Abstract: Many ecological- and individual-level analyses of voting behaviour use multiple regressions with a considerable number of independent variables but few discussions of their results pay any attention to the potential impact of inter-relationships among those independent variables-do they confound the regression parameters and hence their interpretation? Three empirical examples are deployed to address that question, with results which suggest considerable problems. Inter-relationships between variables, even if not approaching high collinearity, can have a substantial impact on regression model results and how they are interpreted in the light of prior expectations. Confounded relationships could be the norm and interpretations open to doubt, unless considerable care is applied in the analyses and an extended principal components method for doing that is introduced and exemplified.

Abstract: We analyze a procedure common in empirical accounting and finance research where researchers use ordinary least squares to decompose a dependent variable into its predicted and residual components and use the residuals as the dependent variable in a second regression. This two‐step procedure is used to examine determinants of constructs such as discretionary accruals, real activities management, discretionary book‐tax differences, and abnormal investment. We show that the typical implementation of this procedure generates biased coefficients and standard errors that can lead to incorrect inferences, with both Type I and Type II errors. We further show that the magnitude of the bias in coefficients and standard errors is a function of the correlations between model regressors. We illustrate the potential magnitude of the bias in accounting research in four commonly used settings. Our results indicate significant bias in many of these settings. We offer three solutions to avoid the bias.

Abstract: Stepwise regression is a popular data-mining tool that uses statistical significance to select the explanatory variables to be used in a multiple-regression model. A fundamental problem with stepwise regression is that some real explanatory variables that have causal effects on the dependent variable may happen to not be statistically significant, while nuisance variables may be coincidentally significant. As a result, the model may fit the data well in-sample, but do poorly out-of-sample. Many Big-Data researchers believe that, the larger the number of possible explanatory variables, the more useful is stepwise regression for selecting explanatory variables. The reality is that stepwise regression is less effective the larger the number of potential explanatory variables. Stepwise regression does not solve the Big-Data problem of too many explanatory variables. Big Data exacerbates the failings of stepwise regression.

Abstract: Research Summary: In multivariate regression analyses of correlated variables, we sometimes observe pairs of estimated beta coefficients large in absolute magnitude and opposite in sign. T‐statistics are also large, suggesting meaningful findings. I found 64 recently published Strategic Management Journal articles with results exhibiting these characteristics. In this article, I demonstrate that such results may be Type 1 errors (false positives): If regressors are correlated via an unobservable common factor, estimated beta coefficients will misleadingly tend toward infinite magnitudes in opposite directions, even if the variables’ real effects are small and of the same sign. Diagnostics such as Variance Inflation Factors (VIF) will misleadingly validate Type 1 errors as legitimate results. After establishing general results via mathematical analysis and simulation, I provide guidelines for detection and mitigation. Managerial Summary: This article demonstrates mathematically how regression analyses with correlated independent variables may generate beta coefficients of opposite sign to the variables’ true effects. To assess the likelihood of this possibility, I propose that: if (a) absolute correlation of two independent variables is about ±0.3 or more (smaller correlations may be problematic for large data sets), (b) the two variables have beta coefficients of opposite sign, if correlated positively, and of the same sign, if correlated negatively, and (c) the bivariate correlation of one independent variable with the dependent variable is of the opposite sign from the beta coefficient, then the beta might be a false positive. To facilitate such analysis, authors should provide complete correlation tables, including dependent variables, interaction terms, and quadratic terms.

Abstract: Linear regression is a statistical procedure for calculating the value of a dependent variable from an independent variable. Linear regression measures the association between two variables. It is a modeling technique where a dependent variable is predicted based on one or more independent variables. Linear regression analysis is the most widely used of all statistical techniques. This article explains the basic concepts and explains how we can do linear regression calculations in SPSS and excel.

Abstract: Fixed effects estimators are frequently used to limit selection bias. For example, it is well known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable’s effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable’s effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls.

Abstract: This work contributes to this ongoing debate on the nature and the characteristics of propagation channels of crash events in international stock markets. Specifically, we investigate transmission mechanisms across stock markets along with effects from bond and currency markets. Our approach comprises a solid forecasting mechanism of the probability of a stock market crash event in various time frames. The developed approach combines different machine learning algorithms which are presented with daily stock, bond and currency data from 39 countries that cover a large spectrum of economies. Specifically, we leverage the merits of a series of techniques including Classification Trees, Support Vector Machines, Random Forests, Neural Networks, Extreme Gradient Boosting, and Deep Neural Networks. To the best of our knowledge, this is the first time that Deep Learning and Boosting approaches are considered in the literature as a means of predicting stock market crisis episodes. The independent variables included in our data contain information regarding both the two fundamental linkage channels through which financial contagion can be initiated: returns and volatility. We apply a suite of machine learning algorithms for selecting the most relevant variables out of a large set of proposed ones. Finally, we employ bootstrap sampling for adjusting the imbalanced nature of the available fitting dataset. Our experimental results provide strong evidence that stock market crises tend to exhibit persistence. We also find significant evidence of interdependence and cross-contagion effects among stock, bond and currency markets. Finally, we show that the use of Deep Neural Networks significantly increases the classification accuracy, while offering a robust way to create a global systemic early warning tool that is more efficient and risk-sensitive than the currently established ones. Thus, central banks may use these tools to early adjust their monetary policy, so as to ensure financial stability.

Abstract: Lagged dependent variables (LDVs) have been used in regression analysis to provide robust estimates of the effects of independent variables, but some research argues that using LDVs in regressions produces negatively biased coefficient estimates, even if the LDV is part of the data-generating process. I demonstrate that these concerns are easily resolved by specifying a regression model that accounts for autocorrelation in the error term. This actually implies that more LDV and lagged independent variables should be included in the specification, not fewer. Including the additional lags yields more accurate parameter estimates, which I demonstrate using the same data-generating process scholars had previously used to argue against including LDVs. I use Monte Carlo simulations to show that this specification returns much more accurate coefficient estimates for independent variables (across a wide range of parameter values) than alternatives considered in earlier research. The simulation results also indicate that improper exclusion of LDVs can lead to severe bias in coefficient estimates. While no panacea, scholars should continue to confidently include LDVs as part of a robust estimation strategy.

Abstract: Eta-squared (η2) and partial eta-squared (ηp2) are effect sizes that express the amount of variance accounted for by one or more independent variables. These indices are generally used in conjuncti...

Abstract: The literature proposes numerous so-called pseudo-R2 measures for evaluating “goodness of fit” in regression models with categorical dependent variables. Unlike ordinary least square-R2, log-likelihood-based pseudo-R2s do not represent the proportion of explained variance but rather the improvement in model likelihood over a null model. The multitude of available pseudo-R2 measures and the absence of benchmarks often lead to confusing interpretations and unclear reporting. Drawing on a meta-analysis of 274 published logistic regression models as well as simulated data, this study investigates fundamental differences of distinct pseudo-R2 measures, focusing on their dependence on basic study design characteristics. Results indicate that almost all pseudo-R2s are influenced to some extent by sample size, number of predictor variables, and number of categories of the dependent variable and its distribution asymmetry. Hence, an interpretation by goodness-of-fit benchmark values must explicitly consider these characteristics. The authors derive a set of goodness-of-fit benchmark values with respect to ranges of sample size and distribution of observations for this measure. This study raises awareness of fundamental differences in characteristics of pseudo-R2s and the need for greater precision in reporting these measures.

Abstract: Autoregressive distributed lag (ARDL) models are often used to analyse dynamic relationships with time series data in a single-equation framework. The current value of the dependent variable is allowed to depend on its own past realisations – the autoregressive part – as well as current and past values of additional explanatory variables – the distributed lag part. The variables can be stationary, nonstationary, or a mixture of the two types. In its equilibrium correction (EC) representation, the ARDL model can be used to separate the long-run and short-run effects, and to test for cointegration or, more generally, for the existence of a long-run relationship among the variables of interest. This talk serves as a tutorial for the ardl Stata command that can be used to estimate an ARDL or EC model with the optimal number of lags based on the Akaike or Schwarz/Bayesian information criteria. Frequently asked questions will be addressed and a step-by-step instruction for the Pesaran, Shin, and Smith (2001 Journal of Applied Econometrics) bounds test for the existence of a long-run relationship will be provided. This test is implemented as the postestimation command estat ectest which features newly computed finite-sample critical values and approximate p-values. These critical values cover a wide range of model configurations and supersede previous tabulations available in the literature. They account for the sample size, the chosen lag order, the number of explanatory variables, and the choice of unrestricted or restricted deterministic model components. The ardl command uses Stata’s regress command to estimate the model. As a consequence, specification tests can be carried out with the standard postestimation commands for linear (time series) regressions and the forecast command suite can be used to obtain dynamic forecasts.

Abstract: The past several decades of e-learning empirical research have advanced our understanding of the effective management of critical success factors (CSFs) of e-learning. Meanwhile, the proliferation of measures of dependent and independent variables has been overelaborated. We argue that a significant reduction in dependent and independent variables and their measures is necessary for building an e-learning success model, and such a model should incorporate the interdependent (not independent) process nature of e-learning success. We applied structural equation modeling to empirically validate a comprehensive model of e-learning success at the university level. Our research advances existing literature on CSFs of e-learning and provides a basis for comparing existing research results as well as guiding future empirical research to build robust e-learning theories. A total of 372 valid unduplicated responses from students who have completed at least one online course at a university in the Midwestern United States were used to examine the structural model. Findings indicated that the e-learning success model satisfactorily explains and predicts the interdependency of six CSFs of e-learning systems (course design quality, instructor, motivation, student-student dialog, student-instructor dialog, and self-regulated learning) and perceived learning outcomes.

Abstract: The paper herein will analyze the sale of iced products affected by variation of temperature. Firstly, we will collect the data of the forecast temperature last year and the sale of iced products and then conduct data compilation and cleansing. Finally, we will set up the mathematical regression analysis model based on the cleansed data by means of data mining theory. Regression analysis refers to the method of studying the relationship between independent variable and dependent variable. Linear regression model that corresponds to the practical situation is proposed in the paper, which is to set up simple linear regression model based on practical problem and then to implement the following with the help of the latest and most popular Python3.6. Python3.6 boasts the features of pure object-oriented, platform independence and concise and elegant language. So we will call the corresponding library function to predict the sale of iced products according to the variation of temperature, which will provide the foundation for the company to adjust its production each month, or even each week and each day. As a result, the situation of overproduction can be avoided. Moreover, the other situation as the profit will be affected by the lack of production since the rise of temperature will also be avoided. So the regression model also has reference value for the other fields of marketing.

Abstract: Data analysis is the process of organizing and sorting data into patterns, categories and basic description units so that themes can be found and work hypotheses can be formulated as based on data. Research data collection techniques include: interviews, questionnaires or questionnaires, observation and documentation. Correlation Analysis is a study that discusses the degree of closeness of the relationship between variables, which is expressed by the Correlation Coefficient. Regression analysis is a study of the dependence of one or more X (independent variables) on Y (dependent variable), with the intention to predict the value of Y. The term hypothesis comes from Greek, that is from the word hupodan thesis. Hupo means temporary, or lacks the truth or the truth is still weak. While thesis means statement or theory.

Abstract: The study aims to explore macroeconomic and banking industry-specific determinants of non-performing loans (NPLs) for Chinese banks, spanning from 2005 to 2014.,It uses three different models to explore the determinants. The first model has only macroeconomic variables as regressors; the second model has only banking industry-specific variables as independent variables; and the third model has macroeconomic and banking industry-specific variables as explanatory variables. Furthermore, system generalized method of moments estimation technique has been used to measure the coefficients of independent variables.,Gross domestic product (GDP) growth rate, effective interest rate, inflation rate, foreign exchange rate, type of bank, bank risk-taking behavior, ownership concentration, leverage and credit quality are significant determinants of NPLs in Chinese banks. Furthermore, the determinants of NPLs for listed and unlisted banks differ. Determinants of NPLs of listed banks include GDP, bank risk-taking behavior and credit quality. However, variation in NPLs of unlisted banks is explained by GDP, inflation rate, foreign exchange rate, bank risk-taking behavior, leverage and credit quality.,This study also finds that using only macroeconomic or banking industry-specific variables as regressors is not a right approach because it may lead to erroneous conclusions.

Abstract: To obtain a probabilistic model for a dependent variable based on some set of explanatory variables, a distributional approach is often adopted where the parameters of the distribution are linked to regressors. In many classical models this only captures the location of the distribution but over the last decade there has been increasing interest in distributional regression approaches modeling all parameters including location, scale, and shape. Notably, so-called non-homogeneous Gaussian regression (NGR) models both mean and variance of a Gaussian response and is particularly popular in weather forecasting. Moreover, generalized additive models for location, scale, and shape (GAMLSS) provide a framework where each distribution parameter is modeled separately capturing smooth linear or nonlinear effects. However, when variable selection is required and/or there are non-smooth dependencies or interactions (especially unknown or of high-order), it is challenging to establish a good GAMLSS. A natural alternative in these situations would be the application of regression trees or random forests but, so far, no general distributional framework is available for these. Therefore, a framework for distributional regression trees and forests is proposed that blends regression trees and random forests with classical distributions from the GAMLSS framework as well as their censored or truncated counterparts. To illustrate these novel approaches in practice, they are employed to obtain probabilistic precipitation forecasts at numerous sites in a mountainous region based on a large number of numerical weather prediction quantities. It is shown that the novel distributional regression forests automatically select variables and interactions, performing on par or often even better than GAMLSS specified either through prior meteorological knowledge or a computationally more demanding boosting approach.

Abstract: This study investigate the impact of government expenditure on economic growth in Nepal. Annual series data between 2002/03 to 2015/16 is used for the study. Economic growth is dependent variable whereas, total capital expenditure, total recurrent expenditure, agriculture, non-agriculture, industry, service and inflation are independent variables. The major objective of the study is to examine the effect of different component of government expenditure in economic growth in context to Nepal. Data are collected from economic survey of Nepal. The tools of analysis are the regression model between the variables, DW Test and for multicollonearity between the variables, VIF test is used. The empirical result shows that there is positive correlation between the dependent variable economic growth and the predictors like agricultural, non-agricultural, industry and service sector. Whereas, total current and recurrent expenditure and inflation are negatively related to economic growth. The beta coefficient is positively significantly for agricultural, non-agricultural, industry, and service sector, it implies that higher the investment in agricultural and non-agricultural sector higher would be economic growth. Similarly, higher the investment on industry and service sector of the country, higher would be economic growth. More specifically, the value of D-W value is 1.301 which implies that there is no autocorrelation between the variables.

Abstract: The research objective is to obtain empirical evidence that environmental performance and environmental disclosure affect the financial performance. The distinctive point of this study with previous research is the use of different variables and measurement method. Previous researchers examined the relationship between variables used while the researchers wanted to test the effect of independent variables on the dependent variable and to use control variables of the firm size and company growth. The hypothesis of this study is based on stakeholder theory, legitimacy theory, signalling theory and political economic theory. Purposive sampling method is used to gather the data of the manufacturing companies listed on the Indonesia Stock Exchange and PROPER program 2010-2014. Multiple linear regressions are used as the analysis method, and type of the data is secondary by using the documentation method. The study result shows that environmental performance and environmental disclosure positively significantly affect financial performance.

Abstract: Regression is a statistical term used for describing models that estimate the relationships among variables. Linear regression models study the relationship between a single dependent variable Y an...

Abstract: In this article, we describe the betamix command, which fits mixture regression models for dependent variables bounded in an interval. The model is a generalization of the truncated inflated beta regression model introduced in Pereira, Botter, and Sandoval (2012, Communications in Statistics—Theory and Methods 41: 907–919) and the mixture beta regression model in Verkuilen and Smithson (2012, Journal of Educational and Behavioral Statistics 37: 82–113) for variables with truncated supports at either the top or the bottom of the distribution. betamix accepts dependent variables defined in any range that are then transformed to the interval (0, 1) before estimation.

Abstract: We extend recent higher order concentration results in the discrete setting to include functions of possibly dependent variables whose distribution (on the product space) satisfies a logarithmic Sobolev inequality with respect to a difference operator that arises from Gibbs sampler type dynamics. Examples of such random variables include the Ising model on a graph with n nodes with general, but weak interactions, i.e. in the Dobrushin uniqueness regime, for which we prove concentration results of homogeneous polynomials, as well as random permutations, and slices of the hypercube with dynamics given by either the Bernoulli-Laplace or the symmetric simple exclusion processes.

Abstract: This paper aims to investigate the Validity of Altman z-score model to predict financial failure in insurance companies listed on Amman Stock Exchange (ASE) over the period 2011-2016. To achieve the goal of the study, the study depended on the different statistics analytical method and Multiple Linear Regression through doing the statistical analysis of the independent variables on the dependent variable related to the subject of the study through the (E-views) program in order to cover the analytical part of the study, in addition to the descriptive method through relying on books, periodicals, previous studies and financial reports of the insurance companies of the study’ sample, whether the direct or the indirect ones, to cover the theoretical part. The result of the study finds a high predictive power for Z-score model. Moreover, the findings reveal that Z-Score model could be valuable instrumental indicators for many users of financial statement such as financial managers, auditors, lenders, investors, to make right decisions in the face of financial failure.