Kuhn-Tucker Estimation of Recreation Demand – A Study of Temporal Stability

The paper examines the Kuhn Tucker model in the context of estimating recreation demand when the choice set it very large. It examines the temporal stability of parameter estimates using multiple years of data on trips to 127 lakes in Iowa made by households in Iowa. The study finds that for the given dataset, the estimates derived from a Kuhn-Tucker model are largely stable over time.Recreation demand, Kuhn-Tucker, Temporal Stability, Environmental Economics and Policy, C2, Q2,

Capturing Preferences Under Incomplete Scenarios Using Elicited Choice Probabilities.

Manski (1999) proposed an approach for dealing with a particular form respondent uncertainty in discrete choice settings, particularly relevant in survey based research when the uncertainty stems from the incomplete description of the choice scenarios. Specifically, he suggests eliciting choice probabilities from respondents rather than their single choice of an alternative. A recent paper in IER by Blass et al. (2010) further develops the approach and presents the first empirical application. This paper extends the literature in a number of directions, examining the linkage between elicited choice probabilities and the more common discrete choice elicitation format. We also provide the first convergent validity test of the elicited choice probability format vis-\`a-vis the standard discrete choice format in a split sample experiment. Finally, we discuss the differences between welfare measures that can be derived from elicited choice probabilities versus those that can obtained from discrete choice responses.discrete choice; Elicited Choice Probabilities

Three essays in economics of the environment

The dissertation titled ``Three essays in economics of the environment" consists of two empirical essays and one theoretical essay. The first essay has two objectives. First, it offers a method to estimate a Kuhn-Tucker model allowing for correlation in preferences over time. The method is implemented to present the first set of estimates of a Kuhn-Tucker model with a large choice set using a four-year panel data set for close to 1200 households. Second, it uses the results to examine the stability of preferences over time. It is shown for the dataset used(drawn from the Iowa Lakes Project) and a simple specification of the Kuhn-Tucker model, preferences are not stable over time. This has implications for policy evaluation. If preferences are not stable over time, cost benefit analysis of policy relying on a single year of data may be misleading. The second essay seeks to examine if the source of the regression error in a highly non-linear Kuhn-Tucker model of recreation demand makes a material difference to welfare measures from hypothetical changes in one or more site quality attributes. Using compensating and equivalent variation computed with two different interpretations of the error term, it concludes that the source of the error does make a difference to the welfare estimates calculated. The third essay introduces green consumerism in an otherwise-standard neoclassical growth model and uses it to study the pollution-growth nexus. Green behavior is modeled by assuming that private agents derive a warm glow from incurring costly, pollution-mitigating expenditures whose effect on pollution is not internalized. Pollution reduces the attractiveness of old-age consumption by reducing the marginal utility from old-age consumption. It is shown that, relative to the competitive outcome, the planner allocates less to both young and old-age consumption, but uses these freed-up resources to finance more pollution-mitigation expenses. The result is lower pollution and lower capital than what the market would have chosen.</p

Kuhn-Tucker Estimation of Recreation Demand – A Study of Temporal Stability

The paper examines the Kuhn Tucker model in the context of estimating recreation demand when the choice set it very large. It examines the temporal stability of parameter estimates using multiple years of data on trips to 127 lakes in Iowa made by households in Iowa. The study finds that for the given dataset, the estimates derived from a Kuhn-Tucker model are largely stable over time

Capturing preferences under incomplete scenarios using elicited choice probabilities

Manski (1999) proposed an approach for dealing with a particular form respondent uncertainty in discrete choice settings, particularly relevant in survey based research when the uncertainty stems from the incomplete description of the choice scenarios. Specifically, he suggests eliciting choice probabilities from respondents rather than their single choice of an alternative. A recent paper in IER by Blass et al. (2010) further develops the approach and presents the first empirical application. This paper extends the literature in a number of directions, examining the linkage between elicited choice probabilities and the more common discrete choice elicitation format. We also provide the first convergent validity test of the elicited choice probability format vis-à-vis the standard discrete choice format in a split sample experiment. Finally, we discuss the differences between welfare measures that can be derived from elicited choice probabilities versus those that can obtained from discrete choice responses.</p

Hybrid feedback active noise control headset based on binaural signal utilization

A standard feedback active noise control (FBANC) headset utilizes the estimate of a primary disturbance at the left-ear (right-ear) error microphone to control noise only at the left (right) ear-cup, i.e., each ear’s controller works independently. In contrast to the FBANC headset, in this paper, a binaural hybrid feedback active noise control (HFBANC) headset is designed that uses the estimate of the primary disturbances at both the left and right-ear error microphones to achieve improved noise control at both the left and right ear-cups. To further improve noise cancellation performance, the nearest Kronecker product decomposition technique is incorporated into the algorithm. The performance of the proposed HFBANC headset is compared to the standard FBANC headset under a variety of different sound field conditions. Experimental results show an improvement of 3–5 dB in the noise cancellation using the proposed algorithms, where the benefits are more prominent for noise sources originating from the side of the user (left and right)

Quantized Information-Driven Laguerre Functional Linked Neural Networks for Nonlinear Active Noise Control

Traditional functional linked neural networks (FLNNs) impose a significant computational burden due to their input expansion, primarily stemming from the utilization of digital filters. This paper presents a Laguerre FLNNs filter for nonlinear active noise control (NANC) systems. By employing the truncated Laguerre series, the presented filter achieves effective approximation of long primary paths with a reduced filter length. Moreover, we develop adaptive algorithms rooted in information-theoretic learning (ITL) within the framework of the Laguerre-FLNNs NANC model presented here. Using ITL criterions, a Laguerre filtered-s maximum correntropy criterion (LFsMCC) algorithm is derived and a Laguerre filtered-s quantized minimum error entropy criterion (LFsQMEE) algorithm is proposed by minimizing Renyi’s quadratic entropy. To reduce the computation cost, an online vector quantization method is utilized to improve the LFsQMEE. This technique selectively quantizes the error vectors, reducing them to a smaller subset of samples within the codebook. Moreover, an enhanced LFsQMEE with a fiducial point is introduced. The steady-state performance and the computational complexity are analyzed. Theoretical analysis is validated through simulations, and the control performance of the proposed model and algorithms is tested in experiments with both simulated and real paths