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Typical censoring models have mass points at the upper or lower tails, or at both tails, of an otherwise continuous outcome distribution. In contrast, we consider a censoring model with a mass point in the interior of the outcome distribution. We refer to this mass point as “bunching” and use it to estimate model parameters. For example, economic theory suggests that, for increasing marginal income tax rates, many taxpayers will report income exactly at the threshold where the tax rate increases. This translates into a censoring model with bunching at the threshold. The size of this mass point of taxpayers can be used to estimate an elasticity parameter that summarizes taxpayers’ responses to taxes. In this article, we introduce the command bunching, which implements new nonparametric and semiparametric identification methods for estimating elasticities developed by Bertanha, McCallum, and Seegert (2021, Technical Report 2021-002, Board of Governors of the Federal Reserve System). These methods rely on weaker assumptions than what are currently made in the literature and result in meaningfully different estimates of the elasticity.

The constant kernel width of generalized maximum correntropy criteria (GMCC) has arisen that the steady-state error and convergence speed can be mutually exclusive. To solve this problem, this paper proposes the variable kernel width (VKW) GMCC algorithm. Actually, due to the censored problem, the output data value beyond the limit of the recording device can not be well observed. In this case, we further developed a variable kernel width GMCC algorithm based on censored regression (CRVKWGMCC). Simulation results show that the proposed CR-VKWGMCC algorithm has excellent performance in both Gaussian and non Gaussian noise.

Non-Gaussian colored input signal and censored observation are encountered frequently in many practical applications of adaptive signal processing. Conventional adaptive algorithms will face convergence performance degradation in such cases. To address the issue, the affine projection algorithm for censored regression (CR-APA) is proposed in the paper. The censored thresholds and the variance of the background noise are regarded as the prior knowledge to compensate for the bias caused by censored observation. By theoretical analysis, the range of the step size is derived to guarantee the stabilization of the CR-APA. Besides, the method of evolving projection order is employed to improve the convergence performance of the CR-APA. Computer simulations in system identification and echo cancellation applications are performed to demonstrate the better convergence performance of the proposed algorithms in censored data processing over competing algorithms, and to verify the improvement of the CR-APA by the method of evolving projection order. The proposed CR-APA and CR-EAPA can be used in engineering applications to make up for the hardware.

In this paper, we use a censored regression model to investigate data on the international trade of small arms and ammunition provided by the Norwegian Initiative on Small Arms Transfers. Taking a network-based view on the transfers, we do not only rely on exogenous covariates but also estimate endogenous network effects. We apply a spatial autocorrelation gravity model with multiple weight matrices. The likelihood is maximized employing the Monte Carlo expectation maximization algorithm. Our approach reveals strong and stable endogenous network effects. Furthermore, we find evidence for a substantial path dependence as well as a close connection between exports of civilian and military small arms. The model is then used in a ‘forensic’ manner to analyse latent network structures and thereby to identify countries with higher or lower tendency to export or import than reflected in the data. The approach is also validated using a simulation study.

We propose two Bayesian methods for regularized left censored regression: the reciprocal Bayesian bridge and the reciprocal Bayesian adaptive bridge. Gibbs samplers are derived based on the recipro...

There is limited empirical evidence measuring productive capabilities and analysing their effect on firm-level export performance in Africa. This paper constructs novel indicators of productive capabilities and examines their effects on the export performance of African firms. Using recent firm-level data from the World Bank Enterprise Survey across 29 African countries, the paper builds new indicators of productive capabilities, which are conceptualised as a combination of technological and production capabilities. Estimating a censored regression model, we find that both technological and production capabilities are important drivers of firms’ direct export performance. This suggests that superior productive capabilities enhance the efficiency and competitiveness of firms, leading to improvements in their export performance. The findings underscore the importance of policies that support the strengthening of productive capabilities in African countries.

According to the annual accident report (2018) of the European Commission it is clear that during the decade 2007-2016 fatalities by mode of transport have dropped significantly inside the European Union (EU). However, there is always a place for further improvements. It is also clear that the changes in the socio-economic and demographic context of the EU countries have reported impacts on the fatalities by different modes of transport and overall, to their road safety levels. Therefore, in order to support the road safety strategies of the EU countries that have an under-performing system (in terms of road safety levels), it is essential to incorporate exposure and socio-economic factors and provide a comparative analysis of the performances of EU countries. This paper aims the support of road safety policymakers by investigating the road safety performance of 18 EU countries over the time period 2007-2016 (incorporating their exposure level and socio-economic context) and measuring the effect that these factors have on the countries’ road safety performance. For the evaluation of the countries’ road safety performance, a benchmarking analysis was implemented, namely Data Envelopment Analysis (DEA). The resulted technical efficiency scores of the countries in relation to their exposure and socio-economic context were used in a censored regression framework, i.e., Tobit regression analysis, for measuring the extent of relation with road safety performance. Overall, this paper aims to provide a ‘picture’ of the road safety levels inside the EU region during the decade 2007-2016.

Multicollinearity is often ignored in censored regression model. In this respect, Tobit Liu estimation is proposed as an alternative in order to dispel the adverse effects of multicollinearity on t...

Sufficient dimension reduction (SDR) that effectively reduces the predictor dimension in regression has been popular in high-dimensional data analysis. Under the presence of censoring, however, most existing SDR methods suffer. In this article, we propose a new algorithm to perform SDR with censored responses based on the quantile-slicing scheme recently proposed by Kim et al. First, we estimate the conditional quantile function of the true survival time via the censored kernel quantile regression (Shin et al.) and then slice the data based on the estimated censored regression quantiles instead of the responses. Both simulated and real data analysis demonstrate promising performance of the proposed method.

Recent sports economics literature has focused on ‘superstar effects,’ or the independent impact of certain exceptional players on fan attendance at live events. This paper investigates the effect ...

This study proposes a semiparametric estimation method for a censored regression model subject to nonparametric sample selection without the exclusion restriction. Consistency and asymptotic normal...

A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset.

In this study, the Tobit Model as a statistical regression model was used to study factors affecting blood pressure (BP) in patients with renal failure. The data have been collected from (300) patients in Shar Hospital in Sulaimani city. Those records contain BP rates per person in patients with renal failure as a response variable (Y) which is measured in units of millimeters of mercury (mmHg), and explanatory variables (Age [year], blood urea measured in milligram per deciliter [mg/dl], body mass index [BMI] expressed in units of kg/m2 [kilogram meter square], and Waist circumference measured by the Centimeter [cm]). The two levels of BP; high and low were taken from the patients. The mean arterial pressure (MAP) was used to find the average of both levels (high and low BP). The average BP rate of those patients equal to or >93.33 mmHg only remained in the dataset. The 93.33 mmHg is a normal range of MAP equal to 12/8 mmHg normal range of BP. The others have been censored as zero value, i.e., left censored. Furthermore, the same data were truncated from below. Then, in the truncated samples, only those cases under risk of BP (greater than or equal to BP 93.33mmHg) are recorded. The others were omitted from the dataset. Then, the Tobit Model applied on censored and truncated data using a statistical program (R program) version 3.6.1. The data censored and truncated from the left side at a point equal to zero. The result shows that factors age and blood urea have significant effects on BP, while BMI and Waist circumference factors have not to affect the dependent variable(y). Furthermore, a multiple regression model was found through ordinary least Square (OLS) analysis from the same data using the Stratigraphy program version 11. The result of (OLS) shows that multiple regression analysis is not a suitable model when we have censored and truncated data, whereas the Tobit model is a proficient technique to indicate the relationship between an explanatory variable, and truncated, or censored dependent variable.

The present study assesses the factors that are significantly related to the use of financial services among the people living in the North-Eastern States of India. This part of the geographical area has a low penetration of banking and financial services as reported in various documents by the regulators. The use of financial services gives a precise picture of the inclusiveness of the financial system. Financial inclusion does not merely mean having a bank account but using that bank account to derive benefits from various financial services. The present study will try to find out the factors acting as impediments towards the use of financial services and affecting the choice of using or not using any such financial services and products. The study is based on the primary data collected using a questionnaire from 1,948 respondents spreading across three states of North East India. Tobit regression analysis is used as the censored regression model to find out the linear relationship between variables affecting the use of financial services. Fitting Tobit regression model, it is found that, the critical factors that are significant towards the use of financial services are financial awareness, economic status of the members, the suitability of financial products, income, education, age of the user and presence of banking correspondents.

In this paper and after introducing a new model along with its properties, we estimate the unknown parameter of the new model using the Maximum likelihood method, Cram er-Von-Mises method, bootstrapping method, least square method and weighted least square method. We assess the performance of all estimation method employing simulations. All methods perform well but bootstrapping method is the best in modeling relief times whereas the maximum likelihood method is the best in modeling survival times. Censored data modeling with covariates is addressed along with the index plot of the modified deviance residuals and its Q-Q plot.

In fisheries where there is a limit on total catch in a given period, catch-per-unit-effort (CPUE) data may not be proportional to abundance because catches may be censored at the limit. Commonly used depletion estimators (e.g., Leslie method) could be biased when ordinary least squares (OLS) regression is used to estimate abundance with censored CPUE data. We used simulations to examine the performance of OLS regression and a censored regression approach when estimating abundance and exploitation using censored CPUE data over a range of known exploitation rates. We also applied the censored regression approach to data from a commercial fishery for the eastern oyster (Crassostrea virginica). The censored regression approach always performed better than the OLS regression when estimating abundance and exploitation in our simulations. Harvest and abundance of oysters in Fishing Bay, Maryland, increased during 2009 to 2013 and then decreased through 2016, while exploitation rates had no substantial trend over time. The censored regression approach is useful for estimating abundance and exploitation when the distribution of CPUE is affected by daily catch limits.

Objectives The EQ-5D-5L valuation protocol recommends combining time trade-off (TTO) and discrete choice experiments (DCEs). DCEs that include a duration attribute (DCETTO) allow modeling on the quality-adjusted life-year scale. Because the choice sequence in a TTO can be construed as a series of DCETTO, we used data from a single TTO study to investigate the extent to which DCE values match TTO values when based on identical preferences. Methods In a TTO design in which a fixed set of choices were administered without termination at preference indifference, 202 individuals each valued 10 EQ-5D health states. From identified indifference points, we estimated three sets of TTO values: (i) plotting means and (ii) applying censored regressions at −1 and 1. Using all strict preferences, we (iii) estimated DCETTO values with a logit model and a bootstrap procedure. Results Estimated DCETTO and TTO values agreed well at the severe end of the quality-adjusted life-year scale, but with decreasing severity, DCETTO values were higher than TTO-values, with the difference peaking at 0.37 for the mildest health state. Left-censoring TTO values at −1 worsen the agreement for the worst health states and did not affect health states. Right censoring at 1 improved the agreement for mild states. Conclusions TTO and the DCETTO values estimated from the same preference data diverged, with increasing difference for milder health states. Although the values converged when applying censored regression at +1, we question the validity of this adjustment.

Access to finance has been an important issue in minimizing the risk in agricultural prices. Farmers in Indonesia can access finance from different financial sources, including from banks, micro finance institutions (MFIs), farmers’ associations, traders, agricultural input kiosks, family, and friends. This research was aimed to investigate the difference of price risk between farmers who had access to finance from different financial providers and those who did not, and to analyze the price risk and its relation to some socioeconomic variables. A survey was conducted to collect data from selected horticultural farmers, i.e. potato and banana farmers at the center of potato and banana production in West Java. The price risk was analyzed using 25th percentile and coefficient of variation. Moreover, the data were analyzed using independent t-test to see the difference of the risk between the groups of farmers, and censored regression model to see the relation between price risk and some socioeconomic variables. The results show that farmers who had access to financial sources from MFIs, farmers’ associations, traders and from other financial sources had a lower price risk of banana compared to the farmers who did not have access to finance from those financial providers. The results also show that farmers having access to finance from traders had lower risk in terms of the price distribution. The findings suggest the need to enhance financial access from MFIs, farmers’ associations, and traders since those finance providers seem to have potency to minimize the farmers’ risk on price.

Censoring and endogeneity are common in empirical applications. However, the existing semiparametric estimation methods for the censored regression model with endogeneity under an independence restriction are associated with some drawbacks. In this paper we propose a new semiparametric estimator that overcomes these drawbacks. We derive conditional quantile moment conditions for all the conditional quantiles and propose a moment-based estimator. In particular, we construct two types of moment conditions and develop a computationally attractive estimator. We show that our estimator is consistent and asymptotic normal. A Monte Carlo study indicates that our estimator performs well in finite samples and compares favorably with existing methods.

In this paper, we propose the power Student-t regression model for censored (limited) observations which extends the Student-t censored regression model. This extension is based on the asymmetric and heavy-tailed power Student-t distribution. The score functions and expected information matrix are given as well as the process for estimating the parameters in the model is discussed by using the likelihood approach. Two simulation studies are conducted to evaluate parameter recovery and properties of the model and finally, two applications to a real data set are reported to demonstrate the usefulness of this new methodology.

Classical adaptive algorithms have good convergence performance in linear regression system identification. However, they will face performance degradation while dealing with censored data since only incomplete information can be obtained. In this paper, the least mean M-estimate algorithm for censored regression (CR-LMM) is proposed for the robust parameter estimation. To compensate for the bias caused by censored observation, the probit regression model is employed to derive the estimated error for constructing the M-estimate cost function. The cost function can expel the adverse impact of the impulsive noise, and it is solved by the unconstrained optimization method. Computer simulations in the impulsive environment are carried out to demonstrate that the proposed CR-LMM algorithm exhibits better convergence performance than the existing algorithms in censored regression system identification scenarios.

Purpose The purpose of this paper is to examine the joint dynamics of volatility–volume relation in the high-yield (junk) corporate bond market during the 2007–2008 financial crisis. Design/methodology/approach The author proposes a new empirical model of three-stage equations to better estimate the volume–volatility relation that helps in alleviating three econometrical problems. In Stage 1, the author estimates the fitted values of trading volume using a censored regression model, to alleviate the truncation problems of using Transaction Reporting and Compliance Engine data. In Stage 2, the author calculates the fitted values of bond return volatility using asymmetric Sign-GARCH model, to control for the asymmetric volatility in return series. In Stage 3, the author uses the fitted values of trading volume from the censored regression model (Stage 1) and the fitted values of return volatility from the GARCH model (Stage 2), to better alleviate the endogeneity problems between both variables. Findings The central finding is that conclusions about the statistical significance and the direction of the volume–volatility relationship in the junk bond market are dependent on the econometric methodology used. Originality/value From a practitioner perspective, it is important for professional traders holding positions in fixed income securities in their trading accounts to be aware of their asymmetric time-varying volume–volatility shifting trends. Such knowledge helps traders diversify their positions and manage their portfolios more appropriately.

This paper considers identification and estimation of a fixed-effects model with an interval-censored dependent variable. In each time period, the researcher observes the interval (with known endpoints) in which the dependent variable lies but not the value of the dependent variable itself. Two versions of the model are considered, a parametric model with logistic errors and a semiparametric model with errors having an unspecified distribution. In both cases, the error disturbances can be heteroskedastic over cross-sectional units as long as they are stationary within a cross-sectional unit; the semiparametric model also allows for serial correlation of the error disturbances. A conditional-logit-type composite likelihood estimator is proposed for the logistic fixed-effects model, and a composite maximum-score-type estimator is proposed for the semiparametric model. In general, the scale of the coefficient parameters is identified by these estimators, meaning that the causal effects of interest are estimated directly in cases where the latent dependent variable is of primary interest (e.g., pure data-coding situations). Monte Carlo simulations and an empirical application to birthweight outcomes illustrate the performance of the parametric estimator.

We show that exact computation of the censored least absolute deviations (CLAD) estimator proposed by Powell (1984) may be achieved by formulating the estimator as a linear Mixed Integer Programming (MIP) problem with disjunctive constraints. We apply our approach to three previously studied datasets and find that widely used approximate optimization algorithms can lead to erroneous conclusions. Extensive simulations confirm that MIP-based computation using available solvers is effective for datasets typically encountered in econometric applications and that, despite the proliferation of competitors, CLAD remains a useful estimator.

Suppose we have a location-scale regression model where the location is the conditional mean and the scale is the conditional standard deviation; the response is possibly right-censored, the covariate is fully observed, and the error is independent of the covariate. We propose new goodness-of-fit testing procedures for the conditional mean and variance based on an integrated regression function technique which uses artificial data points. We obtain the weak convergence of the resulting processes and study their finite sample behavior via simulations. Finally, we analyze a data set about unemployment in Galicia.

Censored observations and impulsive measurement noise are encountered in many practical applications of adaptive signal processing. Traditional adaptive filtering algorithms may fail to work in such cases. This paper proposes a robust adaptive filter algorithm predicated on maximum correntropy criteria (MCC) for censored regression. A detailed performance analysis in terms of mean and mean-square behaviour is provided. Simulations with Gaussian and non-Gaussian noise are presented to verify the theoretical results, and to demonstrate the superior performance of the proposed algorithm over existing algorithms. Refereed/Peer-reviewed

In reality, the range of sensor response is limited in many sensor systems due to the saturation characteristics of the sensor. That is, the value exceeding the sensor response range is not observed. Using traditional adaptive algorithms to identify the system of this type may lead to the performance degradation. To address this problem, the censored regression algorithms have been proposed. However, when the mixed sub-Gaussian and super-Gaussian/impulsive noises occur, these algorithms may fail to work. To overcome these drawbacks, a family of robust M-shaped (FRMS) functions for censored regression (CR-FRMS) is proposed in this paper. When the system to be identified exhibits a certain degree of sparsity, the CR-FRMS algorithm cannot fully utilize the characteristics of the sparse system. Therefore, in this paper, proportionate FRMS (PFRMS) algorithm based on $$ l_{0} $$-norm constraint for censored regression ($$ l_{0} $$-CRPFRMS) is also proposed accordingly. The simulations using Gaussian white noise as the input signal and the non-Gaussian mixed noise as the background noise show that the proposed algorithm performs better than other algorithms.