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We develop and estimate an empirical collective model with endogenous marriage formation, participation, and family labor supply. Intra-household transfers arise endogenously as the transfers that clear the marriage market. The intra-household allocation can be recovered from observations on marriage decisions. Introducing the marriage market in the collective model allows us to independently estimate transfers from labor supplies and from marriage decisions. We estimate a semiparametric version of our model using 1980, 1990, and 2000 US Census data. Estimates of the model using marriage data are much more consistent with the theoretical predictions than estimates derived from labor supply.

The purpose of this paper is to investigate the profitability of European Union (EU) firms with the aim of confirming the mean‐reverting pattern documented by earlier research in the USA. In addition, the paper classifies firms by industry sectors across countries to investigate potential differences.

The paper follows closely a model where the forecasting of profitability is done through year‐by‐year regressions. This approach allows the use of large samples and the year‐by‐year variation in the slopes. Both a linear and a nonlinear partial adjustment models are used for forecasting profitability.

Findings show that the profitability does follow a mean‐reverting process and that profitability forecasting can be improved substantially by exploiting the mean‐reverting feature. Further analysis shows that mean reversion does not play an important role in EU countries as in the USA and there is no evidence of nonlinearity in mean reversion. It was also found that mean‐reverting speed differ across industries, with utilities, financial and manufacturing among the lowest.

The sample companies are not originated from a single economy, but from 15 different countries with different macro‐economic conditions that might influence their profitability.

Studying the European market, where the institutional and financial structure of firms are different from the USA allows us to observe whether the US results are sample specific or can be generalized and applied elsewhere. The difference observed in these sample results is probably due to the fact that the US economy is more competitive than that of EU.

The purpose of this article is to revisit the mean reversion in profitability and earnings among Indian-listed firms, based on the idea that changes in profitability and earnings are somewhat predictable.

The study used a sample of 445 Bombay Stock Exchange (BSE)-listed companies and 309 companies from the manufacturing sector in India for the period from 2007 to 2020. The study employed cross-sectional regressions. Both linear and non-linear Partial Adjustment Models (PAM) were used to forecast profitability and earnings.

The study revealed that profitability and earnings mean revert for both the BSE-listed companies and the manufacturing sector companies from 2007 to 2012. However, for the years from 2013 to 2020, it was found that there is no significant evidence of mean reversion in both the BSE-listed companies or the manufacturing sector companies.

The findings have larger implications for security analysts who forecast future stabilisation or recovery of historically high or low growth rates. Investors and analysts would benefit from having a better understanding of how competitive attacks affect profitability as well as how the overall economic growth of a country affects earnings and valuations.

Most of the empirical research in India has focused on mean reversion in stock prices or stock returns. The present study looked at the mean reversion of profitability and earnings in Indian firms.

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent developments in nonparametric econometrics. Therefore, we choose to limit our focus on the following areas. In Section 2, we review the recent developments of nonparametric estimation and testing of regression functions with mixed discrete and continuous covariates. We discuss nonparametric estimation and testing of econometric models for nonstationary data in Section 3. Section 4 is devoted to surveying the literature of nonparametric instrumental variable (IV) models. We review nonparametric estimation of quantile regression models in Section 5. In Sections 2–5, we also point out some open research problems, which might be useful for graduate students to review the important research papers in this field and to search for their own research interests, particularly dissertation topics for doctoral students. Finally, in Section 6 we highlight some important research areas that are not covered in this paper due to space limitation. We plan to write a separate survey paper to discuss some of the omitted topics.

Hausman (1978) represented a tectonic shift in inference related to the specification of econometric models. The seminal insight that one could compare two models which were both consistent under the null spawned a test which was both simple and powerful. The so-called ‘Hausman test’ has been applied and extended theoretically in a variety of econometric domains. This paper discusses the basic Hausman test and its development within econometric panel data settings since its publication. We focus on the construction of the Hausman test in a variety of panel data settings, and in particular, the recent adaptation of the Hausman test to semiparametric and nonparametric panel data models. We present simulation experiments which show the value of the Hausman test in a nonparametric setting, focusing primarily on the consequences of parametric model misspecification for the Hausman test procedure. A formal application of the Hausman test is also given focusing on testing between fixed and random effects within a panel data model of gasoline demand.

This paper examines a nonparametric CUSUM-type test for common trends in large panel data sets with individual fixed effects. We consider, as in Zhang, Su, and Phillips (2012), a partial linear regression model with unknown functional form for the trend component, although our test does not involve local smoothings. This conveniently forgoes the need to choose a bandwidth parameter, which due to a lack of a clear and sensible information criteria is difficult for testing purposes. We are able to do so after making use that the number of individuals increases with no limit. After removing the parametric component of the model, when the errors are homoscedastic, our test statistic converges to a Gaussian process whose critical values are easily tabulated. We also examine the consequences of having heteroscedasticity as well as discussing the problem of how to compute valid critical values due to the very complicated covariance structure of the limiting process. Finally, we present a small Monte Carlo experiment to shed some light on the finite sample performance of the test.

The purpose of this study is to estimate the relative contributions of individual marketing mix variables to sales as well as short‐term and long‐term effects of advertising in India.

Time‐series data on sales and marketing mix variables have been collected for two brands. Two double‐log regression modes have been fitted on data to estimate the relative contribution of each effort as well as to isolate the amount of sales due to advertising only. In addition, a log‐linear partial‐adjustment model has been fitted on adjusted sales and advertising data to estimate both short‐term and long‐term effects of advertising.

Results reveal that all the marketing mix variables have significant relative contributions to sales in both the cases. It is also found that advertising does have significant short‐term and long‐term effects on adjusted sales for both the brands.

Findings provide a deep insight in dynamic perspective of advertising that make them eminently suitable in the process of allocation of budget to achieve both the short‐term and long‐term goals of advertising.

This research made a notable contribution to the literature due to lack of quantitative modeling works on marketing data reported in the field of advertising in India.

The purpose of this paper is to study the pricing efficiency of convertible bonds and arbitrage opportunities between the convertible bonds and the underlying stocks thus improve market efficiency.

Using nonparametric fixed effect panel data model, the authors build pricing model of convertible bonds and obtain fitted value for them. Then the authors constructs simultaneous confidence band for the smooth function to identify mispricing and study the pricing efficiency and arbitrage opportunities of convertible bonds.

Result shows, convertible bonds’ prices largely depend on stock prices. Pricing efficiency does not improve during the past few years as there are quite a few trading opportunities. Arbitrage opportunities increase as the stock prices approach it maxima, and selling opportunities for convertible bonds surpass buying opportunities which indicates that investors use market neutral strategies to arbitrage. Pricing efficiencies varies a lot and it is affected by the features of the stocks and convertible bonds. Index stocks eligible for margin trading with high liquidity enjoy higher pricing efficiency.

The study does not take into account trading cost and risk management measures.

Arbitrage between the underlying and the convertible bonds is profitable and contributes to pricing efficiency therefore should be encouraged. The regulator should pay attention to the extreme mispricing of the underlying and convertible bonds which cannot be corrected by the market as there might be manipulation.

Since traditional pricing methods are based on the framework of non-arbitrage equilibrium with the assumption of balanced and perfect market, there are many restrictions in the pricing process and the practical utility is somewhat limited, and the impractical assumptions lead to model risk. This study uses nonparametric regression to study the pricing of convertible bonds thus circumvents the problem of model risk. Simultaneous confidence band for smooth function identifies mispricing and explicitly reflects the variation of pricing efficiency as well as signalizes trading opportunities. Application of nonparametric regression and simultaneous confidence band in derivative pricing is advantageous in accuracy and simplicity.

Joon Y. Park is one of the pioneers in developing nonlinear cointegrating regression. Since his initial work with Phillips (Park & Phillips, 2001) in the area, the past two decades have witnessed a surge of interest in modeling nonlinear nonstationarity in macroeconomic and financial time series, including parametric, nonparametric and semiparametric specifications of such models. These developments have provided a framework of econometric estimation and inference for a wide class of nonlinear, nonstationary relationships. In honor of Joon Y. Park, this chapter contributes to this area by exploring nonparametric estimation of functional-coefficient cointegrating regression models where the structural equation errors are serially dependent and the regressor is endogenous. The self-normalized local kernel and local linear estimators are shown to be asymptotic normal and to be pivotal upon an estimation of co-variances. Our new results improve those of Cai et al. (2009) and open up inference by conventional nonparametric method to a wide class of potentially nonlinear cointegrated relations.

This is a survey paper of the recent literature on the application of semiparametric–econometric advances to testing for functional form of the environmental Kuznets curve (EKC). The EKC postulates that there is an inverted U-shaped relationship between economic growth (typically measured by income) and pollution; that is, as economic growth expands, pollution increases up to a maximum and then starts declining after a threshold level of income. This hypothesized relationship is simple to visualize but has eluded many empirical investigations. A typical application of the EKC uses panel data models, which allows for heterogeneity, serial correlation, heteroskedasticity, data pooling, and smooth coefficients. This vast literature is reviewed in the context of semiparametric model specification tests. Additionally, recent developments in semiparametric econometrics, such as Bayesian methods, generalized time-varying coefficient models, and nonstationary panels are discussed as fruitful areas of future research. The cited literature is fairly complete and should prove useful to applied researchers at large.