Given data on a scalar random variable 𝑌, a prediction set for 𝑌 with miscoverage level 𝛼 is a set of values for 𝑌 that contains a randomly drawn 𝑌 with probability 1 − 𝛼, where 𝛼 ∈ (0, 1). Among all prediction sets that satisfy this coverage property, the oracle...

Regularized regression methods have attracted much attention in the literature, mainly due to its application in high-dimensional variable selection problems. Most existing regularization methods assume that the predictors are directly observed and precisely measured. It is well known that in a...

The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machine learning techniques. Evaluations focus on...

Response functions that link regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these functions is typically based on the domain of the modeled quantities and is usually not further scrutinized. For...

This note looks at estimation of spatial autoregressive models for non-negative and count outcomes with multiplicative fixed effects. We show that in presence of significant proportion of zeros in the count variable, control function and Instrumental Variable estimation to model such spatial...

This note looks at estimation of count models with multiplicative fixed effects and spatial dependence in the form of spatial lag autocorrelation. We show that in presence of significant proportion of zeros in the count variable, control function estimation to model such spatial dependence is...

The quality of generalized linear models (GLMs), frequently used by insurance companies, depends on the choice of interacting variables. The search for interactions is time-consuming, especially for data sets with a large number of variables, depends much on expert judgement of actuaries, and...

Predictive modeling has been widely used for insurance rate making. In this paper, we focus on insurance claim count data and address their common issues with more flexible modeling techniques. In particular, we study the zero-inflated and hurdle-generalized Poisson and negative binomial...

Response functions that link regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these functions is typically based on the domain of the modeled quantities and is usually not further scrutinized. For...

The quality of generalized linear models (GLMs), frequently used by insurance companies, depends on the choice of interacting variables. The search for interactions is time-consuming, especially for data sets with a large number of variables, depends much on expert judgement of actuaries, and...

Up to now, powerful outlier robust tests for linear models are based on M-estimators and are quite complicated. On the other hand, the simple robust classical sign test usually provides very bad power for certain alternatives. We present a generalization of the sign test which is similarly easy...

This paper proposes enhanced studies on a model consisting of a finite mixture framework of generalized linear models (GLMs) with gamma-distributed responses estimated using the Bayesian approach coupled with the Markov Chain Monte Carlo (MCMC) method. The log-link function, which relates the...

We define liquidity for corporate bonds as the expected waiting time to reduce a risk position. Our methodology addresses the fact that many bonds are liquidated quickly despite having few trades in the recent past. Building on research from the housing market, we apply survival analysis to bond...

This note looks at the properties of instrumental-variable estimators of count data models in the presence of individual effects. We show that fixed-effect versions of the estimators of Mullahy (1997) and Windmeijer and Santos Silva (1997) are inconsistent under conventional asymptotics, in...

This paper develops a multivariate model for count time series, in which the time-varying intensity parameter determining the probability that an event occurs evolves according to general autoregressive score (GAS) models (see Creal et al., 2013; Harvey, 2013).The model is particularly suitable to...

This report focuses on the forecast of the number of monthly cross-border deals in the European Union. We propose a new model to improve the forecasting properties of a count model of Foreign Direct Investment deals in EU, by taking into account past trends in high-frequency (daily) deal data...

Item count techniques (ICTs) are indirect survey questioning methods designed to deal with sensitive features. These techniques have gained the support of many applied researchers and undergone further theoretical development. Latterly in the literature, two new item count methods, called...

This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring...

The automobile industry is a service-oriented industry that is constantly developing. Households are the most important interlocutors in this industry. The study's aim in this direction is to determine what factors influence the number of automobiles owned by households. The factors that affect...

While the actions of economists and politicians can be influenced by facts, statistics or empirical predictions, narratives are becoming an increasingly important factor for the decision making in the field of economics and politics. Evaluating such narratives not at selective points in time but...

The paper continues the authors’ work (Freise et al. The adaptive Wynn-algorithm in generalized linear models with univariate response. arXiv:1907.02708, 2019) on the adaptive Wynn algorithm in a nonlinear regression model. In the present paper the asymptotics of adaptive least squares...

This paper focuses on developing and adapting statistical models of counts (non-negative integers) in the context of panel data and using them to analyze the relationship between patents and R&D expenditures. The model used is an application and generalization of the Poisson distribution to...

This paper examines the use of count data-based outcome variables such as corporate patents in empirical corporate finance research. We demonstrate that the common practice of regressing the log of one plus the count on covariates ("LOG1PLUS" regression) produces biased and inconsistent...

The steadily growing access to high-quality spatio-temporal crime count data with a high level of spatial detail allows to uncover interesting relationships between crime types within and between small regional units. Data coherent forecasting of such counts has to take the integer and...

The clustering of their letter shapes is performed based on the pairwise distances between their topological signatures.The article presents a new probability distribution, created by compounding the Poisson distribution with the weighted exponential distribution. Important mathematical and...

This paper explores differences in the frequency with which students from different schools reach high levels of math achievement. Data from the American Mathematics Competitions is used to produce counts of high-scoring students from more than two thousand public, coeducational, non-magnet,...

The paper makes three contributions to the economics literature on entrepreneurship. We offer a new measure of entrepreneurship which accounts for variations in persistence in self-employment and as a result avoids the weakness of approaches which categorise an individual as an entrepreneur by...

This paper explores differences in the frequency with which students from different schools reach high levels of math achievement. Data from the American Mathematics Competitions is used to produce counts of high-scoring students from more than two thousand public, coeducational, non-magnet,...

In this paper, a new member of the Poisson family of distributions called the Poison-Exponential-Gamma (PEG) distribution for modeling count data is proposed by compounding the Poisson with Exponential-Gamma distributions. The first four moments about the origin and mean of the new PEG...

This paper presents and assesses analytical strategies that respect the bounded count structures of outcomes that are encountered often in health and other applications. The paper's main motivation is that the applied econometrics literature lacks a comprehensive discussion and critique of...