Improving the validity of theory testing in logistics research using correlated components regression

The purpose of this logistics research methods article is to empirically test and introduce correlated components regression (CCR) as a new statistical technique that will improve the accuracy and validity in testing logistics theoretical models and hypothesised relationships. The purpose of the current study is to use CCR analysis as technique to address multicollinearity. Customer satisfaction data with parcel carriers is analysed with using CCR and multiple regression. To determine the best regression model of these two approaches, cross-validation R² values are used. In addition, comparisons are made to examine the standardised beta coefficients from both methods and to assess the possible impact from high levels of multicollinearity. Findings of the analysis suggest that CCR has a significantly higher cross-validation R² value and thus is determined the best model of these two approaches.     

A new bivariate generalized Poisson distribution

In this paper, a new bivariate generalized Poisson distribution (GPD) that allows any type of correlation is defined and studied. The marginal distributions of the bivariate model are the univariate GPDs. The parameters of the bivariate distribution are estimated by using the moment and maximum likelihood methods. Some test statistics are discussed and one numerical data set is used to illustrate the applications of the bivariate model.     

Sovereign default risk in OECD countries: Do global factors matter?

We study the determinants of sovereign default risk for a group of 23 OECD countries using quarterly data spanning the period between 2000:Q1 and 2016:Q3. Applying the recently developed panel dynamic heterogeneous common correlated effects estimator of Chudik and Pesaran (2015) our study innovates in considering potential endogeneity issues and cross-sectional dependence. We control for both global risk appetite and monetary policy stance, as well as country risk ratings. The results show that common factors are the main drivers of solvency risk for our set of countries. Specially relevant, we find that macroeconomic determinants are not significant predictors of long-term sovereign spreads.     

A bivariate Poisson count data model using conditional probabilities

The applied econometrics of bivariate count data predominantly focus on a bivariate Poisson density with a correlation structure that is very restrictive. The main limitation is that this bivariate distribution excludes zero and negative correlation. This paper introduces a new model which allows for a more flexible correlation structure. To this end the joint density is decomposed by means of the multiplication rule in marginal and conditional densities. Simulation experiments and an application of the model to recreational data are presented.     

Choice model specification,substitution and spatial structure effects: A simulation experiment

Several choice models are compared on their ability to reproduce two types of simulated data sets. The sets belonging to the first type were generated by using a probit model which is able to account for substitution effects while the data sets of the second type were generated by using a probit model which is able to account for spatial structure effects. The main conclusion of the experiment is that simple models like the multinomial logit model, although they perform less than the models used to generate the data, are sufficiently robust to reproduce the simulated data.     

Bivariate exponentiated‐exponential geometric regression model

A bivariate exponentiated‐exponential geometric regression model that allows negative, zero, or positive correlation is defined and studied. The model can accommodate under‐ or over‐dispersed count data. The regression model is based on the univariate exponentiated‐exponential geometric distribution, and the marginal means of the bivariate model are functions of the explanatory variables. The parameters of the bivariate regression model are estimated by using the maximum likelihood method. Some test statistics including goodness of fit are discussed. A simulation study is conducted to compare the model with the bivariate generalized Poisson regression model. One numerical data set is used to illustrate the application of the regression model.     

Monte Carlo methods for estimating,smoothing, and filtering one- and two-factor stochastic volatility models

One- and two-factor stochastic volatility models are assessed over three sets of stock returns data: S&P 500, DJIA, and Nasdaq. Estimation is done by simulated maximum likelihood using techniques that are computationally efficient, robust, straightforward to implement, and easy to adapt to different models. The models are evaluated using standard, easily interpretable time-series tools. The results are broadly similar across the three data sets. The tests provide no evidence that even the simple single-factor models are unable to capture the dynamics of volatility adequately; the problem is to get the shape of the conditional returns distribution right. None of the models come close to matching the tails of this distribution. Including a second factor provides only a relatively small improvement over the single-factor models. Fitting this aspect of the data is important for option pricing and risk management.     

COUNT DATA REGRESSION USING SERIES EXPANSIONS: WITH APPLICATIONS

A new class of parametric regression models for both under- and overdispersed count data is proposed. These models are based on squared polynomial expansions around a Poisson baseline density. The approach is similar to that for continuous data using squared Hermite polynomials proposed by Gallant and Nychka and applied to financial data by, among others, Gallant and Tauchen. The count models are applied to underdispersed data on the number of takeover bids received by targeted firms, and to overdispersed data on the number of visits to health practitioners. The models appear to be particularly useful for underdispersed count data. © 1997 John Wiley & Sons, Ltd.     

Tourism forecasting: accuracy of alternative econometric models

This study evaluates the forecasting accuracy of six alternative econometric models in the context of the demand for international tourism in Denmark. These econometric models are special cases of a general autoregressive distributed lag specification. In addition, the forecasting accuracy of two univariate time series models is evaluated for benchmark comparison purposes. The forecasting competition is based on annual data on inbound tourism to Denmark. Individual models are estimated for each of the six major origin countries over the period 1969–93 and forecasting performance is assessed using data for the period 1994–97. Rankings of these forecasting models over different time horizons are established based on mean absolute percentage error and root mean square percentage error.     

Panels with non-stationary multifactor error structures

The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently, work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference in the case of stationary panel regressions with a multifactor error structure. This paper extends this work and examines the important case where the unobservable common factors follow unit root processes. The extension to I(1)$I\left(1\right)$ processes is remarkable on two counts. First, it is of great interest to note that while intermediate results needed for deriving the asymptotic distribution of the panel estimators differ between the I(1)$I\left(1\right)$ and I(0)$I\left(0\right)$ cases, the final results are surprisingly similar. This is in direct contrast to the standard distributional results for I(1)$I\left(1\right)$ processes that radically differ from those for I(0)$I\left(0\right)$ processes. Second, it is worth noting the significant extra technical demands required to prove the new results. The theoretical findings are further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional-average-based method is robust to a wide variety of data generation processes and has lower biases than the alternative estimation methods considered in the paper.     

Forecasting monthly riverflow time series

Mean monthly flows from thirty rivers in North and South America are used to test the short-term forecasting ability of seasonal ARIMA, deseasonalized ARMA, and periodic autoregressive models. The series were split into two sections and models were calibrated to the first portion of the data. The models were then used to generate one-step-ahead forecasts for the second portion of the data. The forecast performance is compared using various measures of accuracy. The results suggest that a periodic autoregressive model, identified by using the partial autocorrelation function, provided the most accurate forecasts     

Technical efficiency in competing panel data models: a study of Norwegian grain farming

Estimation of technical efficiency is widely used in empirical research using both cross-sectional and panel data. Although several stochastic frontier models for panel data are available, only a few of them are normally applied in empirical research. In this article we chose a broad selection of such models based on different assumptions and specifications of heterogeneity, heteroskedasticity and technical inefficiency. We applied these models to a single dataset from Norwegian grain farmers for the period 2004–2008. We also introduced a new model that disentangles firm effects from persistent (time-invariant) and residual (time-varying) technical inefficiency. We found that efficiency results are quite sensitive to how inefficiency is modeled and interpreted. Consequently, we recommend that future empirical research should pay more attention to modeling and interpreting inefficiency as well as to the assumptions underlying each model when using panel data.     

A forecast comparison of volatility models: does anything beat a GARCH(1,1)?

We compare 330 ARCH‐type models in terms of their ability to describe the conditional variance. The models are compared out‐of‐sample using DM–$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is outperformed by more sophisticated models in our analysis of exchange rates, whereas the GARCH(1,1) is clearly inferior to models that can accommodate a leverage effect in our analysis of IBM returns. The models are compared with the test for superior predictive ability (SPA) and the reality check for data snooping (RC). Our empirical results show that the RC lacks power to an extent that makes it unable to distinguish ‘good’ and ‘bad’ models in our analysis. Copyright © 2005 John Wiley & Sons, Ltd.     

Forecasting Earnings Using Geographical Segment Data: Some UK Evidence

This study examines the question of whether or not the geographical segment data disclosed by UK companies can be used to generate forecasts of earnings that outperform forecasts based upon past consolidated data. One year ahead forecasts of attributable earnings or net income before extraordinary items are generated for both geographical sales data combined with a consolidated attributable earnings to sales margin and segmental earnings data. The forecasts are based upon forecasts of changes in the GNP of individual countries, both with and without the addition of forecasted inflation rates. It is found that models based upon both geographical segment sales and segment earnings outperform the random walk and random walk plus drift consolidated models for the years 1981 to 1983. The difference in the sizes of the errors generated by the segment data based models and the consolidated data based models are significant in the majority of cases especially when the errors are truncated at 100%. However, there is no additional advantage in terms of forecast accuracy in using segment earnings data rather than segment sales data.     

中国地区新型工业化发展模式与路径选择

本文基于生态经济效率视角,选取2005～2006年数据,使用数据包络分析模型和面板数据回归模型,对中国大陆31个省、直辖市和自治区的新型工业发展模式进行实证分析.本文认为,地区工业的生态经济效率是由地区工业的经济效率和生态效率共同构成,并据此划分出各地区在新型工业化发展进程中的4种新型工业发展模式,探讨了生态经济效率与经济效率和生态效率的关系.本文提出了新型工业发展路径的战略选择应符合生态经济效率的二维结构特征,为促进全国各地区工业经济又好又快的发展提出了建议.     

Nested Unobserved Heterogeneity not Orthogonal to Regressors

Many social phenomena are of a nested nature and recordedin hierarchical data, e.g., repeated observations of siblings (individual level) within families(context level). In these phenomena, unobserved heterogeneity can occur at both levels and may becorrelated with the regressors. This article addresses nested unobserved heterogeneity notorthogonal to the regressors, which is rarely discussed in the methodology literature. The articleextends the econometric one-factor fixed-effects approach to handle nested fixed effects.F tests for model comparisons are used to test whether the total heterogeneity exists and whetherthe total heterogeneity consists solely of contextual heterogeneity. It then introduces methods todecompose the two levels of heterogeneity and provides formal tests for each level andtheir relative importance, which are developed from classical ANOVA. To provide a stronger testfor time-varying context-specific heterogeneity, the article develops an estimator using thedifference-in-differences method. An empirical example of a study on child behavior problems isused to illustrate the methods introduced in the article.     

Penalized h-likelihood approach for variable selection in AFT random-effect models

Survival models allowing for random effects (e.g., frailty models) have been widely used for analyzing clustered time-to-event data. Accelerated failure time (AFT) models with random effects are useful alternatives to frailty models. Because survival times are directly modeled, interpretation of the fixed and random effects is straightforward. Moreover, the fixed effect estimates are robust against various violations of the assumed model. In this paper, we propose a penalized h-likelihood (HL) procedure for variable selection of fixed effects in the AFT random-effect models. For the purpose of variable selection, we consider three penalty functions, namely, least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), and HL. We demonstrate via simulation studies that the proposed variable selection procedure is robust against the misspecification of the assumed model. The proposed method is illustrated using data from a bladder cancer clinical trial.     

Forecasting multiparty by-elections using Dirichlet regression

By-elections, or special elections, play an important role in many democracies – but whilst there are multiple forecasting models for national elections, there are no such models for multiparty by-elections. Multiparty by-elections present particular analytic problems related to the compositional character of the data and structural zeros where parties fail to stand. I model party vote shares using Dirichlet regression, a technique suited for compositional data analysis. After identifying predictor variables from a broader set of candidate variables, I estimate a Dirichlet regression model using data from all post-war by-elections in the UK (n=468). The cross-validated error of the model is comparable to the error of costly and infrequent by-election polls (MAE: 4.0 compared to 3.6 for polls). The steps taken in the analysis are in principle applicable to any system that uses by-elections to fill legislative vacancies.     

Modelling and Estimation for Bivariate Financial Returns

Maximum likelihood estimates are obtained for long data sets of bivariate financial returns using mixing representation of the bivariate (skew) Variance Gamma (VG) and two (skew) t distributions. By analysing simulated and real data, issues such as asymptotic lower tail dependence and competitiveness of the three models are illustrated. A brief review of the properties of the models is included. The present paper is a companion to papers in this journal by Demarta & McNeil and Finlay & Seneta.     

Multivariate rotated ARCH models

This paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to fit them using a BEKK-type parameterization of the time-varying covariance whose long-run covariance is the identity matrix. This yields the rotated BEKK (RBEKK) model. The extension to DCC-type parameterizations is given, introducing the rotated DCC (RDCC) model. Inference for these models is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on the DJIA stocks.