Maximizing Profits for a Multi-Category Catalog Retailer

It is a common trend in the retail industry for catalog retailers to mail multiple catalogs, each promoting different product categories. The existing catalog mailing models do not address the issue of optimizing multi-category catalog mailing. We address this research gap by introducing a model that integrates the when and what components of a customer's purchase decision into the how much component (number of catalogs) of a firm's cross-selling strategy. In addition to comparing the impact of category-specific versus full product catalogs in generating sales in a specific category, the study also finds relative impacts of various category-specific catalogs. We jointly estimate the probability of purchase and purchase amounts in multiple product categories by using multivariate proportional hazard model (MVPHM) and a regression based purchase amount model in a Hierarchical Bayesian framework. The model accounts for unobserved heterogeneity, and uses a control function (CF) approach to account for endogeneity in catalog mailing. The results from the Genetic Algorithm (GA) based optimization suggest that the catalog mailing policy as per the proposed model would be able to generate 38.4 percent more customer lifetime value (CLV) from a sample of 10 percent of the households as compared to the current catalog mailing policy of the retailer by reallocation of the catalogs across customers and mailing periods based on their propensity to buy.     

Research productivity patterns in the organizational behavior and human resource management literature

Closely following the analysis approach used for similar studies in the economics and finance literature, we present the first study to examine if there exists an empirical regularity in the bibliometric patterns of research productivity in the organizational behavior (OB) and human resource management (HRM) literature. Our results present strong evidence that there indeed exists a distinct empirical regularity. It is the so-called Generalized Lotka's Law of scientific productivity pattern: The number of authors publishing n papers is about 1/n^c of those publishing one paper. The observed pattern in the OB and HRM area is interestingly very consistent with those in much older, related business disciplines.     

One-step R-estimation in linear models with stable errors

Classical estimation techniques for linear models either are inconsistent, or perform rather poorly, under α$\alpha$-stable error densities; most of them are not even rate-optimal. In this paper, we propose an original one-step R-estimation method and investigate its asymptotic performances under stable densities. Contrary to traditional least squares, the proposed R-estimators remain root-n$n$ consistent (the optimal rate) under the whole family of stable distributions, irrespective of their asymmetry and tail index. While parametric stable-likelihood estimation, due to the absence of a closed form for stable densities, is quite cumbersome, our method allows us to construct estimators reaching the parametric efficiency bounds associated with any prescribed values (_α0,_b0)$({\alpha }_0,b_0)$ of the tail index α$\alpha$ and skewness parameter b$b$, while preserving root-n$n$ consistency under any (α,b)$(\alpha ,b)$ as well as under usual light-tailed densities. The method furthermore avoids all forms of multidimensional argmin computation. Simulations confirm its excellent finite-sample performances.     

On the structure and estimation of hierarchical Archimedean copulas

In this paper we provide a method for estimating multivariate distributions defined through hierarchical Archimedean copulas. In general, the true structure of the hierarchy is unknown, but we develop a computationally efficient technique to determine it from the data. For this purpose we introduce a hierarchical estimation procedure for the parameters and provide an asymptotic analysis. We consider both parametric and nonparametric estimation of the marginal distributions. A simulation study and an empirical application show the effectiveness of the grouping procedure in the sense of structure selection.     

Asymptotic properties of MLE and UMVUE in one-parameter truncated distributions

Consider one-parameter families of continuous distributions whose range depend on an unknown parameter. In case a single sufficient and complete statistic exists, we obtain the limiting distributions of MLE and UMVUE. Both distributions are different transformations of a standard exponential variable.     

Multivariate stable ARMA processes with time dependent coefficients

This paper considers the class of m-variate autoregressive moving average (ARMA) processes with stable innovations and time dependent coefficients. A set of suitable AR and MA regularity conditions is given to ensure existence and uniqueness of valid solutions. A simple form of the above solution is expressed in terms of one sided Green's matrix functions associated with the AR operator. We solve the prediction problem arising in this class of models. A few examples are added to support the general theory.     

Uncertainty and the size distribution of rewards from innovation

 Previous research has shown that the distribution of profit outcomes from technological innovations is highly skew. This paper builds upon those detailed findings to ask: what stochastic processes can plausibly be inferred to have generated the observed distributions? After reviewing the evidence, this paper reports on several stochastic model simulations, including a pure Gibrat random walk with monthly changes approximating those observed for high-technology startup company stocks and a more richly specified model blending internal and external market uncertainties. The most highly specified simulations suggest that the set of profit potentials tapped by innovators is itself skew-distributed and that the number of entrants into innovation races is more likely to be independent of market size than stochastically dependent upon it.     

Bayesian and Bühlmann credibility for phase-type distributions with a univariate risk parameter

Credibility theory is a statistical tool to calculate the premium for the next period based on past claims experience and the manual rate. Each contract is characterized by a risk parameter. A phase-type (or PH) random variable, which is defined as the time until absorption in a continuous-time Markov chain, is fully characterized by two sets of parameters from that Markov chain: the initial probability vector and transition intensity matrix. In this article, we identify an interpretable univariate risk parameter from amongst the many candidate parameters, by means of uniformization. The resulting density form is then expressed as an infinite mixture of Erlang distributions. These results are used to obtain a tractable likelihood function by a recursive formula. Then the best estimator for the next premium, i.e. the Bayesian premium, as well as its approximation by the Bühlmann credibility premium are calculated. Finally, actuarial calculations for the Bühlmann and Bayesian premiums are investigated in the context of a gamma prior, and illustrated by simulated data in a series of examples.     

Semiparametric estimation of multi-asset portfolio tail risk

When correlations between assets turn positive, multi-asset portfolios can become riskier than single assets. This article presents the estimation of tail risk at very high quantiles using a semiparametric estimator which is particularly suitable for portfolios with a large number of assets. The estimator captures simultaneously the information contained in each individual asset return that composes the portfolio, and the interrelation between assets. Noticeably, the accuracy of the estimates does not deteriorate when the number of assets in the portfolio increases. The implementation is as easy for a large number of assets as it is for a small number. We estimate the probability distribution of large losses for the American stock market considering portfolios with ten, fifty and one hundred assets of stocks with different market capitalization. In either case, the approximation for the portfolio tail risk is very accurate. We compare our results with well known benchmark models.     

Unemployment or credit: Which one holds the potential? Results for a small open economy with a low degree of financialization

This paper investigates the relative importance of unemployment versus credit in determining the potential level of real activity for a small open economy with a low degree of financialization. We use a multivariate unobserved component model (MUC) to derive the potential output and the associated output gap for the Lithuanian economy. The model is estimated via Bayesian methods and the time paths of unobserved variables are extracted via the Kalman filter. The inclusion of unemployment in the MUC model substantially improves the estimates of the output gap in real time. Adding information about credit further emphasizes the overheating of the economy in the pre-crisis period, both in real time and ex post. Including credit preserves the conclusions regarding turning points. We uncover a strong negative correlation between the model-implied unemployment gap (without accounting for credit) and real credit growth. Data revisions do not appear to be the primary source of revisions of output gap estimates.