Handling unobserved site characteristics in random utility models of recreation demand

Murdock, Jennifer

doi:10.1016/j.jeem.2005.04.003

Cited by 134 publications

(101 citation statements)

References 15 publications

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“…In this paper we develop a Bayesian approach to estimating a RUM model that incorporates a full set of alternative specific constants, insulating the key travel cost parameter from the influence of the unobserved site attributes. In contrast to estimation procedures recently outlined in Murdock (2006), the posterior simulator we propose (combining data augmentation and Gibbs sampling techniques) can be used in the more general mixed logit framework in which some parameters of the conditional utility function are random. Following a series of generated data experiments to illustrate the performance of the simulator, we apply the estimation procedures to data from the Iowa Lakes Project.…”

Section: Introductionmentioning

confidence: 99%

“…In an important recent article, Murdock [21] provides a resolution to both problems. Drawing on 3 There are, of course, exceptions.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, it cannot be used to obtain maximum likelihood parameter estimates if the RUM model includes random parameters (the so-called mixed logit model). 5,6 Unfortunately, the mean-fitting feature of maximum likelihood estimation, which emerges from the standard logit model and underlies the insight behind Mudock's [21] approach, does not hold once random parameters are introduced. 7 Without this feature, the alternative specific constants obtained by the contraction mapping routine no longer solve the standard first order conditions implied by maximum likelihood estimation.…”

Section: Introductionmentioning

confidence: 99%

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Controlling for Observed and Unobserved Site Characteristics in RUM Models of Recreation Demand

Abidoye

Herriges

Tobias

2012

American J Agri Economics

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Random Utility Maximization (RUM) models of recreation demand are typically plagued by limited information on environmental and other attributes characterizing the available sites in the choice set. To the extent that these unobserved site attributes are correlated with the observed characteristics and/or the key travel cost variable, the resulting parameter estimates and subsequent welfare calculations are likely to be biased. In this paper we develop a Bayesian approach to estimating a RUM model that incorporates a full set of alternative specific constants, insulating the key travel cost parameter from the influence of the unobserved site attributes. In contrast to estimation procedures recently outlined in Murdock (2006), the posterior simulator we propose (combining data augmentation and Gibbs sampling techniques) can be used in the more general mixed logit framework in which some parameters of the conditional utility function are random. Following a series of generated data experiments to illustrate the performance of the simulator, we apply the estimation procedures to data from the Iowa Lakes Project. In contrast to an earlier study using the same data (Egan et al. [7]), we find that, with the addition of a full set of alternative specific constants, water quality attributes no longer appear to influence the choice of where to recreate. Abstract Random Utility Maximization (RUM) models of recreation demand are typically plagued by limited information on environmental and other attributes characterizing the available sites in the choice set. To the extent that these unobserved site attributes are correlated with the observed characteristics and/or the key travel cost variable, the resulting parameter estimates and subsequent welfare calculations are likely to be biased. In this paper we develop a Bayesian approach to estimating a RUM model that incorporates a full set of alternative specific constants, insulating the key travel cost parameter from the influence of the unobserved site attributes. In contrast to estimation procedures recently outlined in Murdock [21], the posterior simulator we propose (combining data augmentation and Gibbs sampling techniques) can be used in the more general mixed logit framework in which some parameters of the conditional utility function are random. Following a series of generated data experiments to illustrate the performance of the simulator, we apply the estimation procedures to data from the Iowa Lakes Project. In contrast to an earlier study using the same data (Egan et al. [7]), we find that, with the addition of a full set of alternative specific constants, water quality attributes no longer appear to influence the choice of where to recreate.

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Section: Introductionmentioning

confidence: 99%

“…In an important recent article, Murdock [21] provides a resolution to both problems. Drawing on 3 There are, of course, exceptions.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Controlling for Observed and Unobserved Site Characteristics in RUM Models of Recreation Demand

Abidoye

Herriges

Tobias

2012

American J Agri Economics

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“…do not appear directly in (1), but rather are subsumed in the α j 's (i.e., the alternative specific constants). As has been noted elsewhere (e.g., Murdock (2006) and Abidoye, Herriges and Tobias (2010)), including a full set of alternative specific constants controls for both observable and unobservable site attributes and insulates the travel cost parameter from potential omitted variables bias stemming from unobserved site attributes. In Murdock (2006), the site attributes are linked to the alternative specific constants using a secondary regression.…”

Section: Basic Structurementioning

confidence: 94%

“…As has been noted elsewhere (e.g., Murdock (2006) and Abidoye, Herriges and Tobias (2010)), including a full set of alternative specific constants controls for both observable and unobservable site attributes and insulates the travel cost parameter from potential omitted variables bias stemming from unobserved site attributes. In Murdock (2006), the site attributes are linked to the alternative specific constants using a secondary regression. Similar to Abidoye, Herriges and Tobias (2010), we use a hierarchical structure, described in Section 3.2 below, to capture the impact of site attributes on the α j 's.…”

Section: Basic Structurementioning

confidence: 94%

Model Uncertainty in Characterizing Recreation Demand

Abidoye

Herriges

2012

Environ Resource Econ

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A Bayesian variable selection procedure is used to control for uncertainty in the specification of a recreational demand model. Specifically, we propose a model that draws on the Bayesian paradigm to integrate the variable selection process into the model and reflect the accompanying uncertainty about which is the "best" specification used for counterfactual predictions. The advantage of this procedure over previous non-Bayesian approaches is that overcomes the problem of pre-testing in specification searches. Keywords October 8, 2010Abstract A Bayesian variable selection procedure is used to control for uncertainty in the specification of a recreational demand model. In contrast to comparing models based on the likelihood values with unknown sampling properties (as in, e.g., Egan et al, 2009), we propose a model that draws on the Bayesian paradigm to integrate the variable selection process into the model and reflect the accompanying uncertainty about which is the "best" specification used for counterfactual predictions.

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Valuing recreational fishing quality at rivers and streams

et al. 2015

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This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence-absence indicators or average self-reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008-2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100-200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios.

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Handling unobserved site characteristics in random utility models of recreation demand

Cited by 134 publications

References 15 publications

Controlling for Observed and Unobserved Site Characteristics in RUM Models of Recreation Demand

Controlling for Observed and Unobserved Site Characteristics in RUM Models of Recreation Demand

Model Uncertainty in Characterizing Recreation Demand

Valuing recreational fishing quality at rivers and streams

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