Academic literature on the topic 'Semiparametric models; endogeneity; empirical demand analysis'

In this paper we are concerned with analyzing the behavior of a semiparametric estimator that corrects for endogeneity in a nonparametric regression by assuming mean independence of residuals from instruments only. Because it is common in many applications, we focus on the case where endogenous regressors and additional instruments are jointly normal, conditional on exogenous regressors. This leads to a severely ill-posed inverse problem. In this setup, we show first how to test for conditional normality. More importantly, we then establish how to exploit this knowledge when constructing an estimator, and we derive the large sample behavior of such an estimator. In addition, in a Monte Carlo experiment we analyze its finite sample behavior. Our application comes from consumer demand. We obtain new and interesting findings that highlight both the advantages and the difficulties of an approach that leads to ill-posed inverse problems. Finally, we discuss the somewhat problematic relationship between endogenous nonparametric regression models and the recently emphasized issue of unobserved heterogeneity in structural models.

Triangular systems with nonadditively separable unobserved heterogeneity provide a theoretically appealing framework for the modeling of complex structural relationships. However, they are not commonly used in practice due to the need for exogenous variables with large support for identification, the curse of dimensionality in estimation, and the lack of inferential tools. This paper introduces two classes of semiparametric nonseparable triangular models that address these limitations. They are based on distribution and quantile regression modeling of the reduced form conditional distributions of the endogenous variables. We show that average, distribution, and quantile structural functions are identified in these systems through a control function approach that does not require a large support condition. We propose a computationally attractive three‐stage procedure to estimate the structural functions where the first two stages consist of quantile or distribution regressions. We provide asymptotic theory and uniform inference methods for each stage. In particular, we derive functional central limit theorems and bootstrap functional central limit theorems for the distribution regression estimators of the structural functions. These results establish the validity of the bootstrap for three‐stage estimators of structural functions, and lead to simple inference algorithms. We illustrate the implementation and applicability of all our methods with numerical simulations and an empirical application to demand analysis.

The main aim of this empirical work is to investigate cost management efficiency determinants of oil and gas companies in Eurasian Economic Union. The data was carefully gathered with updated financial data of 24,813 firm-year observations for the following period 2012-2020. Two main models were developed: with social responsibility and without social responsibility. In order to conduct panel data regression analysis, we employ two-step system GMM. The Durbin, Wu-Hausman test was used to find endogeneity, before we use the system GMM. Findings reveal that capital structure, taxes, and the oil demand crisis of 2014-2015 to be the most dominant determinants of cost management efficiency in the studied sample of oil and gas companies. Results suggest that increased taxes boost the cost management efficiency of oil and gas firms. Findings of the present study offer many insights and policy implications to help investors, managers, and policy makers. The contribution to the literature is twofold.

Purpose The issue of black economy has long been debated in India and it has been one of the key targets of policy action from last four decades. The debate is further fueled by demonetization of higher currency notes in the country. In this context, the purpose of this paper is to estimate the size of black economy in India for the period 1970–2017. Design/methodology/approach A currency demand approach is adopted for this purpose. The test of structure break indicates for a break in the system; therefore, the authors employ Johansen et al. (2000) cointegration test. For estimating the empirical model, the authors utilize fully modified ordinary least squares in a cointegration framework for taking care the endogeneity problem. Findings The estimates indicate that the Indian economy has a sizable black economy. In early 1970s, when the tax rate in India was significantly higher, the estimated black economy was above 30 percent of the official GDP. A variety of economic reforms including taxation, regulation and industrial licensing have drastically reduced the size to below 15 percent of official GDP in the last two decades. In the last estimated year (2017), the black economy was 23,849bn Indian rupees at current market price (around $400bn), which was 14 percent of the official GDP. Practical implications On the basis of the findings, the authors suggest some important fiscal, administrative and regulatory reforms to curb the generation of black economy in India. Originality/value The structural breaks can induce stochastic behavior similar to an integrated process, which makes it difficult to differentiate between the lack of cointegration and a structural shift. Thus, in the present study, the authors attempt to address this issue by incorporating the issue of structural break in the analysis. Furthermore, India is a cash-based economy; therefore, it is likely that currency-based models are more suitable. The application of advanced time-series techniques is likely to yield better and robust results.

As a result of the role played by migrants in supporting host economies, the interest in understanding the impact of migration is growing. However, the literature remains silent on the channels by which migration affects tourism. The present article aims to isolate the effect of migrant networks on tourism by exploring the role of information, travel costs, and demand for visiting friends and relatives. To this end, a theoretical framework that rests upon a structural gravity model is developed. The model allows not only a better understanding of the relationship between tourism and migration but also to overcome several empirical biases like the omission of multilateral resistance in tourism flows or controlling for endogeneity. The empirical analysis considered a sample of 34 OECD countries as destination/home and 157 origin/countries-of-birth for tourist arrivals/migration stock. A positive and robust effect of migration on inbound tourism is estimated and the three channels proposed to drive this nexus become relevant.

Public health education is gaining significance globally, and it is important for managing health risks. This study empirically analyzed the effect of public health education on people's demand for commercial health insurance. And we used the fixed effects and the mediating effect models, and instrumental variables regression in our research based on panel data of 31 provinces (including municipalities and autonomous regions) in China from year 2009 to 2019. The findings show that public health education significantly increases people's demand for commercial health insurance, and this effect remains significant when considering endogeneity and robustness. We further analyzed and found that the increased demand for commercial health insurance is caused by health literacy, health risk perceptions and health risk attitudes. Through heterogeneity analysis, we found that there were significant differences in the effects of public health education in regions with different demographic and socioeconomic characteristics. We found that the effect of health education on promoting people's demand for commercial health insurance is more obvious in regions with high levels of urbanization, proportion of men, education, economic development, medical resources, and social medical insurance coverage. Governments are supposed to take further measures to enhance the effectiveness of public health education, develop high-quality commercial health insurance, and continuously improve health risk coverage.

During the past few decades, nonparametric models have been extensively applied to empirical studies in various fields of economics due to its flexibility for depicting any type of relationship among key economic variables. However, one of the most well-known shortfalls of the model is the curse of dimensionality. It can be conveniently overcome with semiparametric modelling such as partially linear (PL) models and/or single-index (SI) models. Nonetheless, the practicality of these models in the empirical studies has been hampered by the lack of appropriate estimation procedures and a method to address endogeneity. Hence the ultimate goal of this thesis is to establish a novel econometric method for estimating semiparametrics, specifically a PL model and an extended generalised partially linear single-index (EGPLSI) model, with the presence of endogeneity. Furthermore, semiparametric analysis is an important tool for analysing empirical Engel curves, which often involve endogeneity in total expenditure. We show that, our newly developed estimation procedures and methods are able to address the endogeneity problem in the semiparametric analysis of empirical Engel curves. These goals can be broken down into a few research objectives. (1) Firstly, this thesis aims to construct a comprehensive and systematic treatment of endogeneity in semiparametrics, given the complexity of the models containing both parametric and nonparametric components. (2) Secondly, it aims to develop novel estimation procedures and methods to address endogeneity in a PL model and an EGPLSI model. (3) Lastly, it aims to analyse the empirical demand function semiparametrically by applying the estimation procedures and methods in this thesis.
Thesis (Ph.D.) -- University of Adelaide, School of Economics, 2013