Interaction Of Explanatory Variables Research Articles

Integrating data science into an econometrics course with a Kaggle competition

As vast amounts of data have become available in business in recent years, the demand for data scientists has been rising. The author of this article provides a tutorial on how one entry-level machine learning competition from Kaggle, an online community for data scientists, can be integrated into an undergraduate econometrics course as an engaging activity using only linear regression. Other techniques in this tutorial include log-linear and quadratic models and interactions of explanatory variables, which are common functional forms in econometrics. The competition allows students to use real-world data, build a predictive model, submit their model online to be evaluated instantaneously based on accuracy, and keep improving their model. R and Python codes are provided to make it possible for readers to replicate.

The Effect of Export Diversification on the Economic Growth of West-Asian Arab Countries

This paper examined the relationship between the patterns of export diversification and economic growth in west Asian Arab countries during the period (2000-2017) using World Bank, UNCTAD, and Pen World data. The study used the DX Diversification Index (UNCTAD). This index measures the country's export divergence from the world's diversification patterns. The OLS and FMOLS methods were used to estimate the study model coefficients. The study found that the exports diversification structures in this group diverged clearly from the world diversification pattern since the exports of group countries driven by a high degree of primary exports concentration. Moreover, the economic growth was influenced positively by human capital, primary products export growth, and the adoption of efficient anti-corruption policies, but, it negatively influenced by trade openness and population growth. The study disclosed a long-term relationship between economic growth and the interactions of explanatory variables in the model, while in the short run the relationship was weak and insignificant. Moreover, there was a reciprocal causality relationship between economic growth and the degree of exports diversification using (DX-index). The study recommends that Arab countries should reduce the reliance on the exports of primary products, enhancing the share of industrial-based exports to achieve sustainable growth and to reduce the impacts of primary export’s prices instability on their economies.

A New GNB Model of Crash Frequency for Freeway Sharp Horizontal Curve Based on Interactive Influence of Explanatory Variables

Crash prediction of the sharp horizontal curve segment of freeway is a key method in analyzing safety situation of freeway horizontal alignment. The target of this paper is to improve predicting accuracy after considering the elastic influence of explanatory variables and interaction of explanatory variables on crash rate prediction. In the paper, flexibility and elasticity are defined to express the elastic influence of explanatory variables and interaction of explanatory variables on crash rate prediction. Thus, we proposed 6 types of models to predict crash frequency. These 6 types of models include 2 NB models (models 1 and 2), 2 GNB models (models 3 and 4), one NB model (model 5), and one GNB model (model 6) with flexibility and variable elasticity considered. The alignment and crash report data of 88 sharp horizontal curve segments from different institutions were surveyed to build the crash models. Traffic volume, highway horizontal radius, and curve length have been assigned as explanatory variables. Subsequently, statistical analysis is performed to determine the model parameters and conducted sensitivity analysis by AIC, BIC, and Pseudo R2. The results demonstrated the effective use of flexibility and elasticity in analyzing explanatory variables and in predicting freeway sharp horizontal curve segments. In six models, the result of model 6 is much better than those of the other models by fitting rules. We also compared the actual results from crashes of 88 sharp horizontal curve segments with those predicted by models 1, 3, and 6. Results demonstrate that model 6 is much more reasonable than the others.

Nonlinear higher order abiotic interactions explain riverine biodiversity

AbstractAimTheory and experiments strongly support the importance of interactive effects of multiple factors shaping biodiversity, although their importance rarely has been investigated at biogeographically relevant scales. In particular, the importance of higher order interactions among environmental factors at such scales is largely unknown. We investigated higher order interactions of environmental factors to explain diversity patterns in a metacommunity of aquatic invertebrates at a biogeographically relevant scale and discuss the findings in an environmental management context.LocationAll major drainage basins in Switzerland (Rhine, Rhone, Ticino and Inn; 41,285 km2).MethodsRiverine α‐diversity patterns at two taxonomic levels (family richness of all benthic macroinvertebrates and species richness of Ephemeroptera, Plecoptera and Trichoptera) were examined at 518 sites across the basins. We applied a novel machine learning technique to detect key three‐way interactions of explanatory variables by comparing the relative importance of 1,140 three‐way combinations for family richness and 680 three‐way combinations for species richness.ResultsRelatively few but important three‐way interactions were meaningful for predicting biodiversity patterns among the numerous possible combinations. Specifically, we found that interactions among elevational gradient, prevalence of forest coverage in the upstream basin and biogeoclimatic regional classification were distinctly important.Main conclusionOur results indicated that a high prevalence of terrestrial forest generally sustains riverine benthic macroinvertebrate diversity, but this relationship varies considerably with biogeoclimatic and elevational conditions likely due to community composition of forests and macroinvertebrates changing along climatic and geographical gradients. An adequate management of riverine ecosystems at relevant biogeographical scales requires the identification of such interactions and a context‐dependent implementation.

Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice

In the current paper, we propose the use of a multivariate skew-normal (MSN) distribution function for the latent psychological constructs within the context of an integrated choice and latent variable (ICLV) model system. The multivariate skew-normal (MSN) distribution that we use is tractable, parsimonious in parameters that regulate the distribution and its skewness, and includes the normal distribution as a special interior point case (this allows for testing with the traditional ICLV model). Our procedure to accommodate non-normality in the psychological constructs exploits the latent factor structure of the ICLV model, and is a flexible, yet very efficient approach (through dimension-reduction) to accommodate a multivariate non-normal structure across all indicator and outcome variables in a multivariate system through the specification of a much lower-dimensional multivariate skew-normal distribution for the structural errors. Taste variations (i.e., heterogeneity in sensitivity to response variables) can also be introduced efficiently and in a non-normal fashion through interactions of explanatory variables with the latent variables. The resulting model we develop is suitable for estimation using Bhat’s (2011) maximum approximate composite marginal likelihood (MACML) inference approach. The proposed model is applied to model bicyclists’ route choice behavior using a web-based survey of Texas bicyclists. The results reveal evidence for non-normality in the latent constructs. From a substantive point of view, the results suggest that the most unattractive features of a bicycle route are long travel times (for commuters), heavy motorized traffic volume, absence of a continuous bicycle facility, and high parking occupancy rates and long lengths of parking zones along the route.

Soil bulk density pedotransfer functions of the humus horizon in arable soils

Soil bulk density (D b) is an important indicator of soil quality, site productivity and soil compaction and is widely used as an input variable for various models. However, D b is often not determined during actual soil surveys, and many attempts have been made to predict it from other more easily and routinely measured variables. The aim of this study was to develop a predictive model for D b estimation for the humus horizon of arable soils. In addition, we hypothesised that the mixed model approach would enable more accurate predictions than the multiple regression model. Estonian National Soil Monitoring data (1983–2008) were used for D b modelling. The dataset contains 17,293 entries for the humus horizon of Estonian arable soils. Mixed model methodology and linear multiple regression analysis were used to develop prediction models. Soil sampling depth, soil organic carbon and water content had the greatest effects on D b estimation. D b is determined through interactions of explanatory variables, and their contribution to D b variation in mixed model differs from variable contribution indicated by the simple regression model. We found that the regression model overestimates the importance of SOC. The mixed model prediction was an improvement compared to multiple regression models (MSE = 0.009 compared to MSE = 0.014). In addition, the predicted values in the mixed model showed good agreement with observed values, while the multiple regression model underestimated D b values above 1.6 g cm −3 and overestimated D b values below 1.2 g cm −3. We conclude that the mixed model approach enables higher prediction accuracy than multiple regression analysis. We propose a mixed model with 20 variables as a prediction model for the humus horizon of Estonian arable soils. The methodology is also applicable for other soil conditions.

Most Recent Birth Intervals in a Traditional Society: A Life Table and Hazards Regression Analysis

Retrospective data is obtained from the 1988-89 Survey of the Effects of Socioeconomic Factors on Fertility among Scheduled Castes in Rural Karimganj District Assam India (1805 scheduled caste households in selected villages). Multivariate hazard regression models are used to estimate the net effect of explanatory variables on the most recent (closed) birth interval and the interaction of explanatory variables. Rindfuss et al. guidelines and Namboodiri and Suchindran life-table techniques are used in constructing median birth intervals and the proportion not conceiving within 97 months and measuring the tempo and amount of fertility. Birth intervals exclude the postpartum amenorrhea period which has a median of 9-13 months in Assam. Findings show that the median most recent birth (MRB) interval is 2.25 years. The shortest interval is an average of 1.5 years and followed an infant death. MRB interval increases with decreasing age at marriage and increased age parity and spousal age differences. MRB interval increases also with increased income. The Wilcoxon test shows statistically significant differences between groups for the following factors: marriage age maternal age parity spousal age difference survival status of the next-to-the-last birth and family income. Multivariate hazards regression models show that females married at 19 years of age or higher have a greater likelihood of having a long MRB interval. Longer birth intervals are also more likely among the youngest female cohort aged less than 25 years the highest parity of 5 or higher and the lower income households. The youngest birth cohort has a risk of having an MRB over 2 times higher than the oldest birth cohort. The risk is 1.69 times higher for women with a prior childs death than for women without this experience. Maternal age is highly significantly related to parity and education. The length of MRB interval is significantly more likely to increase with increased maternal age of literate mothers with lower parities. A younger age at marriage older cohorts lower parities lower income groups higher age differences between spouses and having a prior surviving child are related to a slower pace at which the last child was born.