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Endogeneity or nonorthogonality in discrete choice models occurs when the systematic part of the utility is correlated with the error term. Under this misspecification, the model's estimators are inconsistent. When endogeneity occurs at the level of each observation, the principal technique used to treat for it is the control-function method, where a function that accounts for the endogenous part of the error term is constructed and is then included as an additional variable in the choice model. Alternatively, the latent-variable method can also address endogeneity. In this case, the omitted quality attribute is considered as a latent variable and modeled as a function of observed variables and/or measured through indicators. The link between the controlfunction and the latent-variable methods in the correction for endogeneity has not been established in previous work. This paper analyzes the similarities and differences between a set of variations of both methods, establishes the formal link between them in the correction for endogeneity, and illustrates their properties using a Monte Carlo experiment. The paper concludes with suggestions for future lines of research in this area.

Endogeneity is a potential threat to the validity of international marketing (IM) research. The purpose of this paper is to draw the attention of IM researchers to issues of endogeneity, to provide a comprehensive overview of the sources of endogeneity, and to discuss the statistical solutions.

The authors conduct the research in two steps. In the first step, the authors review the nature and sources of endogeneity specifically in IM research. In the second step, the authors review 60 IM papers on endogeneity published in the period 1995-2014 and assess the current practice of addressing endogeneity in the IM literature.

Sample selection bias and simultaneity are prevalent sources of endogeneity in IM research. Internationalization-performance relationship and innovation-export nexus are the two most frequently adopted models subject to potential endogeneity. Simply lagging the main independent variable is statistically flawed in dealing with endogeneity despite its popularity in IM research.

First, a careful choice and application of methods are critical when addressing endogeneity. Second, the authors suggest the employment of multiple study methods to address endogeneity robustly. Third, to prevent or solve endogeneity in structural equation modeling, researchers may either collect data on independent and dependent variables from different respondents or employ a two-stage least squares approach. Finally, it is helpful to design dedicated models to prevent proactively potential endogeneity a priori.

The contribution of this study is twofold. First, it is the first in the literature to discuss the endogeneity issue specifically in IM research. In particular, the study elaborates the origins and consequences of the three most frequently confronted types of endogeneity in IM research. Second, the authors assess the four major methods of addressing endogeneity in IM research with a systematic discussion of the literature from the last two decades. The authors offer suggestions on how to minimize endogeneity in model design and empirical implementation for future IM research.

This paper aims to investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models. Endogeneity problems in demand models occur when certain factors, unobserved by the researcher, affect both demand and the values of a marketing mix variable set by managers. For example, unobserved factors such as style, prestige or reputation might result in higher prices for a product and higher demand for that product. If not addressed properly, endogeneity can bias the elasticities of the endogenous variable and subsequent optimization of the marketing mix. In practice, instrumental variables (IV) estimation techniques are often used to remedy an endogeneity problem. It is well-known that, for linear regression models, the use of IV techniques with poor-quality instruments can produce very poor parameter estimates, in some circumstances even worse than those that result from ignoring the endogeneity problem altogether. The literature has not addressed the consequences of using poor-quality instruments to remedy endogeneity problems in non-linear models, such as logit-based demand models.

Using simulation methods, the authors investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models applied to finite-sample data sets. The results show that, even when the conditions for lack of parameter identification due to poor-quality instruments do not hold exactly, estimates of price elasticities can still be quite poor. That being the case, the authors investigate the relative performance of several non-linear IV estimation procedures utilizing readily available instruments in finite samples.

The study highlights the attractiveness of the control function approach (Petrin and Train, 2010) and readily available instruments, which together reduce the mean squared elasticity errors substantially for experimental conditions in which the theory-backed instruments are poor in quality. The authors find important effects for sample size, in particular for the number of brands, for which it is shown that endogeneity problems are exacerbated with increases in the number of brands, especially when poor-quality instruments are used. In addition, the number of stores is found to be important for likelihood ratio testing. The results of the simulation are shown to generalize to situations under Nash pricing in oligopolistic markets, to conditions in which cross-sectional preference heterogeneity exists and to nested logit and probit-based demand specifications as well. Based on the results of the simulation, the authors suggest a procedure for managing a potential endogeneity problem in logit-based demand models.

The literature on demand modeling has focused on deriving analytical results on the consequences of using poor-quality instruments to remedy endogeneity problems in linear models. Despite the widespread use of non-linear demand models such as logit, this study is the first to address the consequences of using poor-quality instruments in these models and to make practical recommendations on how to avoid poor outcomes.

This paper aims to foster a new discussion on endogeneity in hospitality and tourism research.

This paper elaborates on some of the common sources of endogeneity and the methods available to address them.

The authors present a variety of methods that can be used to mitigate the endogeneity problem. The authors provide simulation evidence regarding the risk of incorrectly selecting instrumental variables. The authors also provide several important practical recommendations for future research.

There are other issues and methods of correcting for endogeneity, that is not covered in this paper. However, the paper focuses on issues and methods that can be generalized to most contexts.

The paper provides practical recommendations for more rigorous regression estimation.

The purpose of this paper is to examine the drivers of default in the Malaysian housing market in the light of various policy interventions by the country’s central bank, and the government’s expressed concern to ensure balanced growth in the market. This paper assesses the importance of considering the endogeneity of loan-to-value (LTV) in predicting housing loan default and its implications.

In this paper, the author addresses the endogeneity problem in the LTV variable using two instrumental variables (IV) in this probit regression: national residential property gains tax and the statutory reserve ratio of Bank Negara Malaysia. This study uses the instrumental variable probit model to consider endogeneity bias. This study assumes a latent (unobservable) variable (Y*), representing a borrower’s tendency to default, which is associated linearly with the borrower’s and loan characteristics and other variables (Xi). This study uses individual borrower-level information of 43,156 housing loan borrowers from the files of a well-established housing bank in Malaysia.

This study’s results confirm that endogeneity causes a substantial difference in the magnitude of the estimated effects of LTV on the default tendency. At the lower values of LTV, the probability of default is over-estimated, and at the higher values, the default probability is substantially underestimated. Endogeneity bias also affects the estimated coefficients of loan and borrower characteristics. The authors find that the interest rate is less relevant in predicting loan default. Other loan characteristics, such as loan age, tenure, payment amount and the built-up area, are relevant. This study’s result confirms that the borrower’s location matters, and an increase in state gross domestic product per capita and an increase in the supply of residential units reduce default probability.

The present study did not explore the applicability of the “equity theory of default” in the Malaysian housing market. This study did not assess “strategic default” issues and the effect of borrowers’ characteristics, personality traits and self-control of Malaysian housing loan borrowers in the mortgage decision-making process. The evolving dynamics of the Malaysian housing market microstructure in property valuation remained unexplored in the present study.

The findings have crucial relevance in the decision-making process of commercial banks, the central bank and the government to frame policies to foster balanced growth and development in the housing market. The authors argue that striking a subtle balance between the concerns of financial stability and productive risk-taking by commercial banks in Malaysia remains a continuing challenge for the country’s central bank. The authors also argue that designing suitable taxation policies by the government can deliver its cherished goal of balanced development in the housing market.

Empirical research on the Malaysian housing market based on micro-level data is scarce due to a paucity of relevant data. This study is based on the individual borrower-level information of 43,156 housing loan borrowers from the files of a well-established housing bank in Malaysia. In this analysis, the authors find clear evidence of endogeneity in LTV and argue that any attempts to decipher the default drivers of housing loans without addressing the issue of endogeneity may lead to faulty interpretation. Therefore, this study is unique in recognizing endogeneity and has gone deeper in identifying the default drivers in the Malaysian housing market not addressed by earlier papers.

We show that in sorting cross-sectional data, the endogeneity of a variable may be successfully detected by graphically examining the cumulative sum of the recursive residuals. Moreover, the sign of the bias implied by the endogeneity may be deducible through such graphs. In general, instrumental variables are needed to implement the graphical test. However, when a continuous or ordered (e.g. years of schooling) variable is suspected to be endogenous, a graphical test for misspecification due to endogeneity (e.g. self-selection) can be obtained without instrumental variables.

This study aims to investigate the factors that drive housing loan default in India based on unique micro-level data drawn from a public sector bank's credit files with a national presence in India. The authors address endogeneity in the loan to value ratio (LTV) while deciphering the drivers of default.

The study uses a probit regression approach to analyze the relationship between the probability of default and the explanatory variables. The authors introduce two instrumental variables to address the issue of endogeneity. The authors also add state-level demographic and several other control variables, including an indicator variable that captures the recent regulatory change. The authors’ analysis is based on 102,327 housing loans originated by the bank between January 2001 and December 2017.

The authors find that addressing the endogeneity issue is essential to specify default drivers, especially LTV, correctly. The nature of employment, gender, socio-religious category and age have a distinct bearing on housing loan defaults. Apart from the LTV ratio, the other key determinants of default are the interest rate, frequency of repayment, prepayment options and the loan period. The findings suggest that the population classification of branch location plays a significant role in loan default. The authors find that an increase in per capita income and an increase in the number of employed people in the state, which reflects borrowers' ability to pay by borrowers, reduce the probability of default. The change in the regulatory classification of loan assets by the Reserve Bank of India did not bear the main results.

The non-availability of the house price index in analyzing the default dynamics in the Indian housing finance market for the period covered under the study has constrained our analysis. The applicability of the equity theory of default, strategic default, borrowers' characteristics and personality traits are potential research areas in the Indian housing finance market.

The study's findings are expected to provide valuable inputs to the banks and the housing finance companies to explore and formulate appropriate strategic options in lending to this sector. It has highlighted various vistas of tailor-making housing loan product offerings by the commercial banks to ensure and steady and healthy growth of their loan portfolio. It has also highlighted the regulatory and policy underpinnings to ensure the healthy growth of the Indian housing finance market.

The study provides a fresh perspective on the default drivers in the Indian housing finance market based on micro-level data. In our analysis, the authors find clear evidence of endogeneity in LTV and argue that any attempts to decipher the default drivers of housing loans without addressing the issue of endogeneity may lead to faulty interpretation. Therefore, this study is unique in recognizing endogeneity and has gone deeper in identifying the default drivers in the Indian housing market not addressed by earlier papers on the Indian housing market. The authors also control for the regulatory changes in the Indian housing finance market and include state-level control variables like per capita GDP and the number of workers per thousand to capture the borrowers' ability to pay characteristics.

Argues that ethics and values are systemic realities and can be scientifically programmed in cybernetically oriented socio‐scientific systems. The case taken is of economic general equilibrium with possibilities of multiple equilibria. The treatment of ethics and values in this sense in economic theory makes them endogenous phenomena of socio‐economic reality. This substantive idea of ethics and values as endogenous phenomena in socio‐scientific systems is termed the principle of ethical endogeneity. Its social cybernetical possibilities are developed mathematically. While the mathematical treatment uses bilinear algebra for the formulation, greater importance may be seen in the scientific essence of the principle of ethical endogeneity applicable universally. This is particularly true of systems which need to be epistemologically unified.

An important but often overlooked obstacle in multivariate discrete data models is the specification of endogenous covariates. Endogeneity can be modeled as latent or observed, representing competing hypotheses about the outcomes being considered. However, little attention has been applied to deciphering which specification is best supported by the data. This paper highlights the use of existing Bayesian model comparison techniques to investigate the proper specification for endogenous covariates and to understand the nature of endogeneity. Consideration of both observed and latent modeling approaches is emphasized in two empirical applications. The first application examines linkages for banking contagion and the second application evaluates the impact of education on socioeconomic outcomes.

Without controlling for selection bias and the potential endogeneity of the treatment by using proper methods, the estimation of treatment effect could lead to biased or incorrect conclusions. However, these issues are not addressed adequately and properly in higher education research. This study reviews the essence of self-selection bias, treatment assignment endogeneity, and treatment effect estimation. We introduce three treatment effect estimators – propensity score matching analysis, doubly robust estimation (augmented inverse probability weighted approach), and endogenous treatment estimator (control-function approach) – and examine literature that applies these methods to research in higher education. We then use the three methods in a case study that estimates the effects of transfer student pre-enrollment debt on persistence and first year grades. The final discussion provides guidelines and recommendations for causal inference research studies that use such quasi-experimental methods.