Forecasting intermittent demand by hyperbolic-exponential smoothing

Croston’s method is generally viewed as being superior to exponential smoothing when the demand is intermittent, but it has the drawbacks of bias and an inability to deal with obsolescence, where the demand for an item ceases altogether. Several variants have been reported, some of which are unbiased on certain types of demand, but only one recent variant addresses the problem of obsolescence. We describe a new hybrid of Croston’s method and Bayesian inference called Hyperbolic-Exponential Smoothing, which is unbiased on non-intermittent and stochastic intermittent demand, decays hyperbolically when obsolescence occurs, and performs well in experiments.     

An optimization approach for making causal inferences

To make causal inferences from observational data, researchers have often turned to matching methods. These methods are variably successful. We address issues with matching methods by redefining the matching problem as a subset selection problem. Given a set of covariates, we seek to find two subsets, a control group and a treatment group, so that we obtain optimal balance, or, in other words, the minimum discrepancy between the distributions of these covariates in the control and treatment groups. Our formulation captures the key elements of the Rubin causal model and translates nicely into a discrete optimization framework.     

Pair trading based on quantile forecasting of smooth transition GARCH models

Pair trading is a statistical arbitrage strategy used on similar assets with dissimilar valuations. We utilize smooth transition heteroskedastic models with a second-order logistic function to generate trading entry and exit signals and suggest two pair trading strategies: the first uses the upper and lower threshold values in the proposed model as trading entry and exit signals, while the second strategy instead takes one-step-ahead quantile forecasts obtained from the same model. We employ Bayesian Markov chain Monte Carlo sampling methods for updating the estimates and quantile forecasts. As an illustration, we conduct a simulation study and empirical analysis of the daily stock returns of 36 stocks from U.S. stock markets. We use the minimum square distance method to select ten stock pairs, choose additional five pairs consisting of two companies in the same industrial sector, and then finally consider pair trading profits for two out-of-sample periods in 2014 within a six-month time frame as well as for the entire year. The proposed strategies yield average annualized returns of at least 35.5% without a transaction cost and at least 18.4% with a transaction cost.     

THE EFFECTS OF HUMAN CAPITAL INTERVENTIONS ON ENTREPRENEURIAL PERFORMANCE IN INDUSTRIALIZED COUNTRIES

This paper provides a systematic review of studies on the effects of human capital interventions on entrepreneurial performance in industrialized countries. We identify 21 experimental and quasi‐experimental studies published before September 2018. These studies examine the effects of business training, formal education, and entrepreneurship education. Their performance outcomes include firm profits, firm size, and entrepreneurial earnings. The main finding across these studies is that these interventions do not have statistically significant effects. Formal education is the only exception, showing positive effects on firm profits and entrepreneurial earnings, yet these effects are small in magnitude. Evidence is inconclusive regarding effect duration. These findings stand in stark contrast to correlational studies, which tend to find large positive correlations between human capital interventions and entrepreneurial performance. We therefore conclude that correlational studies tend to overestimate the benefits of human capital interventions. Moreover, our estimates show that the interventions are associated with moderately low additionality.     

Processing consistency in non-Bayesian inference

We propose a coherent inference model that is obtained by distorting the prior density in Bayes’ rule and replacing the likelihood with a so-called pseudo-likelihood. This model includes the existing non-Bayesian inference models as special cases and implies new models of base-rate neglect and conservatism. We prove a sufficient and necessary condition under which the coherent inference model is processing consistent, i.e., implies the same posterior density however the samples are grouped and processed retrospectively. We further show that processing consistency does not imply Bayes’ rule by proving a sufficient and necessary condition under which the coherent inference model can be obtained by applying Bayes’ rule to a false stochastic model.     

Accessible Conceptions of Statistical Inference: Pulling Ourselves Up by the Bootstraps

With the rapid, ongoing expansions in the world of data, we need to devise ways of getting more students much further, much faster. One of the choke points affecting both accessibility to a broad spectrum of students and faster progress is classical statistical inference based on normal theory. In this paper, bootstrap‐based confidence intervals and randomisation tests conveyed through dynamic visualisation are developed as a means of reducing cognitive demands and increasing the speed with which application areas can be opened up. We also discuss conceptual pathways and the design of software developed to enable this approach.     

Erratum to ‘Inventory record inaccuracy: Causes and labor effects’

Inventory record inaccuracy (IRI) is a pervasive problem in retailing and causes non-trivial profit loss. In response to retailers’ interest in identifying antecedents and consequences of IRI, we present a study that comprises multiple modeling initiatives. We first develop a dynamic simulation model to compare and contrast impacts of different operational errors in a continuous (Q, R) inventory system through a full-factorial experimental design. While backroom and shelf shrinkage are found to be predominant drivers of IRI, the other three errors related to recording and shelving have negligible impacts on IRI. Next, we empirically assess the relationships between labor availability and IRI using longitudinal data from five stores in a global retail chain. After deriving a robust measure of IRI through Bayesian computation and estimating panel data models, we find strong evidence that full-time labor reduces IRI whereas part-time labor fails to alleviate it. Further, we articulate the reinforcing relationships between labor and IRI by formally assessing the gain of the feedback loop based on our empirical findings and analyzing immediate, intermediate, and long-term impacts of IRI on labor availability. The feedback modeling effort not only integrates findings from simulation and econometric analysis but also structurally explores the impacts of current practices. We conclude by discussing implications of our findings for practitioners and researchers.     

Diversification by entry into a new submarket?

Using a dataset on the subsector and geographical segmentation of 208 Pharmaceutical companies, a Bayesian panel probit is used to analyze the role of state dependency, size and achieved diversification in affecting entry decision. We properly account for unobservable heterogeneity in a context with nonstrictly exogenous regressors. We find that achieved diversification, measured by the number of submarkets already entered affects negatively the probability of entry. Beside some country-specific exceptions, size and the lagged dependent variable do not seem to be relevant.     

Modelling Overdispersion for Complex Survey Data

The population characteristics observed by selecting a complex sample from a finite identified population are the result of at least two processes: the process which generates the values attached to the units in the finite population, and the process of selecting the sample of units from the population. In this paper we propose that the resulting observations by viewed as the joint realization of both processes. We overcome the inherent difflculty in modelling the joint processes of generation and selection by exploring second moment and other simplifying assumptions. We obtain general expressions for the mean and covariance function of the joint processes and show that several overdispersion models discussed in the literature for the analysis of complex surveys are a direct consequence of our formulation, undere particular sampling schemes and population structures.     

治理环境、政府干预与大股东利益输送

利用樊纲和王小鲁（2003）编制的中国各地区市场化进程数据及其子数据，考察了治理环境对大股东利益输送行为的影响。实证研究发现：上市公司所处地区的治理环境与大股东的资金侵占行为显著相关，上市公司所处地区的市场化进程越快、政府对企业的干预程度越低，则大股东资金侵占程度越低；治理环境对大股东的影响程度还受到控股股东产权性质和组织形式的影响，国有控股上市公司和集团控股上市公司的大股东资金侵占行为更容易受到上市公司所处地区市场化进程、政府对企业干预程度的影响；上市公司所处地区的法治化水平对大股东资金侵占行为没有显著影响。