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This study aims to examine the association between women on board and business performance. It also aims to investigate the impact of corporate social responsibility (CSR) and female directors on stock prices, including the function of female directors in moderating the CSR–market performance link that ultimately provides valuable insights into the impact of gender diversity on corporate boards.

Data from US publicly listed firms between 2000 and 2018 were collected and analysed using OLS regression, median regression, M-estimator regression and MM-estimator regression at 70% and 95% efficiency. In this study, firm market value was measured through Tobin’s Q, board diversity with ISS database and CSR strength and concern with the KLD database.

The results indicated that CSR positively impacts market performance by 3.1%, female board representation positively influences market performance by 4.8% and female board members strengthen the CSR–market performance relationship by 1.0% while playing a moderating role. Overall, these studies demonstrated the significance of female boards of directors for enhancing market performance.

This study used the data of US-listed firms from 2000 to 2018. The results have contributed to the ongoing discussion about the importance of gender diversity in boards and its influence on firm success. Further research works are suggested to expand the analysis by including other countries or considering additional factors that may influence the association between CSR, board representation of women and market share.

This study is essential for investors, legislators and CSR institutions in developed countries. The favourable impact of female board presence on market performance and the enhancement of the CSR–market performance relationship highlight the necessity of encouraging gender diversity on boards of directors and CSR activities.

This study emphasises the significance of gender balance on corporate boards in solving important social challenges including climate change, resource scarcity and gender equality. Companies can actively assist in addressing global issues and improving the well-being of stakeholders by promoting gender-diverse boards and encouraging CSR efforts.

To the best of the authors’ knowledge, this study is the first study demonstrating that gender diversity on corporate boards moderates the significant association between CSR performance and profitability in the USA. It has contributed to the expanding body of information regarding the moderating influence of female directors on firm value and stronger evidence for female directors in the governance of businesses.

In this paper, we follow the same logic as in Hausman (1978) to create a testing procedure that checks for the presence of outliers by comparing a regression estimator that is robust to outliers (S-estimator), with another that is more efficient but affected by them. Some simulations are presented to illustrate the good behavior of the test for both its size and its power.

The purpose of this paper is to demonstrate the application of robust techniques to the estimation of hedonic house price indexes.

The authors use simulation analysis to compare an index estimated using ordinary least squares (OLS) with several indexes estimated using robust techniques. The analysis uses sales transactions data from a US city. The authors then explore how robust methods can correct for omitted variables under some circumstances and how they affect the revision problem that occurs when longitudinal hedonic indexes are updated.

Robust methods can resolve missing variable problems in some circumstances and also can substantially reduce the revision problem in longitudinal hedonic indexes.

Robust techniques may be preferable to OLS when constructing longitudinal hedonic indexes.

This is the first paper to undertake a systematic analysis of the applicability of robust techniques in constructing hedonic house price indexes.

Maize production in China is exposed to pronounced yield risks, in particular weather risk, which is one of the most important and least controllable sources of risk in agriculture. The purpose of this paper is to analyze the extent to which weather index-based insurance can contribute to reducing the revenue risk in maize production caused by yield variations. An average farm producing maize is analyzed for each of eight Chinese provinces, six of which are part of the Northern Plains of China.

Data are based on the Statistical Yearbook of China and the Chinese Meteorological Administration. The used method of insurance pricing is burn analysis. Hedging effectiveness of precipitation index-based insurance is measured by the relative reduction of the standard deviation (SD) and the Value at Risk of maize revenues.

Results reveal that precipitation index-based insurance can cause a reduction of up to 15.2 percent of the SD and 38.7 percent of the Value at Risk with a 90 percent confidence level of maize revenues in the study area. However, there are big differences in the hedging efficiencies of precipitation index-based insurance measured at different weather stations in the various provinces. Therefore, it is recommended for insurance providers to analyze the hedging effectiveness of weather index-based insurance with regard to the geographical location of their reference weather station if they would like to offer weather index-based insurance products.

The absence of individual, long-term yield data in the study area prevents the evaluation of risk on individual farms. Thus, the hedging effectiveness can only be analyzed on an aggregated level of yield data and can rather be modeled for an average farm of a particular province.

To the author's knowledge, this paper is the first that investigates the hedging effectiveness of precipitation index-based insurance designed for reducing revenue risk of maize production in eight Chinese provinces.

The purpose of this paper is to analyze the effects of data aggregation and farm‐level crop acreage on the level of natural hedge, i.e. the level of price‐yield correlations, which is an important issue in risk modeling and management.

Swiss FADN data for five crops covering the period 2002‐2009 are used to estimate price‐yield correlations at the farm‐ as well as on an aggregated level. Tobit regressions are used to estimate empirical relationships between the level of natural hedge and the underlying crop acreage.

Price‐yield correlations differ significantly between farm‐ and aggregated‐level. More specifically, the natural hedge observed at the farm‐level is much smaller, i.e. correlations are closer to zero. Taking correlations from aggregated levels thus leads to an underestimation of farm‐level revenue variability. Furthermore, it is found that larger farms have a stronger natural hedge. For instance, a 1 percent increase in area under maize and intensive barley leads to a change in the correlation by −0.02 and −0.08, respectively.

The natural hedge is often approximated with correlations observed at more aggregated levels, e.g. the county level. The results show that this implies errors in risk assessment and modeling as well as insurance applications. Thus, farm‐level estimates should be used. The here presented relationship between price‐yield correlations and farm‐level crop acreage can be used to derive better information on levels of the natural hedge.

Even though the effects of data aggregation on price‐yield correlations have been discussed in earlier research, this paper is the first to also account for on‐farm effects of underlying crop acreage on levels of natural hedge. It is found that this simple relationship can be useful in risk management and modeling applications.

There is a growing consensus that entrepreneurial activity is essentially a collective family endeavour, with some configuration of family involvement in business (FIB) working better than others. This paper aims to examine the effects of FIB on strategy and financial performance (FP), drawing from the institutional theory for the Indian family businesses.

The sample comprises of 105 pharmaceutical companies listed on the Bombay Stock Exchange for FY2013–2017. A two-way random effects panel model was invoked to examine the relationship between FIB and strategy, as well as the intermediating effect that strategy has on the FIB-FP link.

On average, the family has a high ownership concentration, with the founders predominantly holding the chief executive officer (CEO) and chair positions. The econometric results highlight that the founder’s descendants adopt a conservative strategy. A significant positive moderating effect of strategy on FIB-FP link was observed for the descendants as the largest owners, CEO and board chair. The presence of a professional CEO and independent chair, however, leads to an intervening adverse impact on FP. The ownership-management-governance configurations highlight that some combinations of family and non-FIB leads to better performance than others.

The study provides a plausible explanation for the conflicting evidence on the direct FIB-FP relationship through the strategy intermediation. The institutional perspective emphasizing the identity and role family members play in terms of strategy provides an unconventional epistemological underpinning to the present research.

This chapter shows how a weighted average of a forward and reverse Jackknife IV estimator (JIVE) yields estimators that are robust against heteroscedasticity and many instruments. These estimators, called HFUL (Heteroscedasticity robust Fuller) and HLIM (Heteroskedasticity robust limited information maximum likelihood (LIML)) were introduced by Hausman, Newey, Woutersen, Chao, and Swanson (2012), but without derivation. Combining consistent estimators is a theme that is associated with Jerry Hausman and, therefore, we present this derivation in this volume. Additionally, and in order to further understand and interpret HFUL and HLIM in the context of jackknife type variance ratio estimators, we show that a new variant of HLIM, under specific grouped data settings with dummy instruments, simplifies to the Bekker and van der Ploeg (2005) MM (method of moments) estimator.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.

The purpose of this paper is to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.

The authors apply six estimation methods (linear least squares, robust regression, mixed-effects regression, random forests, gradient boosting and neural networks) and two updating methods (moving and extending windows). They use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017.

The gradient boosting method yields the greatest accuracy, while the robust method provides the least volatile predictions. There is a clear trade-off across methods depending on whether the goal is to improve accuracy or avoid volatility. The choice between moving and extending windows has only a modest effect on the results.

This paper compares a range of linear and machine learning techniques in the context of moving or extending window scenarios that are used in practice but which have not been considered in prior research. The techniques include robust regression, which has not previously been used in this context. The data updating allows for analysis of the volatility in addition to the accuracy of predictions. The results should prove useful in improving hedonic models used by property tax assessors, mortgage underwriters, valuation firms and regulatory authorities.