Money,random matching and endogenous growth: A quantitative analysis

In this study, we develop a search-and-matching monetary growth model to analyze the effects of inflation on economic growth and social welfare by introducing endogenous economic growth via capital externality into a two-sector search-and-matching model. We find that the channel through which inflation affects economic growth in the search-and-matching model is different from the traditional cash-in-advance model. To facilitate the calibration, we obtain an empirical estimate of the effects of inflation on economic growth using panel regressions. In the simulation analysis, we quantitatively evaluate the welfare effect of inflation in the search-and-matching endogenous growth model and compare it to a search-and-matching exogenous growth model. We find that the welfare effect of inflation is nonlinear in the endogenous growth model whereas it is linear in the exogenous growth model. Furthermore, we find that the welfare cost of inflation under endogenous growth is up to four times as large as the welfare cost of inflation under exogenous growth.     

Random regret minimization for consumer choice modeling: Assessment of empirical evidence

This paper introduces to the field of marketing a regret-based discrete choice model for the analysis of multi-attribute consumer choices from multinomial choice sets. This random regret minimization (RRM) model, which has recently been introduced in the field of transport, forms a regret-based counterpart of the canonical random utility maximization (RUM) paradigm. This paper assesses empirical results based on 43 comparisons reported in peer-reviewed journal articles and book chapters, with the aim of finding out to what extent, when, and how RRM can form a viable addition to the consumer choice modeler's toolkit. The paper shows that RRM and hybrid RRM–RUM models outperform RUM counterparts in a majority of cases, in terms of model fit and predictive ability. Although differences in performance are quite small, the two paradigms often result in markedly different managerial implications due to considerable differences in, for example, market share forecasts.     

The cost-effectiveness of solifenacin vs. trospium in the treatment of patients with overactive bladder in the German National Health Service

Objective:

To carry out a cost–utility analysis comparing initial treatment of patients with overactive bladder (OAB) with solifenacin 5?mg/day versus either trospium 20?mg twice a day or trospium 60?mg/day from the perspective of the German National Health Service.

Methods:

A decision analytic model with a 3 month cycle was developed to follow a cohort of OAB patients treated with either solifenacin or trospium during a 1 year period. Costs and utilities were accumulated as patients transitioned through the four cycles in the model. Some of the solifenacin patients were titrated from 5?mg to 10?mg/day at 3 months. Utility values were obtained from the published literature and pad use was based on a US resource utilization study. Adherence rates for individual treatments were derived from a United Kingdom general practitioner database review. The change in the mean number of urgency urinary incontinence episodes/day from after 12 weeks was the main outcome measure. Baseline effectiveness values for solifenacin and trospium were calculated using the Poisson distribution. Patients who failed second-line therapy were referred to a specialist visit. Results were expressed in terms of incremental cost–utility ratios.

Results:

Total annual costs for solifenacin, trospium 20?mg and trospium 60?mg were €970.01, €860.05 and €875.05 respectively. Drug use represented 43%, 28% and 29% of total costs and pad use varied between 45% and 57%. Differences between cumulative utilities were small but favored solifenacin (0.6857 vs. 0.6802 to 0.6800). The baseline incremental cost–effectiveness ratio ranged from €16,657 to €19,893 per QALY.

Limitations:

The difference in cumulative utility favoring solifenacin was small (0.0055–0.0057 QALYs). A small absolute change in the cumulative utilities can have a marked impact on the overall incremental cost-effectiveness ratios (ICERs) and care should be taken when interpreting the results.

Conclusion:

Solifenacin would appear to be cost-effective with an ICER of no more than €20,000/QALY. However, small differences in utility between the alternatives means that the results are sensitive to adjustments in the values of the assigned utilities, effectiveness and discontinuation rates.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

Objective methods for graphical structural learning

Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected-Posterior Prior and on the Power-Expected-Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein-signaling data, which wieldy confirms existing results in the scientific literature.     

On the existence of the ex post symmetric random entry model

This paper studies symmetry among countably infinitely many agents who randomly enter into a stochastic process, one for each period. Upon entry, they observe only the current period signal and try to draw inference about the underlying state governing the stochastic process. We show that there exist random entry models under which agents are ex post symmetric. That is, all agents have identical posterior belief about the underlying states, although they are not ex ante symmetric. The form of the posterior belief is uniquely pinned down by ex post symmetry and a stationarity condition. Our results provide a common prior foundation for the model studied in Liu and Skrzypacz (2014).     

Ex-post incentive compatible and individually rational assignments in housing markets with interdependent values

This paper presents a model for housing markets with interdependent values. We introduce private information on the quality of a house (i.e., high or low), which is known only to the initial owner. Interdependency means that the ex-post preference of an agent depends on the private information of the other agents with regard to the quality of houses. We prove that on a domain satisfying a richness condition, the no-trade rule is the only rule that satisfies ex-post incentive compatibility and ex-post individual rationality.     

How risky is a random process?

The riskiness of random processes is compared by (a) employing a decision theoretic equivalence between processes and lotteries on path-spaces to identify the riskiness of the former with that of the latter, and (b) using the theory of comparative riskiness of lotteries over vector spaces to compare the riskiness of lotteries on a given path-space. We derive the equivalence used in step (a) and contribute a new criterion to the theory applied in step (b). The validity of the new criterion, which applies second order stochastic dominance to utility distributions, is established by showing its equivalence to the benchmark decision theoretic criterion when comparing the riskiness of lotteries over any vector space. We demonstrate the theory’s tractability via diverse economic applications.     

NONPARAMETRIC AND SEMIPARAMETRIC PANEL DATA MODELS: RECENT DEVELOPMENTS

In this paper, we provide an intensive review of the recent developments for semiparametric and fully nonparametric panel data models that are linearly separable in the innovation and the individual-specific term. We analyze these developments under two alternative model specifications: fixed and random effects panel data models. More precisely, in the random effects setting, we focus our attention in the analysis of some efficiency issues that have to do with the so-called working independence condition. This assumption is introduced when estimating the asymptotic variance–covariance matrix of nonparametric estimators. In the fixed effects setting, to cope with the so-called incidental parameters problem, we consider two different estimation approaches: profiling techniques and differencing methods. Furthermore, we are also interested in the endogeneity problem and how instrumental variables are used in this context. In addition, for practitioners, we also show different ways of avoiding the so-called curse of dimensionality problem in pure nonparametric models. In this way, semiparametric and additive models appear as a solution when the number of explanatory variables is large.