Economics

Many Stated Preference studies conducted in developing countries provide a low willingness to pay (WTP) for a wide range of goods and services. However, recent studies in these countries indicate that this may partly be a result of the choice of payment vehicle, not the preference for the good. Thus, low WTP may not indicate a low welfare effect for public projects in developing countries. We argue that in a setting where 1) there is imperfect substitutability between money and other measures of wealth (e.g. labor), and 2) institutions are perceived to be corrupt, including payment vehicles that are currently available to the individual and less pron to corruption may be needed to obtain valid welfare estimates. Otherwise, we risk underestimating the welfare benefit of projects. We demonstrate this through a rural household contingent valuation (CV) survey designed to elicit the value of access to reliable irrigation water in Ethiopia. Of the total average annual WTP for access to reliable irrigation service, cash contribution comprises only 24.41 %. The implication is that socially desirable projects might be rejected based on cost-benefit analysis as a result of welfare gain underestimation due to mismatch of payment vehicles choice in valuation study.

The Internet plays a key role in society and is vital to economic development. Due to the pressure of competition, most technology companies, including Internet finance companies, continue to explore new markets and new business. Funding subsidies and resource inputs have led to significant business income tendencies in financial statements. This tendency of business income is often manifested as part of the business loss or long-term unprofitability. We propose a risk change indicator (RFR) and compare the risk indicator of fourteen representative companies. This model combines extreme risk value with slope, and the combination method is simple and effective. The results of experiment show the potential of this model. The risk volatility of technology enterprises including Internet finance enterprises is highly cyclical, and the risk volatility of emerging Internet fintech companies is much higher than that of other technology companies.

We construct robust empirical Bayes confidence intervals (EBCIs) in a normal means problem. The intervals are centered at the usual linear empirical Bayes estimator, but use a critical value accounting for shrinkage. Parametric EBCIs that assume a normal distribution for the means (Morris, 1983) may substantially undercover when this assumption is violated, and we derive a simple rule of thumb for gauging the potential coverage distortion. In contrast, our EBCIs control coverage regardless of the means distribution, while remaining close in length to the parametric EBCIs when the means are indeed Gaussian. If the means are treated as fixed, our EBCIs have an average coverage guarantee: the coverage probability is at least 1−α on average across the n EBCIs for each of the means. Our empirical applications consider effects of U.S. neighborhoods on intergenerational mobility, and structural changes in a large dynamic factor model for the Eurozone.

We use a decision-theoretic framework to study the problem of forecasting discrete outcomes when the forecaster is unable to discriminate among a set of plausible forecast distributions because of partial identification or concerns about model misspecification or structural breaks. We derive "robust" forecasts which minimize maximum risk or regret over the set of forecast distributions. We show that for a large class of models including semiparametric panel data models for dynamic discrete choice, the robust forecasts depend in a natural way on a small number of convex optimization problems which can be simplified using duality methods. Finally, we derive "efficient robust" forecasts to deal with the problem of first having to estimate the set of forecast distributions and develop a suitable asymptotic efficiency theory. Forecasts obtained by replacing nuisance parameters that characterize the set of forecast distributions with efficient first-stage estimators can be strictly dominated by our efficient robust forecasts.

This paper proposes a robust method for semiparametric identification and estimation in panel multinomial choice models, where we allow for infinite-dimensional fixed effects that enter into consumer utilities in an additively nonseparable way, thus incorporating rich forms of unobserved heterogeneity. Our identification strategy exploits multivariate monotonicity in parametric indexes, and uses the logical contraposition of an intertemporal inequality on choice probabilities to obtain identifying restrictions. We provide a consistent estimation procedure, and demonstrate the practical advantages of our method with simulations and an empirical illustration with the Nielsen data.

We study efficiency improvements in estimating a vector of potential outcome means using linear regression adjustment when there are more than two treatment levels. We show that using separate regression adjustments for each assignment level is never worse, asymptotically, than using the subsample averages. We also show that separate regression adjustment improves over pooled regression adjustment except in the obvious case where slope parameters in the linear projections are identical across the different assignment levels. We also characterize the class of nonlinear regression adjustment methods that preserve consistency of the potential outcome means despite arbitrary misspecification of the conditional mean functions. Finally, we apply this general potential outcomes framework to a contingent valuation study for estimating lower bound mean willingness to pay for an oil spill prevention program in California.

Inferences of robust behavioural and statistical models are insensitive to outlying observations resulting from aberrant behaviour, misreporting and misclassification. Standard discrete choice models such as logit and probit lack robustness to outliers due to their rigid kernel error distributions. In this paper, we analyse two robust alternatives to the multinomial probit (MNP) model. The two models belong to the family of robit models whose kernel error distributions are heavy-tailed t-distributions which moderate the influence of outlying observations. The first model is the multinomial robit (MNR) model, in which a generic degrees of freedom parameter controls the heavy-tailedness of the kernel error distribution. The second model, the generalised multinomial robit (Gen-MNR) model, is more flexible than MNR, as it allows for distinct heavy-tailedness in each dimension of the kernel error distribution. For both models, we derive efficient Gibbs sampling schemes, which also allow for a straightforward inclusion of random parameters. In a simulation study, we illustrate the excellent finite sample properties of the proposed Bayes estimators and show that MNR and Gen-MNR produce more exact elasticity estimates if the choice data contain outliers through the lens of the non-robust MNP model. In a case study on transport mode choice behaviour, MNR and Gen-MNR outperform MNP by substantial margins in terms of in-sample fit and out-of-sample predictive accuracy. We also find that the benefits of the more flexible kernel error distributions underlying MNR and Gen-MNR are maintained in the presence of random heterogeneity.

In the international oil trade network (iOTN), trade shocks triggered by extreme events may spread over the entire network along the trade links of the central economies and even lead to the collapse of the whole system. In this study, we focus on the concept of "too central to fail" and use traditional centrality indicators as strategic indicators for simulating attacks on economic nodes, and simulates various situations in which the structure and function of the global oil trade network are lost when the economies suffer extreme trade shocks. The simulation results show that the global oil trade system has become more vulnerable in recent years. The regional aggregation of oil trade is an essential source of iOTN's vulnerability. Maintaining global oil trade stability and security requires a focus on economies with greater influence within the network module of the iOTN. International organizations such as OPEC and OECD established more trade links around the world, but their influence on the iOTN is declining. We improve the framework of oil security and trade risk assessment based on the topological index of iOTN, and provide a reference for finding methods to maintain network robustness and trade stability.

The multinomial probit model is a popular tool for analyzing choice behaviour as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives. This paper proposes a factor structure on the covariance matrix, which makes the model scalable to large choice sets. The main challenge in estimating this structure is that the model parameters require identifying restrictions. We identify the parameters by a trace-restriction on the covariance matrix, which is imposed through a reparametrization of the factor structure. We specify interpretable prior distributions on the model parameters and develop an MCMC sampler for parameter estimation. The proposed approach significantly improves performance in large choice sets relative to existing multinomial probit specifications. Applications to purchase data show the economic importance of including a large number of choice alternatives in consumer choice analysis.

This study presents a semi-nonparametric Latent Class Choice Model (LCCM) with a flexible class membership component. The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification with the aim of comparing the two approaches on various measures including prediction accuracy and representation of heterogeneity in the choice process. Mixture models are parametric model-based clustering techniques that have been widely used in areas such as machine learning, data mining and patter recognition for clustering and classification problems. An Expectation-Maximization (EM) algorithm is derived for the estimation of the proposed model. Using two different case studies on travel mode choice behavior, the proposed model is compared to traditional discrete choice models on the basis of parameter estimates' signs, value of time, statistical goodness-of-fit measures, and cross-validation tests. Results show that mixture models improve the overall performance of latent class choice models by providing better out-of-sample prediction accuracy in addition to better representations of heterogeneity without weakening the behavioral and economic interpretability of the choice models.