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on Discrete Choice Models |
By: | Sauthoff, Saramena; Danne, Michael; Mußhoff, Oliver |
Abstract: | In order to achieve an environmentally friendly and sustainable energy supply, it is necessary that this goal is supported by society. In different countries worldwide it has been shown that one way consumers want to support the energy transition is by purchasing green electricity. However, few people make the leap from their intention to a buying decision. This study explores parameters that influence whether German consumers decide to switch to a green electricity tariff. We conducted a quota-representative online survey including a discrete choice experiment with 371 private households in Germany in 2016. For the econometric analysis, a generalized multinomial logit model in willingness to pay (WTP) space was employed, enabling the estimation of WTP values to be as realistic as possible. The results show that consumers' decision regarding whether or not to make the switch to green energy is influenced by many underlying drivers, such as the source of green energy, whether a person can outsource the switching process, and a person's attitude towards the renewable energy sources levy that currently exists in Germany. Implications for policy makers and recommendations for the marketing of green energy tariffs are provided. |
Keywords: | energy transition,green energy,tariff switch,discrete choice experiment,generalised multinomial logit model,WTP space |
Date: | 2017 |
URL: | http://d.repec.org/n?u=RePEc:zbw:daredp:1707&r=dcm |
By: | Igor Kheifets (New Economic School, Moscow); Carlos Velasco (Dept. of Economics, Universidad Carlos III de Madrid) |
Abstract: | This paper proposes new specification tests for conditional models with discrete responses, which are key to apply efficient maximum likelihood methods, to obtain consistent estimates of partial effects and to get appropriate predictions of the probability of future events. In particular, we test the static and dynamic ordered choice model specifications and can cover infinite support distributions for e.g. count data. The traditional approach for specification testing of discrete response models is based on probability integral transforms of a jittered discrete data which leads to continuous uniform iid series under the true conditional distribution. Then, standard specification testing techniques for continuous variables could be applied to the transformed series, but the extra randomness from jitters affects the power properties of these methods. We investigate in this paper an alternative transformation based only on original discrete data that avoids any randomization. We analyze the asymptotic properties of goodness-of- t tests based on this new transformation and explore the properties in finite samples of a bootstrap algorithm to approximate the critical values of test statistics which are model and parameter dependent. We show analytically and in simulations that our approach dominates the methods based on randomization in terms of power. We apply the new tests to models of the monetary policy conducted by the Federal Reserve. |
Keywords: | Specification tests, Count data, Dynamic discrete choice models, Conditional probability integral transform |
JEL: | C12 C22 C52 |
Date: | 2017–06 |
URL: | http://d.repec.org/n?u=RePEc:cwl:cwldpp:1924r&r=dcm |
By: | Eric Girardin; Sandrine Lunven; Guonan Ma |
Keywords: | monetary policy in China, People's Bank of China, Taylor rule, inflation targeting, discrete-choice model, open-economy model |
JEL: | E52 E58 O11 O52 |
Date: | 2017–06 |
URL: | http://d.repec.org/n?u=RePEc:bis:biswps:641&r=dcm |