the US using both monthly and quarterly data over 1985-2009. Employing a Markov regime switching approach to model output dynamics, we show that inflation uncertainty obtained from a Markov regime switching GARCH model exerts a negative and regime dependant impact on output growth. In particular, we show that the negative impact of inflation uncertainty on output growth is almost 4.5 times higher during the low growth regime than that during the high growth regime. We verify the robustness of our findings using quarterly data

Jointness of Growth Determinants

Model uncertainty arises from uncertainty about correct economic theories, data issues and empirical specification problems. This paper investigates mutual dependence or jointness among variables in explaining the dependent variable. Jointness departs from univariate measures of variable importance, while addressing model uncertainty and allowing for generally unknown forms of dependence. Positive jointness implies that regressors are complements, representing distinct, but interacting economic factors. Negative jointness implies that explanatory variables are substitutes and act as proxies for a similar underlying mechanism. In a cross-country dataset, we show that jointness among 67 determinants of growth is important, ffecting inference and economic policy

CopulaDTA: An R Package for Copula Based Bivariate Beta-Binomial Models for Diagnostic Test Accuracy Studies in a Bayesian Framework

The current statistical procedures implemented in statistical software packages for pooling of diagnostic test accuracy data include hSROC regression and the bivariate random-effects meta-analysis model (BRMA). However, these models do not report the overall mean but rather the mean for a central study with random-effect equal to zero and have difficulties estimating the correlation between sensitivity and specificity when the number of studies in the meta-analysis is small and/or when the between-study variance is relatively large. This tutorial on advanced statistical methods for meta-analysis of diagnostic accuracy studies discusses and demonstrates Bayesian modeling using CopulaDTA package in R to fit different models to obtain the meta-analytic parameter estimates. The focus is on the joint modelling of sensitivity and specificity using copula based bivariate beta distribution. Essentially, we extend the work of Nikoloulopoulos by: i) presenting the Bayesian approach which offers flexibility and ability to perform complex statistical modelling even with small data sets and ii) including covariate information, and iii) providing an easy to use code. The statistical methods are illustrated by re-analysing data of two published meta-analyses. Modelling sensitivity and specificity using the bivariate beta distribution provides marginal as well as study-specific parameter estimates as opposed to using bivariate normal distribution (e.g., in BRMA) which only yields study-specific parameter estimates. Moreover, copula based models offer greater flexibility in modelling different correlation structures in contrast to the normal distribution which allows for only one correlation structure.Comment: 26 pages, 5 figure

Do preceding questions influence the reporting of childbearing intentions in social surveys?

For demographers fertility intentions are a long standing source of both interest and scepticism. Scepticism has been expressed because fertility intentions regularly fail to precisely predict fertility and because they are liable to change across the life course. Here we demonstrate an additional consideration: simply changing the questions that precede fertility intentions questions can have a significant influence on responses. We illustrate this risk using a series of randomised experiments with different preceding questions; first, on mortality and risk in two convenience samples of UK undergraduate students. Secondly, we will present provisional results from a ground-breaking longitudinal experiment where the manipulated preceding questions are on close family and friends. As far as we are aware this later study is the first time that question ordering experiment looking at fertility intentions has been embedded in a representative survey, and the first longitudinal measurement of preceding-question effects using the same individuals

Simple Computational Methods for Measuring the Difference of Empirical Distributions: Application to Internal and External Scope Tests in Contingent Valuation

This paper develops a statistically unbiased and simple method for measuring the difference of independent empirical distributions estimated by bootstrapping or other simulation approaches. This complete combinatorial method is compared with other unbiased and biased methods that have been suggested in the literature, first in Monte Carlo simulations and then in a field test of external and internal scope testing in contingent valuation. Tradeoffs between methods are discussed. When the empirical distributions are not independent a straightforward difference test is suggested.Research Methods/ Statistical Methods,

On association in regression: the coefficient of determination revisited

Universal coefficients of determination are investigated which quantify the strength of the relation between a vector of dependent variables Y and a vector of independent covariates X. They are defined as measures of dependence between Y and X through theta(x), with theta(x) parameterizing the conditional distribution of Y given X=x. If theta(x) involves unknown coefficients gamma the definition is conditional on gamma, and in practice gamma, respectively the coefficient of determination has to be estimated. The estimates of quantities we propose generalize R^2 in classical linear regression and are also related to other definitions previously suggested. Our definitions apply to generalized regression models with arbitrary link functions as well as multivariate and nonparametric regression. The definition and use of the proposed coefficients of determination is illustrated for several regression problems with simulated and real data sets