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To distinguish between respondents that have attended to/ignored an attribute in discrete choice experiments (DCE), Hess and Hensher (HH) apply the coefficient of variation of the conditional distribution, setting a threshold of 2 as a conservative rule of thumb. This paper develops an analytical framework (piecewise regression analysis — PWRA) to refine the HH approach, offering a flexible method to identify attribute non-attendance (ANA) in highly context-dependent DCE. It is empirically tested on a datasetusedtovalueagriculturalpublicgoods.Theresultssuggestthattheidentification of non-attendance and goodness of fit of different random parameter logit models that accommodate ANA are better when the framework developed in this research is applied. When comparing welfare estimates from the HH and PWRA approach, significant differences are observed. Consequently, the flexibility of the PWRA notably contributes to revealing context-specific ANA patterns that can help to provide more accurate welfare measures and therefore policy recommendations.

Modeling loss in the case of default is a crucial task for financial institutions to support the decision making process in the risk management framework. It has become an inevitable part of modern debt collection strategies to keep promising loans on the banking book and to write off those that are not expected to be recovered at a satisfactory level. Research tends to model Loss Given Default directly or to decompose it based on the dependent variable distribution. Such an approach neglects the patterns which exist beneath the recovery process and are mainly driven by the activities made by collectors in the event of default. To overcome this problem, we propose a decomposition of the LGD model that integrates cures, partial recoveries, and write-offs into one equation, defined based on common collection strategies. Furthermore, various levels of data aggregation are applied to each component to reflect the domain that influences each stage of the default process. To assess the robustness of our approach, we propose a comparison with two benchmark models on two different datasets. We assess the goodness of fit on out-of-sample data and show that the proposed decomposition is more effective than state-of-the-art methods, maintaining a strong level of interpretability.

Vine copula, constructed from bivariate copulas, provides great flexibility in modelling complex high-dimensional dependence. When applied to multi-population mortality modelling, vine copula yields significant improvement over traditional multivariate copulas. In this paper, we propose to capture time-varying features in mortality dependence with dynamic regular vine (R-vine) copula which is built from bivariate copulas with time-varying dependence parameters. We develop two dependence dynamics for R-vine copulas and illustrate the selection and estimation of dynamic R-vine copulas using mortality data from eight populations. The estimated R-vine copulas using the proposed dependence dynamics are shown to yield better goodness of fit than both static and regime-switching vine copulas. We further demonstrate the simulation of mortality paths using dynamic R-vine copulas and examine the impact of vine copula choice on the assessed effectiveness of longevity hedge.

The Campbell and Vuolteenaho (Am Econ Rev 94(5):1249–1275, 2004) two–beta model decomposes the systematic risk in the sensitivity of cash flow and discount rate change. We employed this model, which we call the Two Beta Decomposition Model (TBDM) and found that this model is useful to compute the cost of capital for unlisted companies (UCs) via a proxy from listed companies. This model includes not only the accounting return reaction to long-term changes in consumption, but also links fundamental reactions to temporal changes in risk aversion. We test this model along with three traditional alternatives that are potentially useful in computing the cost of equity for UCs: accounting betas (AB), unlevered betas (UB), and operational betas (OB). Our results show that AB, UB and TBDM can partially explain the cross-sectional variations of stock returns. Additionally, using a series of non-parametric ranking test along with several statistics of goodness of fit, we found that the TBDM is the model that produces the best fit among competing models followed by the UB which is currently the most used among proxy methods.

Strong empirical links exist between the number of years spent schooling and earnings. How­ever, the relationship may be masked due to the effect of unobserved factors that influence both wages and schooling. Two of the main econometric models, namely fixed-effects and se­lection-effects, used to analyse returns to schooling were compared using monozygotic and di­zygotic twins’ datasets in Ghana. The efficiency of the models was assessed based on the stan­dard errors associated with the return to schooling estimates. Goodness of fit measures was used as a basis for comparison of the performance of the two models. The results revealed that based on their standard errors, the regression estimates from the selection effects model (MZ = 0.1014±0.0197; DZ = 0.0947±0.0095) were more efficient than the regression estimates from the fixed-effects model (MZ = 0.1115±0.0353; DZ = 0.082±0.0127). However, the AICc values of the fixed effects model (MZAICc = 57.8 and DZAICc = 105.4) were smaller than the AICc values of the selection effects model (MZAICc = 151.6 and DZAICc = 221.6). Findings from the study indicate that, although both models produced consistent estimates of the economic returns to schooling, the fixed effects model provided a better fit to the twins’ data set.

Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed.

Summary Methods and models for longitudinal data with categorical, multi-dimensional outcomes are quite limited, but they are essential to the study of life histories. For example, in the Swiss Household Panel, information on the co-residence and professional status of several thousand individuals is available through to age 45 years. Interest centres on the time and order of life course events such as having children and working full or part time and the duration of the phases that they delineate. With data of this type, optimal matching and clustering algorithms relying on a distance metric or parametric models of duration in a competing risks framework are used; the appropriateness of each derives from competing goals and orientation. We prefer model-based approaches when certain goals are paramount: simulation of individual trajectories; adjusting for time-dependent covariates; handling multistate trajectories and missing outcomes. Several of these goals are particularly challenging when the number of states is of moderate size, and many transitions are infrequent and/or time inhomogeneous. Using the Swiss Household Panel, we demonstrate the appropriateness of latent class growth curve models for analysing sequence data. In particular, models including heterogeneous dependence structure provide new techniques for assessing goodness of fit as well as yield insights into social processes.

In Pharmacokinetic (PK) and Pharmacodynamic (PD), the hazard rate functions play a central role in modelling time-to-event data. In the context of assessing the appropriateness of a given parametri...

Established tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms can be manipulated by changing the order of variables in the forecasting model. We derive order‐invariant tests. The new tests are applicable to densities of arbitrary dimensions and can deal with parameter estimation uncertainty and dynamic misspecification. Monte Carlo simulations show that they often have superior power relative to established approaches. We use the tests to evaluate generalized autoregressive conditional heteroskedasticity‐based multivariate density forecasts for a vector of stock market returns and macroeconomic forecasts from a Bayesian vector autoregression with time‐varying parameters.

Rank-based procedures are commonly used for inference in copula models for continuous responses whose behavior does not depend on covariates. This paper describes how these procedures can be adapted to the broader framework in which (possibly non-linear) regression models for the marginal responses are linked by a copula that does not depend on covariates. The validity of many of these techniques can be derived from the asymptotic equivalence between the classical empirical copula process and its analog based on suitable residuals from the marginal models. Moment-based parameter estimation and copula goodness-of-fit tests are shown to remain valid under weak conditions on the marginal error term distributions, even when the residual-based empirical copula process fails to converge weakly. The performance of these procedures is evaluated through simulation in the context of two general insurance applications: micro-level multivariate insurance claims, and dependent loss triangles.

This paper analyzes credit rating default dependencies in a multisectoral framework. Using Mergent's FISD database, we study the default series in the U.S. over the last two decades, disaggregating defaults by industry-sector group. During this period, two main waves of default occurred: the implosion of the “dot-com” bubble and the global financial crisis. We estimate a Multivariate Autoregressive Conditional Poisson model according to the biweekly number of defaults that occurred in different sectors of the economy from 1996 to 2015. We discuss the contagion effect between sectors in two ways: the degree of transmission of the probability of default from one sector to another, i.e., the “infectivity” of the sector, and the degree of contagion of one sector from another, i.e., the “vulnerability” of the sector. Our results show differences between the sectors' relations during the first and second part of our sample. We add some exogenous variables to the analysis and evaluate their contribution to the goodness of fit.

In the past decade, panel data models using time-series observations of several geographical units have become popular due to the availability of software able to implement them. The aim of this study is an updated comparison of estimation techniques between the implementations of spatiotemporal panel data models across MATLAB and R softwares in order to fit real mortality data. The case study used concerns the male and female mortality of the aged population of European countries. Mortality is quantified with the Comparative Mortality Figure, which is the most suitable statistic for comparing mortality by sex over space when detailed specific mortality is available for each studied population. The spatial dependence between the 26 European countries and their neighbors during 1995–2012 was confirmed through the Global Moran Index and the spatiotemporal panel data models. For this reason, it can be said that mortality in European population aging not only depends on differences in the health systems, which are subject to national discretion but also on supra-national developments. Finally, we conclude that although both programs seem similar, there are some differences in the estimation of parameters and goodness of fit measures being more reliable MATLAB. These differences have been justified by detailing the advantages and disadvantages of using each of them.

It has been observed that certain economic and financial variables commonly exhibit switching behavior depending on their magnitude. This phenomenon in general cannot be naturally captured by the linear regression (LR), which assumes a linear relationship between the dependent and explanatory variables. To decipher investor behavior more appropriately by accounting for this observation, a switching-regime regression (SRR) is proposed and applied to the S&P 500 market return with respect to seven explanatory variables. It is shown that, compared with LR, the new regression results in a significantly improved adjusted $$R^2$$ , increasing from less than $$4\%$$ to over $$50\%$$ . In addition, SRR yields better out-of-sample forecasting performance, besides that the fitted values from the new regression even resemble the dip during the 2008 financial crisis, while those from LR do not. The study thus indicates that the switching-regime regression improves significantly the statistical properties including the goodness of fit as well as conforms more to investor behavior theory.

We propose a parsimonious statistical model of firm competition where structural differences in the strength of competitive pressure and the magnitude of return fluctuations above and below the system-wide benchmark translate into a skewed Subbotin or asymmetric exponential power (AEP) distribution of returns to capital. Empirical evidence from US data illustrates that the AEP distribution compares favorably to popular alternative models such as the symmetric or asymmetric Laplace density in terms of goodness of fit when entry and exit dynamics of markets are taken into account.

The purpose of this study was to develop a valid and reliable rubric to be used for the evaluation of large ensemble wind band performances. The guiding questions for this study were: (a) what are the psychometric qualities (i.e., reliability and validity) of the scale developed to assess wind band ensemble performance at the high school level? (b) how do the items fit the model and vary in difficulty? (c) how does the structure of the rating scale vary across individual items? and (d) how can the rating scale be transferred into an informative rubric? The primary data analysis tool used in this study was the Multifaceted Rasch Partial Credit Measurement Model. Music content experts ( N = 20) were solicited to evaluate 40 wind band performances, each evaluator listening to four. A 4-point Likert-type rating scale (e.g., Strongly Agree, Agree, Disagree, and Strongly Disagree) was used to evaluate each recorded performance. Results indicated good model data fit and resulted in a final rubric containing 24 items ranging from two to four performance categories. Implications for classroom teaching and consequential validity are discussed.

This simulation study was conducted to compare the performances of Frequentist and Bayesian approaches in the context of power to detect model misspecification in terms of omitted cross-loading in CFA models with respect to the several variables (number of omitted cross-loading, magnitude of main loading, number of factors, number of indicators per factor and sample size) and (to) investigate the efficiency of BSEM approach to detect cross-loadings. BSEM approach allows including and estimating certain number of cross-loadings by specifying informative piror with small-variance for cross-loadings in the model. By this way, BSEM approach enables researchers to come up with models that better represent the substantive theory. At this simulation study, model misspecification was considered as major misspecification (cl=0.3) and minor misspecification (cl=0.1) according to the amount of omitted cross-loading. Results of this study revealed that Frequentist approach was so sensitive to minor model misspecification whereas Bayesian approach with non-informative prior was so sensitive to the major model misspecification. Finally, it was concluded that the power of BSEM approach to detect cross-loading varied according to the both amount and number of cross-loadings and for large amount of cross-loading the performance of this approach was so well.

Despite being extensively studied, the organizational commitment still remains in the research agenda due to a lack of consensus on conceptual and structural issues. Therefore, this research proposed to refine the Organizational Commitment Bases Scale (EBACO) in search for an instrument with better psychometric properties and a better theoretical adjustment. The research was divided into two phases, the first one was eminently exploratory, carried out with 149 observations, and evaluated the EBACO’s psychometric fitting with the reduction of two bases. The second phase, confirmatory, occurred in 45 organizations - 27 public and 18 private, with 812 observations. The confirmatory factor analysis was adopted to evaluate the psychometric properties and the theoretical adequacy of the refined model. The results indicated that with five dimensions the EBACO model presented higher levels of KMO, Cronbach’s Alpha, and explained variance. It was also verified that the model discussed obtained adequate values of the goodness of fit indexes χ2/df, RMSEA, CFI, TLI and RMR. Finally, it was verified that the refined model, EBACO-R, presented a convergent validity, ensuring a greater suitability of the scale to the theory. Furthermore, the model refinement, excluding the bases, contributes to the discussion by Osigweh (1989) about the 'concept stretching' commitment.

Curve-fitting methods are widely used in derivatives markets for construction of the implied volatility surface (IVS). Here we discuss the goodness of fit, smoothness, and economic implications of 12 distinctive curve-fitting methods. The choice of method relies on specific requirements. When fitting the Chicago Board Options Exchange data, three interpolation methods were found to provide the best goodness, whereas quadratic regression, the Nadaraya–Watson kernel regression, and the theoretical Carr–Wu model generate the smoothest surfaces. Because of the irregular nature of the emerging options market data, we propose a transformation method to improve three statistical methods to satisfy the Lee’s condition. Empirically, quadratic regression provides the best goodness when fitting the China 50ETF options data. In addition, the Carr–Wu model is a very good alternative because it natively satisfies the Lee’s condition and has economic implications.

Because model misspecification can lead to inconsistent and inefficient estimators and invalid tests of hypotheses, testing for misspecification is critically important. We focus here on several general purpose goodness-of-fit tests which can be applied to assess the adequacy of a wide variety of parametric models without specifying an alternative model. Parametric bootstrap is the method of choice for computing the p-values of these tests however the proof of its consistency has never been rigourously shown in this setting. Using properties of locally asymptotically normal parametric models, we prove that under quite general conditions, the parametric bootstrap provides a consistent estimate of the null distribution of the statistics under investigation.

Numerous multivariate time series encountered in real applications display seasonal behavior. In this paper we consider portmanteau tests for testing the adequacy of structural multiplicative seasonal vector autoregressive moving-average (SVARMA) models under the assumption that the errors are uncorrelated but not necessarily independent (i.e. weak SVARMA). We study the asymptotic distributions of residual autocorrelations at seasonal lags of multiple of the length of the seasonal period under weak assumptions on the noise. We deduce the asymptotic distribution of the proposed multivariate portmanteau statistics, which can be quite different from the usual chi-squared approximation used under independent and identically distributed (iid) assumptions on the noise. A set of Monte Carlo experiments and an application of U.S. monthly housing starts and housing sold are presented.

Most statistical methods are based on models, but most practical applications ignore the fact that the results depend on the model as well as on the data. This paper examines the size of this model dependence, and finds that there can be very considerable variation between the results of fitting different models to the same data, even if the models being considered are restricted to those which give an acceptable fit to the data. Under reasonable regularity conditions, we show that different empirically acceptable models can give rise to non-overlapping confidence intervals for the same parameter. Application papers need to recognize that the validity of conventional statistical results rests on the assumption that the underlying model is known to be correct, and that this is a much stronger requirement than merely confirming that the model gives a good fit to the data. The problem of model dependence is only partially resolved by using formal methods of model selection or model averaging.

Since their introduction, quanto options have steadily gained popularity. Matching Black–Scholes-type pricing models and, more recently, a fat-tailed, normal tempered stable variant have been established. The objective here is to empirically assess the adequacy of quanto-option pricing models. The validation of quanto-pricing models has been a challenge so far, due to the lack of comprehensive data records of exchange-traded quanto transactions. To overcome this, we make use of exchange-traded structured products. After deriving prices for composite options in the existing modeling framework, we propose a new calibration procedure, carry out extensive analyses of parameter stability and assess the goodness of fit for plain vanilla and exotic double-barrier options.

Refereed/Peer-reviewed The issue of property evaluation and appraisal has been of high interest for private and public agents involved in the housing industry for the purposes of trade, insurance and tax. This paper aims to investigate how different factors related to the location of a property affect its price over time. The predictive models applied in this research are driven by real estate transactions data of Tehran Metropolitan Area, captured from open data available to the public. The parameters of the functions that describe the behavior of the housing market are estimated through applying different types of statistical models, including ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR). This suite of models has been run in order to compare their efficiency and accuracy in predicting the variations in housing price. The GTWR model showed significantly better performance than OLS and GWR models, as the goodness of ft index (adjusted R²) improved by 22 percent. Therefore, spatio-temporal non-stationary modelling is significant in the explanation of the variations in housing value and the GTWR coefficients were found more reliable. Three internal factors (size of building; building age; building quality), and eight external factors (topography; land-use mix; population density; distance to city center; distance to subway station; distance to regional parks; distance to highway; distance to airport) influence the property price, either positively or negatively. Moreover, using significant variables that extracted from regression models, the optimum number of housing value clusters is generated using the spatial 'k'luster analysis by tree edge removal (SKATER) method. Five clusters of housing patterns were recognized. The policy implication of this paper is grouping of Metropolitan Tehran housing value data into five clusters with different characteristics. The varying factors influencing housing value in each cluster are different, making this data analysis technique useful for policy-makers in the housing sector.

The finance literature provides substantial evidence on the dependence between international bond markets across developed and emerging countries. Early works in this area were based on linear models and multivariate GARCH models. However, based on the limitations of these models this paper re-examines the non-linearity, multivariate and tail dependence structure between government bond markets of the US, UK, Japan, Germany, Canada, France, Italy, Australia and the Eurozone, from January 1970 to February 2019 using ARMA-GARCH based pair- copula models. We find that the bond markets in our sample tend to have both upper tail dependence in terms of positive shocks and lower tail dependence in terms of negative shocks. The estimated C-vine shows Eurozone has the highest average dependency. The D-vine, with optimal chain dependency structure shows the best order of connectedness to be the UK, the USA, Italy, Japan, Eurozone, France, Canada, Germany and Australia. The R-vine copula results underline the complex dynamics of bond market relations existing between the selected economies. The estimated R-vine shows Eurozone, Germany and Australia are the most inter-connected nodes. The multivariate distribution structure (interdependency) of bond markets for all countries were modelled with the C-vine, D-vine and R-vine copulas. In this application, the R-vine copula allows for detailed modelling of all bond markets and hence provides a more accurate goodness of fit and mean square error for the interdependency between all markets. In light of the changing volatility in bond markets, we conduct additional tests using time-varying copulas and find that the dependence structure among the bond markets examined is time-varying with the dynamic dependence parameter plots revealing that the nature of the dependence structure is intense during crisis periods. post-print 1625 KB

With tremendous associated economic and social costs of crashes, researchers have been trying not only to identify the factors affecting crashes, but also to estimate those coefficients in the most accurate ways. Estimating model coefficients without accounting for a correct distribution would result in biased and erroneous results. This risk especially holds true when modeling skewed equivalent property damage only (EPDO) crashes with a preponderance of zeroes. The distribution of EPDO is known for not being modeled with known distributions such as Poisson or negative binomial distributions. This issue is highlighted in particular for a mountainous state like Wyoming that has very low traffic levels and a severely high crash rate. In addition, we included barriers in the model that did not experience any crashes but did suffer from being under-designed by geometric architects, thereby adding to the number of zero count observations. Various models with different distributional characteristics were considered and compared in this study. Comparisons were not just made across models in terms of their goodness of fit, but the estimated coefficients were also compared to see the impact of considering the wrong distributional assumptions on model parameter estimates. As the objectives of this study are to implement the identified results for optimization purposes and locate hazardous locations that could host future crashes, the results highlight accurate model estimations and the consequences of a failure to account for the right distributions. After conducting different goodness-of-fit measures, a hurdle model was proposed in this study to accommodate observations with zero crashes, and to account for a sparse distribution of EPDO crashes in the state of Wyoming. For the hurdle model, binary logistic regression was used to account for observations with zero crashes, while the negative binomial method was considered for non-zero observations. The findings of this study have direct implications on the allocation of limited funds for policymakers in Wyoming, as optimization could be conducted on the geometric characteristics of traffic barriers in future studies.

Structural equation modeling is an ideal data analytical tool for testing complex relationships among many analytical variables. It can simultaneously test multiple mediating and moderating relationships, estimate latent variables on the basis of related measures, and address practical issues such as nonnormality and missing data. To test the extent to which a hypothesized model provides an appropriate characterization of the collective relationships among its variables, researchers must assess the “fit” between the model and the sample’s data. However, interpreting estimates of model fit is a problematic process. The traditional inferential test of model fit, the chi-square test, is biased due to sample size. Fit indices provide descriptive (i.e., noninferential) values of model fit (e.g., comparative fit index, root-mean-square error of approximation), but are unable to provide a definitive “acceptable” or “unacceptable” fit determination. Marcoulides and Yuan have introduced an equivalence-testing technique for assessing model fit that combines traditional descriptive fit indices with an inferential testing strategy in the form of confidence intervals to facilitate more definitive fit conclusions. In this paper, we explain this technique and demonstrate its application, highlighting the substantial advantages it offers the life sciences education community for drawing robust conclusions from structural equation models. A structural equation model and data set (N = 1902) drawn from previously published research are used to illustrate how to perform and interpret an equivalence test of model fit using Marcoulides and Yuan’s approach.

Economic recessions occur with varying duration and intensity and may entail substantial losses in terms of GDP, employment, household income, and investment spending. In this work, we propose a statistical model for the time intervals between recessions that accounts for the state of the economy and the impact of market adjustments and regulatory changes. The model uses a generalized renewal process based on the Gumbel distribution (GuGRP) in which times between consecutive events are conditionally independent. We also present a novel goodness of fit test tailored to the GuGRP that validates the use of the statistical model for the analysis of recessions. Analyzing recessions in the U.S. and Europe, we demonstrate that the statistical model characterizes well recession inter-arrival times and that the model performs better than simpler, commonly used distributions. In addition, the presented statistical model enables us to compare the adjustment processes in different economies and to forecast the occurrence of future recessions.

In this paper, we propose a general family of Birnbaum–Saunders autoregressive conditional duration (BS-ACD) models based on generalized Birnbaum–Saunders (GBS) distributions, denoted by GBS-ACD. We further generalize these GBS-ACD models by using a Box-Cox transformation with a shape parameter λ to the conditional median dynamics and an asymmetric response to shocks, this is denoted by GBS-AACD. We then carry out a Monte Carlo simulation study to evaluate the performance of the GBS-ACD models. Finally, an illustration of the proposed models is made by using New York stock exchange (NYSE) transaction data.

This paper analyzes the choice behavior of the high-speed rail Italian passengers, with a random utility latent class structure for modelling travelers' tastes. Latent class structures are widely used for addressing inter/intra-respondent taste variation in transport field. The simple Multinomial Logit (MNL) is commonly used as a kernel model (in the sense it will be clarified in the paper) into such latent class structures. The resulting random utility model (RUM) is well known in the literature as latent class Multinomial Logit (LC-MNL). In this study, we propose to test kernel models with different inherent distributional assumptions for random residuals of the utilities than that of the simple Multinomial Logit (MNL), namely the Gumbel distribution assumption (Extreme Value type I). Such different kernel RUMs are obtained by assuming random residuals of the utilities distributed as: Weibull (i.e. Extreme Value-type III), Exponential, Rayleigh, Pareto and q-Gumbel. Results on an estimation exercise, based on a dataset coming from a stated preference (SP) survey, show that some of these assumptions allow the resulting LC-RUM to outperform the well-known LC-MNL in terms of goodness of fit (GOF).

Over the last few decades, many robust statistics have been proposed in order to assess the fit of structural equation models. To date, however, no clear recommendations have emerged as to which te...

In recent years, there is growing recognition that common unobserved factors that influence crash frequency by one attribute level are also likely to influence crash frequency by other attribute levels. The most common approach employed to address the potential unobserved heterogeneity in safety literature is the development of multivariate crash frequency models. The current study proposes an alternative joint econometric framework to accommodate for the presence of unobserved heterogeneity – referred to as joint negative binomial-multinomial logit fractional split (NB-MNLFS) model. Furthermore, the study undertakes a first of its kind comparison exercise between the most commonly used multivariate model (multivariate random parameter negative binomial model) and the proposed joint approach by generating an equivalent log-likelihood measure. The empirical analysis is based on the zonal level crash count data for different collision types from the state of Florida for the year 2015. The model results highlight the presence of common unobserved effects affecting the two components of the joint model as well as the presence of parameter heterogeneity. The equivalent log-likelihood and goodness of fit measures clearly highlight the comparable performance offered by the proposed joint model relative to the commonly used multivariate approach. Overall, the model interpretations and fit measures clearly highlight the potential complementary role of the proposed approach for crash frequency analyses.

Fuel-factor nesting structures are critical features of CGE models but have rarely been chosen in a robust manner. This study thoroughly examines two popular fuel-factor nesting structures in CGE models of China, namely KL-E 1 and KE-L. 2 We use non-linear algorithms to estimate the underlying CES production functions of these two structures. We recommend the latter to be used in CGE models of China, as it exhibits 1) better goodness of fit, 2) better convergence, and 3) better compliance with economic meanings. Our estimations show that the ranges for the inner- and outer-CES parameters 3 in our preferred KE-L structure are 0.6–0.8 and 0.7–0.9, respectively. Applying these in a CGE model, we find nesting strategies used in previous CGE exercises might have overestimated energy conservation costs in China. A decomposition shows that, using our KE-L, changes in energy use are dominate by output effects rather than substitution effects.

We examine the extreme behaviors at both the lower 5% and 1% quantile levels of the three exchange rates series (Japanese yen, Korean won, and New Taiwan dollar) against the US dollar between 2002 and 2017. We employ two econometric tools for specific purposes: (1) to determine the bootstrap goodness of fit test for the Generalized Pareto distribution (GPD) of the tail behaviors proposed by Villasenor-Alva and Gonzalez-Estrada (2009); and (2) to identify, estimate, and test the hidden Markov model (HMM). The testing outcomes mostly reject the GPD assumption for this study. HMM estimation outcomes provide the detailed pictures. They evidence the multiple structural breaks in the returns and the gaps between the qualified extremes. The respective properties of the exchange markets are revealed in the comparisons of the estimation outcomes in terms of transition matrix, response parameter, and bivariate analysis. In general, Japanese yen is evaluated as a resilient safe haven currency. Korean won exhibits longer distress duration. New Taiwan dollar occurs with heavier losses with longer gaps.

Power law distributions have previously been observed in data like city-size distributions (Zipf’s Law), income distributions, and financial asset prices. In this paper, we explore the distribution of real estate prices in Charleston County, South Carolina. We fit power law, lognormal and exponential distributions to the data and compare the goodness of fit among the distributions. We find that the best fit distribution lies somewhere between the lognormal and power law distributions. We estimate how the power law exponent changes over time and find a potential relationship between the shape of the power law distribution and the bursting of the real estate bubble in 2007.

We use a multilevel approach to investigate whether a general and robust relationship between weather shocks and (internal and international) migration intentions can be uncovered in Western African countries. We combine individual survey data with measures of localized weather shocks for 13 countries over the 2008–2016 period. A meta-analysis on results from about 51,000 regressions is conducted to identify the specification of weather anomalies that maximizes the goodness of fit of our empirical model. We then use this best specification to document heterogeneous mobility responses to weather shocks. We find that variability in Standardized Precipitation Evapotranspiration Index/rainfall is associated with changing intentions to move locally or internationally in a few countries only. However, the significance, sign and magnitude of the effect are far from being robust and consistent across countries. These differences might be due to imperfections in the data or to differences in long-term climate conditions and adaptation capabilities. They may also suggest that credit constraints are internalized differently in different settings, or that moving internally is not a relevant option as weather conditions are spatially correlated while moving abroad is an option of last resort. Although our multilevel approach allows us to connect migration intentions with the timing and spatial dimension of weather shocks, identifying a common specification that governs weather-driven mobility decisions is a very difficult, if not impossible, task, even for countries belonging to the same region. Our findings also call for extreme caution before generalizing results from specific case studies.

Given the recent growth of bicycle-sharing systems (BSS) around the world, it is of interest to BSS operators/analysts to identify contributing factors that influence individuals’ decision processes in adoption and usage of bicycle-sharing systems. The current study contributes to research on BSS by examining user behavior at a trip level. Specifically, we study the decision process involved in identifying destination locations after picking up the bicycle at a BSS station. In the traditional destination/location choice approaches, the model frameworks implicitly assume that the influence of exogenous factors on the destination preferences is constant across the entire population. We propose a finite mixture multinomial logit (FMMNL) model that accommodates such heterogeneity by probabilistically assigning trips to different segments and estimate segment-specific destination choice models for each segment. Unlike the traditional destination choice based multinomial logit (MNL) model or mixed multinomial logit (MMNL), in an FMMNL model, we can consider the effect of fixed attributes across destinations such as users’ or origins’ attributes in the decision process. Using data from New York City bicycle-sharing system (CitiBike) for 2014, we develop separate models for members and non-members. We validate our models using hold-out samples and compare our proposed FMMNL model results with the traditional MNL and MMNL model results. The proposed FMMNL model provides better results in terms of goodness of fit measures, explanatory power and prediction performance.

This study considers a goodness-of-fit test for location-scale time series models with heteroscedasticity, including a broad class of generalized autoregressive conditional heteroscedastic-type models. In financial time series analysis, the correct identification of model innovations is crucial for further inferences in diverse applications such as risk management analysis. To implement a goodness-of-fit test, we employ the residual-based entropy test generated from the residual empirical process. Since this test often shows size distortions and is affected by parameter estimation, its bootstrap version is considered. It is shown that the bootstrap entropy test is weakly consistent, and thereby its usage is justified. A simulation study and data analysis are conducted by way of an illustration.

The logistic regression model constitutes a natural and simple tool to understand how covariates (when available) contribute to explain the topology of a binary network. After estimating the logistic parameters, one of the main questions which arises in practice is to assess the goodness of fit of the corresponding model. To address this problem, we add a general term, related to the graphon function of W-graph models, to the logistic function. Such an extra term aims at characterizing the residual structure of the network, that is not explained by the covariates. We approximate this new generic logistic model using a class of models with blockwise constant residual structure. This framework allows to derive a Bayesian procedure from a model based selection context using goodness-of-fit criteria. All these criteria depend on marginal likelihood terms for which we do provide estimates relying on two series of variational approximations. Experiments on toy data are carried out to assess the inference procedure. Finally, six networks from social sciences and ecology are studied to illustrate the proposed methodology. Logistic regression is a natural and simple tool to understand how covariates contribute to explain the topology of a binary network. Once the model is fitted, the practitioner is interested in the goodness-of-fit of the regression in order to check if the covariates are sufficient to explain the whole topology of the network and, if they are not, to analyze the residual structure. To address this problem, we introduce a generic model that combines logistic regression with a network-oriented residual term. This residual term takes the form of the graphon function of a W-graph. Using a variational Bayes framework, we infer the residual graphon by averaging over a series of blockwise constant functions. This approach allows us to define a generic goodness-of-fit criterion, which corresponds to the posterior probability for the residual graphon to be constant. Experiments on toy data are carried out to assess the accuracy of the procedure. Several networks from social sciences and ecology are studied to illustrate the proposed methodology.

Claims experience in non-life insurance is contingent on random eventualities of claim frequency and claim severity. By design, a single policy may possibly incur more than one claim such that the total number of claims as well as the total size of claims due on any given portfolio is unpredictable. For insurers to be able to settle claims that may occur from existing portfolios of policies at some future time periods, it is imperative that they adequately model historical and current data on claims experience; this can be used to project the expected future claims experience and setting sufficient reserves. Non-life insurance companies are often faced with two challenges when modeling claims data; selecting appropriate statistical distributions for claims data and establishing how well the selected statistical distributions fit the claims data. Accurate evaluation of claim frequency and claim severity plays a critical role in determining: An adequate premium loading factor, required reserve levels, product profitability and the impact of policy modifications. Whilst the assessment of insurers’ actuarial risks in respect of their solvency status is a complex process, the first step toward the solution is the modeling of individual claims frequency and severity. This paper presents a methodical framework for choosing a suitable probability model that best describes automobile claim frequency and loss severity as well as their application in risk management. Selected statistical distributions are fitted to historical automobile claims data and parameters estimated using the maximum likelihood method. The Chi-square test is used to check the goodness-of-fit for claim frequency distributions whereas the Kolmogorov-Smirnov and Anderson-Darling tests are applied to claim severity distributions. The Akaike information criterion (AIC) is used to choose between competing distributions. Empirical results indicate that claim severity data is better modeled using heavy-tailed and skewed distributions. The lognormal distribution is selected as the best distribution to model the claim size while negative binomial and geometric distributions are selected as the best distributions for fitting the claim frequency data in comparison to other standard distributions.

Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes) are deciding factors in customers’ consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products) would influence such decisions. This paper presents a network-based approach based on Exponential Random Graph Models to study customers’ consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles) besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers’ consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers’ decision-making.

In empirical corporate finance, firm size is commonly used as an important, fundamental firm characteristic. However, no research comprehensively assesses the sensitivity of empirical results in corporate finance to different measures of firm size. This paper fills this hole by providing empirical evidence for a “measurement effect” in the “size effect”. In particular, we examine the influences of employing different proxies (total assets, total sales, and market capitalization) of firm size in 20 prominent areas in empirical corporate finance research. We highlight several empirical implications. First, in most areas of corporate finance the coefficients of firm size measures are robust in sign and statistical significance. Second, the coefficients on regressors other than firm size often change sign and significance when different size measures are used. Unfortunately, this suggests that some previous studies are not robust to different firm size proxies. Third, the goodness of fit measured by R-squared also varies with different size measures, suggesting that some measures are more relevant than others in different situations. Fourth, different proxies capture different aspects of “firm size”, and thus have different implications. Therefore, the choice of size measures needs both theoretical and empirical justification. Finally, our empirical assessment provides guidance to empirical corporate finance researchers who must use firm size measures in their work.

The majority of stated choice experiments using best-worst responses do not control for the way in which the respondents process the attributes offered in each choice scenario. We speculate that various candidate processing strategies (CPS) are possible, but in practice the analyst is likely to choose one CPS and to assume that it is adopted by the respondents in modelling best-worst data. This decision is usually based on model goodness-of-fit measures and/or the order of the best worst questions under a certain behavioural choice rule, typically utility maximisation. This paper proposes the use of the irrelevance of state-wise dominated alternative (ISDA) axiom as a theoretical benchmark to identify the CPS and the behavioural choice rule respondents are most likely to adopt (at least up to a probability). An empirical assessment using a road pricing reform dataset collected in Sydney suggests that a test based on an incidence of compliance with ISDA produces more convincing evidence than a test that is based on goodness of fit measures. The results suggest that respondents seem to process the information and respond to the worst question first, even though it is structured after the best question. Individual heterogeneity in the way they process the information is also examined and a way to capture this through a latent class model is promoted.

International audience; The mixture of distribution hypothesis (MDH) model offers an appealing explanation for the positive relation between trading volume and volatility of returns. In this specification, the information flows constitute the only mixing variable responsible for all changes. However, this single static latent mixing variable cannot account for the observed short-run dynamics of volume and volatility. In this paper, we propose a dynamic extension of the MDH that specifies the impact of information arrival on market characteristics in the context of liquidity frictions. We distinguish between short-term and long-term liquidity frictions. Our results highlight the economic value and statistical accuracy of our specification. First, based on some goodness of fit tests, we show that our dynamic two-latent factor model outperforms all competing specifications. Second, the information flows latent variable can be used to propose a new momentum strategy. We show that this signal improves once we allow for a second signal – the liquidity frictions latent variable – as the momentum strategies based on our model present better performance than the strategies based on competing models

This paper proposes an efficient option pricing model that incorporates stochastic interest rate (SIR), stochastic volatility (SV), and double exponential jump into the jump-diffusion settings. The model comprehensively considers the leptokurtosis and heteroscedasticity of the underlying asset’s returns, rare events, and an SIR. Using the model, we deduce the pricing characteristic function and pricing formula of a European option. Then, we develop the Markov chain Monte Carlo method with latent variable to solve the problem of parameter estimation under the double exponential jump-diffusion model with SIR and SV. For verification purposes, we conduct time efficiency analysis, goodness of fit analysis, and jump/drift term analysis of the proposed model. In addition, we compare the pricing accuracy of the proposed model with those of the Black–Scholes and the Kou (2002) models. The empirical results show that the proposed option pricing model has high time efficiency, and the goodness of fit and pricing accuracy are significantly higher than those of the other two models. Keywords: Option pricing model, Stochastic interest rate, Stochastic volatility, Double exponential jump, Markov Chain Monte Carlo with Latent Variable

In this study, the fundamental model of customer equity is studied and an alternative model is tested where attitudinal and behavioral customer loyalty are treated as two additional individual components of customer equity. In gathering required data, survey method is used with 377 respondents based on judgement sampling. In order to test the proposed model, a different calculation for customer equity is applied. In model testing, LISREL package program is used and structural equation modelling (SEM) is applied. Results indicate that the proposed model stating that customer equity, has two more components, namely attitudinal loyalty and behavioral loyalty, is significant at 95% confidence level, has a high goodness of fit (GFI = .93) and has an acceptable level of root mean square error (RMSEA= .07). However, results also show a correlation between value equity and brand equity terms, which can be tested with different products or sample in future studies.

Summary The estimation of time series models with heavy-tailed innovations has been widely discussed, but corresponding goodness-of-fit tests have attracted less attention, primarily because the autocorrelation function commonly used in constructing goodness-of-fit tests necessarily imposes certain moment conditions on the innovations. As a bounded random variable has finite moments of all orders, we address the problem by first transforming the residuals with a bounded function. More specifically, we consider the sample autocorrelation function of the transformed absolute residuals of a fitted generalized autoregressive conditional heteroscedastic model. With the corresponding residual empirical distribution function naturally employed as the transformation, a robust goodness-of-fit test is then constructed. The asymptotic distributions of the test statistic under the null hypothesis and local alternatives are derived, and Monte Carlo experiments are conducted to examine finite-sample properties. The proposed test is shown to be more powerful than existing tests when the innovations are heavy-tailed.

This paper investigates the potential asymmetry in international oil market and the Saudi Arabia sectors tail dependence. Our methodology incorporates copula-based dependence measures with ARMA-GARCH margins, during the period 2007–2016. Empirically, results are validated by establishing nonparametric tests of independence and Goodness-of-fit test for copula. The aim results have shown that the responses of the sector indices to oil price fluctuations are significantly asymmetric, especially for the left tail, except for Petrochemical sector index and Agricultures & Food sector index where tail dependence is symmetric. These dependence specifications of sectors with oil price extreme fluctuations increase the precision of portfolio re-balancing, which can guarantee higher performance.

Contemporary theories of associative learning are increasingly complex, which necessitates the use of computational methods to reveal predictions of these models. We argue that comparisons across multiple models in terms of goodness of fit to empirical data from experiments often reveal more about the actual mechanisms of learning and behavior than do simulations of only a single model. Such comparisons are best made when the values of free parameters are discovered through some optimization procedure based on the specific data being fit (e.g., hill climbing), so that the comparisons hinge on the psychological mechanisms assumed by each model rather than being biased by using parameters that differ in quality across models with respect to the data being fit. Statistics like the Bayesian information criterion facilitate comparisons among models that have different numbers of free parameters. These issues are examined using retrospective revaluation data.

The main aim for this paper is motivated by the usefulness of genetic algorithms (GAs) for the fitting of distribution models to financial market data. In detail, we use a GA along with the least squares method in order to achieve a more relatively accurate and robust approach for optimizing non-linear objective functions. The combination of these two methods is applied for fitting parametric distributions to a dataset of market index returns, improving the methodology of cumulative returns prediction. The process of extrapolation plays a fundamental role in this area of analysis, being essential to empirically fit a convenient distribution that describes the available data as closely as possible. For comparison and illustrative purpose, we analyse distribution models used in the financial literature for modelling such dataset, and then the practical application is carried out again on a more updated dataset from the same financial index. In addition, a brief simulation study is developed to illustrate th...