scholarly journals Estimating Recreational Value of Foy's Lake: An Application of Travel Cost Count Data Model for Truncated Zeros

2017 ◽  
Author(s):  
Md. Touhidul Alam ◽  
Anis-Ul-Ekram Chowdury ◽  
Md. Sajib Hossian
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Data Model ◽  
Poisson Regression ◽  
Consumer Surplus ◽  
Travel Cost ◽  
Travel Cost Method ◽  
Poisson Regression Model ◽  
Recreational Value ◽  
Recreational Benefits

To estimate the recreational value provided by the Foy’s Lake annually using the most applicable model for on-site data is the main objective of this study. Adhere to the objective of this study; Individual Travel Cost Method (ITCM) has been applied and Zero Truncated Poisson Regression Model has been found plausible among other models to estimate consumer surplus. Based on the findings of the study, an estimate of the consumer surplus or recreational benefits per trip per visitor can be recommended as BDT 5,875 or US $ 73.44 and counting the consumer surplus per trip per visitor, the annual recreational value (total consumer surplus) provided by the lake is found to be BDT 321 million or US $ 40.2 million.

scholarly journals Recreational benefits of wetlands: a survey on the Dal Lake in Jammu and Kashmir of India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Showkat Ahmad Shah ◽  
Md. Saiful Islam
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Consumer Surplus ◽  
Primary Data ◽  
Poisson Regression Model ◽  
Jammu And Kashmir ◽  
Content Type ◽  
Dal Lake ◽  
Recreational Services ◽  
Recreational Benefits

PurposeA wetland is a place of tourist attraction, and tourism values play a key role in economic development. Among various services provided by a wetland, recreational services are increasingly valuable in the tourism sector. This paper aims to unfold the potential recreational values of the Dal Lake in Jammu and Kashmir, India.Design/methodology/approachThe study uses individual travel cost methods (TCMs) and assesses its impact on regional development in terms of income and employment generation. A sample of 200 tourists is selected through an on-site survey on Dal Lake, and the demand for recreational visits and its value is estimated by employing the truncated Poisson regression model (TPRM) and un-truncated Poisson regression model (UTPRM). The consumers' surplus is estimated and tourists' benefit to visiting the wetland is explored.FindingsOn average, estimated consumers' surplus per visitor is Rs 6,250 (US$96.15) and Rs 25,000 (US$384.61) from respective models. The annual total recreational value of the lake is accounted for Rs 1713m (US$ 26m). This high consumer surplus (CS) and recreational values of the lake indicate large demand for its recreational facilities.Originality/valueThe study is based on primary data and thus, is original. The paper has implications for the policymakers to formulate sustainable management plans for the proper use of Dal Lake and tourism development.

Analysis of The Mixed Effects Regression Model for Clustered Count Response Data

2021 ◽  
Author(s):  
Aragaw Eshetie Aguade ◽  
B.Muniswamy Begari
Keyword(s):  
Drinking Water ◽  
Regression Model ◽  
Count Data ◽  
Data Model ◽  
Poisson Regression ◽  
Score Test ◽  
Poisson Model ◽  
Poisson Regression Model ◽  
Response Data ◽  
Count Response

Abstract BackgroundThe Poisson regression model is useful for analyse count data, but, when the observations are correlated the Poisson estimate will be biased. Whereas, when the over-dispersion and heterogeneity problems occur the imposition of the Poisson model underestimate the standard error and overestimate the significance of the regression parameters. Therefore, the objective of this paper was to develop a test statistic to model and predict clustered count response data via the application and simulation data.MethodsThis paper concentrated on the clustered count data model to take into account heterogeneity. Accordingly, we developed a score test based on the multilevel Poisson model for testing heterogeneity with the alternative Poisson regression model. In addition, for the model application, we used the EDHS children`s data. Therefore, to evaluate the proposed model, we used both simulation and application data.ResultsSimulation results showed that the proposed score test has high power to predict and used to control heterogeneity between groups. Oromia, Amhara, and SNNPR are among the regions with the highest child mortality rates (Table 1). The results indicated that women who made marriage a mean age of 16 years and gave birth to the first child a mean age of 18 years and 8 months. Table 1 showed that 81% of all child deaths have recorded in rural areas. 78% of child families were illiterate, as a result, 75% of children don't have access to latrines and drinking water. Rivers and open-source waters are the common sources of drinking water, which comprised 79% of the total water supply. Therefore, from the research finding, it is possible to conclude that most child mortality is due to scarcity of water.ConclusionThe Power of test estimates indicated that the proposed method was better than the existing models. All covariant and dummy explanatory variables have a significant effect on the deaths of children. Hence, the multilevel Poisson model results indicated that there exists high variability among regions for the deaths of children. Therefore, this work suggested that the applications of the random-effects model provided a simple and robust means to predict the count response data model.

scholarly journals Analysis of  the Mixed Effects Regression Model for Clustered Count Response Data

2020 ◽  
Author(s):  
Aragaw Eshetie Aguade ◽  
Muniswamy Begari
Keyword(s):  
Drinking Water ◽  
Regression Model ◽  
Count Data ◽  
Data Model ◽  
Poisson Regression ◽  
Score Test ◽  
Poisson Model ◽  
Poisson Regression Model ◽  
Response Data ◽  
Count Response

Abstract Background The Poisson regression model is useful to analyze count data, but, when the observations are correlated the Poisson estimate will be biased. Whereas, when the over-dispersion and heterogeneity problems occur the imposition of the Poisson model underestimate the standard error and overestimate the significance of the regression parameters. Therefore, the objective of this paper was to develop a test statistic to model and predict clustered count response data via application and simulation data. Methods This paper concentrated on the clustered count data model to take into account heterogeneity. Accordingly, we developed a score test based on the multilevel Poisson model for testing heterogeneity with the alternative Poisson regression model. In addition, for the model application, we used the EDHS children`s data. Therefore, to evaluate the proposed model, we used both simulation and application data. Results Simulation results showed that the proposed score test has high power to predict and used to control heterogeneity between groups. Oromia, Amhara, and SNNPR are among the regions with the highest child mortality rates (Table 1). The results indicated that women who made marriage a mean age of 16 years and gave birth for the first child a mean age of 18 years and 8 months. Table 1 showed that 81% of all child deaths have recorded in rural areas. 78% of child families were illiterate, as a result, 75% of children don't have access to latrines and drinking water. Rivers and open-sources waters are the common sources of drinking water which comprised 79% of the total water supply. Therefore, from the research finding, it is possible to conclude that most child mortality is due to scarcity of water. Conclusion The Power of test estimates indicated that the proposed method was better than the existing models. All covariant and dummy explanatory variables have a significant effect on the deaths of children. Hence, the multilevel Poisson model results indicated that there exist high variability among regions for the deaths of children. Therefore, this work suggested that the applications of the random-effects model provided a simple and robust means to predict the count response data model.

A multivariate Poisson regression model for count data

2021 ◽  
pp. 1-17
Author(s):  
J. M. Muñoz-Pichardo ◽  
R. Pino-Mejías ◽  
J. García-Heras ◽  
F. Ruiz-Muñoz ◽  
M. Luz González-Regalado
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Poisson Regression Model

PARAMETER ESTIMATION ON HURDLE POISSON REGRESSION MODEL WITH CENSORED DATA

2012 ◽  
Vol 57 (1) ◽  
Author(s):  
SEYED EHSAN SAFFAR ◽  
ROBIAH ADNAN ◽  
WILLIAM GREENE
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Goodness Of Fit ◽  
Poisson Model ◽  
Likelihood Method ◽  
Poisson Regression Model ◽  
Response Variable ◽  
The Mean ◽  
Over Dispersion

A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the same as before. In fact, the variance value of the dependent variable will be much more than the mean value of the dependent variable and this is called over–dispersion. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, it is suggested to use a hurdle Poisson regression model to overcome over–dispersion problem. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle Poisson regression model is introduced on count data with many zeros. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness–of–fit for the regression model is examined. We study the effects of right censoring on estimated parameters and their standard errors via an example.

scholarly journals An economic evaluation of a forest park using the individual travel cost method (a case study of Ghaleh Rudkhan forest park in northern Iran)

2018 ◽  
Vol 6 (2) ◽  
pp. 48-55 ◽  
Author(s):  
Solmaz Zandi ◽  
Soleiman Mohammadi Limaei ◽  
Neda Amiri
Keyword(s):  
Ecosystem Services ◽  
Economic Value ◽  
Market Price ◽  
Consumer Surplus ◽  
Travel Cost ◽  
Travel Cost Method ◽  
Northern Iran ◽  
Forest Park ◽  
Recreational Value ◽  
The Individual

AbstractThe true economic value of ecosystem services may not be reflected in market transactions, because there is no real transaction for ecosystem services in the market. Therefore, it is important to evaluate the cost of time and travel to define the value people place on something in the absence of a market price. This study estimates the recreational value of Ghaleh Rudkhan forest park in the north of Iran using the individual travel cost method. This method is considered to be a substitute approach for the market. The data required were collected using questionnaires. Therefore, 271 questionnaires were randomly distributed between the visitors of the recreational site in 2016. In this study, a linear function is used to estimate the effects of explanatory variables including economic and social variables on the number of visits to estimate the recreational value of the forest park. Results showed that a consumer surplus of each person for their visit was 21500 Rials and the annual recreational value of the park was 78390595 Rials per ha. Furthermore, the variables such as travel expenses, income, distance, family size and visitor’s age are effective factors in the recreational use of the park. The results of this study can improve the quality of environmental services of the Ghaleh Rudkhan forest park and could expand the variety of services that they could supply based on the demand of the people.

scholarly journals Testing Significance of Multi-Destination and Multi-Purpose Trip Effects in a Travel Cost Method Demand Model for Whale Watching Trips

2000 ◽  
Vol 29 (2) ◽  
pp. 183-191 ◽  
Author(s):  
John Loomis ◽  
Shizuka Yorizane ◽  
Douglas Larson
Keyword(s):  
Willingness To Pay ◽  
Count Data ◽  
Cost Model ◽  
Consumer Surplus ◽  
Travel Cost ◽  
Travel Cost Method ◽  
Demand Model ◽  
Generalized Model ◽  
Travel Cost Model ◽  
Recreation Site

Inclusion of multi-destination and multi-purpose visitors has an appreciable influence on a standard count data travel cost model derived estimate of willingness to pay but the differences are not statistically significant. We adapt a more general travel cost model (TCM) of Parsons and Wilson (1997) that allows for inclusion of multi-destination visitors as incidental demand to allow estimation of an unbiased measure of single and multi-destination willingness to pay for whale viewing using a single pooled equation. The primary purpose trip values from the standard TCM and simple generalized TCM model are identical at $43 per person per day and neither are significantly different from the $50 day value from a generalized model that distinguishes between joint and incidental trips. The general models avoid underestimation of total recreation site benefits that would result from omitting the consumer surplus of multi-destination visitors.

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