Modeling Site Specific Heterogeneity in an On-Site Stratified Random Sample of Recreational Demand

Using estimation of demand for the George Washington/Jefferson National Forest as a case study, it is shown that in a stratified/clustered on-site sample, latent heterogeneity needs to be accounted for twice: first to account for dispersion in the data caused by unobservability of the process that results in low and high frequency visitors in the population, and second to capture unobservable heterogeneity among individuals surveyed at different sites according to a stratified random sample (site specific effects). It is shown that both of the parameters capturing latent heterogeneity are statistically significant. It is therefore claimed in this paper, that the model accounting for site-specific effects is superior to the model without such effects. Goodness of fit statistics show that our empirical model is superior to models that do not account for latent heterogeneity for the second time. The price coefficient for the travel cost variable changes across model resulting in differences in consumer surplus measures. The expected mean also changes across different models. This information is of importance to the USDA Forest Service for the purpose of consumer surplus calculations and projections for budget allocation and resource utilization.Recreational Demand models, Clustering, Subject-specific effects, Truncated Stratified Negative Binomial Model, Overdispersion, Environmental Economics and Policy,

An Optimal Rule for Switching over to Renewable fuels with Lower Price Volatility: A Case of Jump Diffusion Process

This study investigates the optimal switching boundary to a renewable fuel when oil prices exhibit continuous random fluctuations along with occasional discontinuous jumps. In this paper, oil prices are modeled to follow jump diffusion processes. A completeness result is derived. Given that the market is complete the value of a contingent claim is risk neutral expectation of the discounted pay off process. Using the contingent claim analysis of investment under uncertainty, the Hamilton-Jacobi-Bellman (HJB) equation is derived for finding value function and optimal switching boundary. We get a mixed differential-difference equation which would be solved using numerical methods.Demand and Price Analysis, Resource /Energy Economics and Policy,

Modeling Demand for Outdoor Recreation Settings with Choice Based Data Accounting for Exogenous and Endogenous Stratification

Estimating regional demand models by pooling different samples without correcting for such differences causes model misspecification as each sample belongs to a different population. Weighted regression using Pseudolikelihood to account for differences in sample population with adjustment for heteroskedasticity improves efficiency but the estimates are biased. We estimate regional demand for National Forest settings types in the southeastern states of U.S using weighted and unweighted regression. Using estimation of demand for National Forests as a case study, we resolve problems relating to inference about the data generating process when different samples are pooled together. We show that though efficiency of weighted estimates improves after correcting for heteroskedasticity, they still remain biased as the weights interact with covariates to explain part of model misspecification. In this paper, we show that it is best to use unweighted regression including interactions with weights as covariates.exogenous stratification, endogenous stratification, choice based data, outdoor recreation, Environmental Economics and Policy, Q000, Q500,

Accounting for Geographic Heterogeneity in Recreation Demand Models

Spatial differences in site characteristics and user populations may result in heterogeneity of recreation preferences and values across geographic regions. Non-linear mixed effects models provide a potential means of accounting for this heterogeneity. This approach was tested by estimating a national-level recreation demand model with encouraging results.Resource /Energy Economics and Policy,

Reporting trends, practices, and resource utilization in neuroendocrine tumors of the prostate gland: a survey among thirty-nine genitourinary pathologists

Background: Neuroendocrine differentiation in the prostate gland ranges from clinically insignificant neuroendocrine differentiation detected with markers in an otherwise conventional prostatic adenocarcinoma to a lethal high-grade small/large cell neuroendocrine carcinoma. The concept of neuroendocrine differentiation in prostatic adenocarcinoma has gained considerable importance due to its prognostic and therapeutic ramifications and pathologists play a pivotal role in its recognition. However, its awareness, reporting, and resource utilization practice patterns among pathologists are largely unknown. Methods: Representative examples of different spectrums of neuroendocrine differentiation along with a detailed questionnaire were shared among 39 urologic pathologists using the survey monkey software. Participants were specifically questioned about the use and awareness of the 2016 WHO classification of neuroendocrine tumors of the prostate, understanding of the clinical significance of each entity, and use of different immunohistochemical (IHC) markers. De-identified respondent data were analyzed. Results: A vast majority (90%) of the participants utilize IHC markers to confirm the diagnosis of small cell neuroendocrine carcinoma. A majority (87%) of the respondents were in agreement regarding the utilization of type of IHC markers for small cell neuroendocrine carcinoma for which 85% of the pathologists agreed that determination of the site of origin of a high-grade neuroendocrine carcinoma is not critical, as these are treated similarly. In the setting of mixed carcinomas, 62% of respondents indicated that they provide quantification and grading of the acinar component. There were varied responses regarding the prognostic implication of focal neuroendocrine cells in an otherwise conventional acinar adenocarcinoma and for Paneth cell-like differentiation. The classification of large cell neuroendocrine carcinoma was highly varied, with only 38% agreement in the illustrated case. Finally, despite the recommendation not to perform neuroendocrine markers in the absence of morphologic evidence of neuroendocrine differentiation, 62% would routinely utilize IHC in the work-up of a Gleason score 5 + 5 = 10 acinar adenocarcinoma and its differentiation from high-grade neuroendocrine carcinoma. Conclusion: There is a disparity in the practice utilization patterns among the urologic pathologists with regard to diagnosing high-grade neuroendocrine carcinoma and in understanding the clinical significance of focal neuroendocrine cells in an otherwise conventional acinar adenocarcinoma and Paneth cell-like neuroendocrine differentiation. There seems to have a trend towards overutilization of IHC to determine neuroendocrine differentiation in the absence of neuroendocrine features on morphology. The survey results suggest a need for further refinement and development of standardized guidelines for the classification and reporting of neuroendocrine differentiation in the prostate gland

Modeling Site Specific Heterogeneity in an On-Site Stratified Random Sample of Recreational Demand

Using estimation of demand for the George Washington/Jefferson National Forest as a case study, it is shown that in a stratified/clustered on-site sample, latent heterogeneity needs to be accounted for twice: first to account for dispersion in the data caused by unobservability of the process that results in low and high frequency visitors in the population, and second to capture unobservable heterogeneity among individuals surveyed at different sites according to a stratified random sample (site specific effects). It is shown that both of the parameters capturing latent heterogeneity are statistically significant. It is therefore claimed in this paper, that the model accounting for site-specific effects is superior to the model without such effects. Goodness of fit statistics show that our empirical model is superior to models that do not account for latent heterogeneity for the second time. The price coefficient for the travel cost variable changes across model resulting in differences in consumer surplus measures. The expected mean also changes across different models. This information is of importance to the USDA Forest Service for the purpose of consumer surplus calculations and projections for budget allocation and resource utilization

Mainstreaming biodiversity into policy–Do the numbers add-up?

In this paper, we review the literature on economic approaches to providing value estimates of biodiversity. In most cases, the value estimates represent a lower estimate for a multitude of reasons, emanating from the complexity of definition and conceptualization of biodiversity as a capital stock and/or an ensuing flow of various ecosystem services. However, this exercise is important for accounting for these values in national income accounts, policy reforms, and raising funds for biodiversity financing. We discuss some of the theoretical and empirical challenges towards this valuation exercise along with providing estimates of biodiversity values

An Optimal Rule for Switching over to Renewable fuels with Lower Price Volatility: A Case of Jump Diffusion Process

This study investigates the optimal switching boundary to a renewable fuel when oil prices exhibit continuous random fluctuations along with occasional discontinuous jumps. In this paper, oil prices are modeled to follow jump diffusion processes. A completeness result is derived. Given that the market is complete the value of a contingent claim is risk neutral expectation of the discounted pay off process. Using the contingent claim analysis of investment under uncertainty, the Hamilton-Jacobi-Bellman (HJB) equation is derived for finding value function and optimal switching boundary. We get a mixed differential-difference equation which would be solved using numerical methods

Modeling Demand for Outdoor Recreation Settings with Choice Based Data Accounting for Exogenous and Endogenous Stratification

Estimating regional demand models by pooling different samples without correcting for such differences causes model misspecification as each sample belongs to a different population. Weighted regression using Pseudolikelihood to account for differences in sample population with adjustment for heteroskedasticity improves efficiency but the estimates are biased. We estimate regional demand for National Forest settings types in the southeastern states of U.S using weighted and unweighted regression. Using estimation of demand for National Forests as a case study, we resolve problems relating to inference about the data generating process when different samples are pooled together. We show that though efficiency of weighted estimates improves after correcting for heteroskedasticity, they still remain biased as the weights interact with covariates to explain part of model misspecification. In this paper, we show that it is best to use unweighted regression including interactions with weights as covariates

Accounting for Geographic Heterogeneity in Recreation Demand Models

Spatial differences in site characteristics and user populations may result in heterogeneity of recreation preferences and values across geographic regions. Non-linear mixed effects models provide a potential means of accounting for this heterogeneity. This approach was tested by estimating a national-level recreation demand model with encouraging results