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While consumers did not previously have information on detailed housing features via traditional media, such as magazines, nowadays, due to the progress in information technology, they can access detailed information on various housing features via housing information websites. Therefore, detailed housing features may affect current rents to some extent. This paper aims to identify the effects of detailed housing features on rent and on omitted variable bias in Tokyo, Japan.

This paper applies the hedonic approach. To identify the effects of features which are not observed previously, we use a unique data set that contains various housing features and over 200,000 housing units. This data set enables to simulate the situations when the researcher cannot get some variables, and this simulation shows which variables cause omitted variable bias.

The analysis shows that housing features significantly influence housing rent. If significant housing feature variables are not included in the hedonic model, the estimated coefficients show omitted variable bias. Additionally, unit-specific features such auto-locking door can cause omitted variable bias on location-specific features such accessibility to downtown.

This paper shows empirical evidence that detailed housing features can cause omitted variable bias on other features including variables which are often used in previous searches. The result from our unique data set can be a guide for variable selection to reduce omitted variable bias.

In this chapter, we explicate two related techniques that help quantify the sensitivity of a given causal inference to potential omitted variables and/or other sources of unexplained heterogeneity. In particular, we describe the Impact Threshold of a Confounding Variable (ITCV) and the Robustness of Inference to Replacement (RIR). The ITCV describes the minimum correlation necessary between an omitted variable and the focal parameters of a study to have created a spurious or invalid statistical inference. The RIR is a technique that quantifies the percentage of observations with nonzero effects in a sample that would need to be replaced with zero effects in order to overturn a given causal inference at any desired threshold. The RIR also measures the percentage of a given parameter estimate that would need to be biased in order to overturn an inference. Each of these procedures is critical to help establish causal inference, perhaps especially for research urgently studying the COVID-19 pandemic when scholars are not afforded the luxury of extended time periods to determine precise magnitudes of relationships between variables. Over the course of this chapter, we define each technique, illustrate how they are applied in the context of seminal strategic management research, offer guidelines for interpreting corresponding results, and delineate further considerations.

We argue that resource-based view (RBV) researchers must take into account three interdependencies, (i) intrafirm resource complementarity, (ii) interfirm resource complementarity or rivalry, and (iii) compatibility or incompatibility of firm resources to broader socio-economic institutions, when attempting to empirically verify the RBV. However, these interdependencies lead to three potential causes of statistical bias, which can reduce the interpretability of such empirical examinations. First, omitted variable bias results from a researcher's inability to find and include in empirical analyses appropriate operationalizations of constructs. Second, selection bias can arise when a researcher samples only from one subset of the population, and not others. Bias in estimates can occur if a correlation between unobserved determinants of the outcome and factors affecting the selection process exist. Finally, joint dependence, where two explanatory variables are themselves mutual determinants, can lead to biased estimation.

Studies in criminal psychology are inevitably undertaken in a context of uncertainty. One class of methods addressing such uncertainties is Monte Carlo (MC) simulation. The purpose of this paper is to provide an introduction to MC simulation for representing uncertainty and focusses on likely uses in studies of criminology and psychology. In addition to describing the method and providing a step-by-step guide to implementing a MC simulation, this paper provides examples using the Fragile Families and Child Wellbeing Survey data. Results show MC simulations can be a useful technique to test biased estimators and to evaluate the effect of bias on power for statistical tests.

After describing MC simulation methods in detail, this paper provides a step-by-step guide to conducting a simulation. Then, a series of examples are provided. First, the authors present a brief example of how to generate data using MC simulation and the implications of alternative probability distribution assumptions. The second example uses actual data to evaluate the impact that omitted variable bias can have on least squares estimators. A third example evaluates the impact this form of heteroskedasticity can have on the power of statistical tests.

This study shows MC simulated variable means are very similar to the actual data, but the standard deviations are considerably less in MC simulation-generated data. Using actual data on criminal convictions and income of fathers, the authors demonstrate the impact of omitted variable bias on the standard errors of the least squares estimator. Lastly, the authors show the p-values are systematically larger and the rejection frequencies correspondingly smaller in heteroskedastic error models compared to a model with homoskedastic errors.

The aim of this paper is to provide a better understanding of what MC simulation methods are and what can be achieved with them. A key value of this paper is that the authors focus on understanding the concepts of MC simulation for researchers of statistics and psychology in particular. Furthermore, the authors provide a step-by-step description of the MC simulation approach and provide examples using real survey data on criminal convictions and economic characteristics of fathers in large US cities.

International organizations pursue multiple objectives in hiring policies including cultural diversity, reducing costs and avoiding discrimination among which there can be sharp trade‐offs. The paper has the purpose of studying how these trade‐offs are resolved in the World Bank's hiring processes.

The paper estimates that half of salary and grade differentials between men and women and staff from high‐ and low‐income countries are attributable to differences in productive characteristics. Alternative explanations for the remainder are explored, including omitted variable bias, quotas and discrimination.

The paper argues that the salary and grade differentials and differences in productive characteristics are not compelling explanations. Discrimination probably exists, though less than would be implied by a cost minimizing hiring policy.

Provides a discussion of the World Bank's hiring processes.

This article argues that existing research poorly specifies the link between planning and performance because of omitted variable bias. Researchers agree planning is a critical part of creating any new venture. Many researchers assess planning by whether a small firm has a written business plan. Unfortunately, efforts empirically to validate this relationship have been inconclusive. This article proposes that researchers should assess business plans both on the quality of the plan (and the planning process that produced it), and on the quality of the underlying business opportunity. Failure to account for both aspects of a business plan amounts to omitted variable bias, frustrating attempts to accurately estimate the true relationship.

This study investigates the asymmetric effects of exchange rate and investors' sentiments simultaneously on stock market performance in the United States context. In addition, we have also considered the potential effect of the global financial crisis of 2008 on this nexus.

We have employed the NARDL (nonlinear autoregressive distributed lag) model on monthly data ranging from January-1999 to December-2018 to investigate the asymmetric (short- and long-run) effects of exchange rate and investors' sentiments on stock market performance. We have also broken down the data into two segments, pre and post-crisis periods to capture the effect of the global financial crisis of 2008.

The findings of the study reveal that exchange rate and investors' sentiments simultaneously affect stock market performance and omitting any of these variables can produce misleading results. Results also show that the effect of sentiments is stronger than the exchange rate. There is significant evidence of asymmetric short-run and long-run effects of both explanatory variables. Moreover, we have found different outcomes for pre and post-crisis periods. Specifically, the impact of macroeconomic variables on the stock market has been substantiated in the post-crisis period.

Several studies are available which separately evidence the effects of investors' sentiments and exchange rate on performance of the stock market but they can suffer from the problem of omitted variable bias. This study is conducted to test the said effect simultaneously in a single model. Moreover, this study is considering short-run and long-run asymmetry in analyzing the effects of explanatory variables along with the inclusion of the global financial crisis of 2008.

Hedonic models are commonly used in housing markets studies to obtain quantitative measures of various implicit prices. The use of panel data in other fields of research has proved to be valuable when accounting for unobserved heterogeneity. Given that houses are extremely heterogeneous, and given that it is impossible to include all relevant attributes in hedonic models, removing unobserved heterogeneity by basic panel data models sounds appealing. This paper seeks to compare results between models that use pooled cross section data and panel data. The main research question is whether the pooled model gives unbiased estimates on some basic implicit prices.

The paper applies the hedonic methodology. It uses regression analysis and estimate basic and parsimonious models that use either pooled time series and cross section data or panel data. The empirical results when using the two different approaches are compared.

The paper illustrates that the results from the pooled timeseries and cross section model could be biased for some basic implicit prices. With some nuances, it is illustrated that in specific situations the use of a basic panel data estimator could be a simple solution to the problem of misspecification due to omitted, time‐invariant explanatory variables.

Most of the included variables do not change over time, however. In these cases potential bias using a basic fixed effects approach could not be checked for. It is also problematic that the variation in some of the time‐varying variables is not reliable and small. Finally, there could be a problem with sample selection bias. This may limit the usefulness of using panel data in disaggregated hedonic house price studies.

Hedonic house price models are frequently used in housing market research. It is therefore important to study in various ways whether the traditional approaches provide unbiased results. In this paper models that use panel data are compared to models that use more traditional time series and cross section data. To the author's knowledge, this approach has not been followed before.

There is evidence that corporate social responsibility (CSR) practices predict higher firm value, but little evidence on which specific aspects of CSR drive this relationship. The purpose of this paper is to study this question in a sample drawn from 35 countries over 2003-2016.

The authors employ a research design that analyzes observational data with panel data methods including ordinary least squares, firm-random effects, and firm-fixed effects.

The authors find in a sample drawn from 35 countries over 2003-2016 an economically significant relationship between an overall CSR measure and firm value. The overall CSR score builds on data from Asset4 and is comprised of three indices for environmental, social, and corporate governance aspects of CSR. The authors find that the social index consistently predicts higher market value. The authors also show that the use of particular elements of CSR can lead to substantial omitted variables bias when predicting firm value. The results also suggest a similar bias in studies that focus on a single index, which captures a specific aspect of CSR, but omits the remaining aspects.

The study is subject to limitations common to observational studies.

The authors find robust evidence that CSR predicts market value using a country-benchmarked overall CSR index. The power to predict firm value comes solely from the social dimension of this measure, which captures firm-level practices related to treatment of employees and stakeholder relations including those with customers and the broader community. Three elements drive the social index: customer/product responsibility, human rights, and employment quality. None of the remaining 12 elements significantly predicts firm vale in an empirical setting with firm-FE and extensive covariates. The authors also show that omitted aspects of CSR can easily lead to an omitted variable bias and that the magnitude of this bias is potentially greater with an OLS specification.

Among the many dimensions of CSR, only a subset drives firm value. Policies that target to improve the CSR performance of firms adopt a broader definition of CSR.

The authors provide first-hand evidence on which specific aspects of CSR drive firm market value.

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.