Search results
1 – 10 of 699Badi H. Baltagi, Georges Bresson and Jean-Michel Etienne
This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other…
Abstract
This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.
Details
Keywords
Wai Weng Yap, Tamat Sarmidi, Abu Hassan Shaari and Fathin Faizah Said
The purpose of this paper is to investigate the nonlinear relationship between shadow economy and income inequality and determine whether the size of shadow economy can influence…
Abstract
Purpose
The purpose of this paper is to investigate the nonlinear relationship between shadow economy and income inequality and determine whether the size of shadow economy can influence the level of income inequality.
Design/methodology/approach
Both parametric (panel OLS) and nonparametric/semiparametric regression suggested by Robinson (1988) will be used to capture the dynamic nonlinear relationship between these variables using unbalanced panel data of 154 countries from 2000 to 2007. Additionally, the relationship between income inequality and shadow economy on both developed and developing countries will be analyzed and compared.
Findings
First, semiparametric analysis and nonparametric analysis are significantly different than parametric analysis and better in nonlinear analysis between income inequality and shadow economy. Second, income inequality and shadow economy resemble an inverted-N relationship. Third, the relationship between income inequality and shadow economy is different in developed countries (OECD countries) and developing countries, where OECD countries have similar inverted-N relationship as before. However, for developing countries, income inequality and shadow economy show an inverted-U relationship, similar to the original Kuznets hypothesis.
Practical implications
This study suggests that there is a possible trade-off between income inequality and shadow economy and helps policy makers in solving both problems effectively.
Originality/value
Despite the growing importance of income inequality and shadow economy, literature linking the two variables is scarce. To the best of the authors’ knowledge, there is no literature that nonlinearly links these two variables. Furthermore, the dynamics of the relationship between these two variables in developed countries and developing countries will be explored as well.
Details
Keywords
Hector O. Zapata and Krishna P. Paudel
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)…
Abstract
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.
Peter A. Jones, Vincent Reitano, J.S. Butler and Robert Greer
Public management researchers commonly model dichotomous dependent variables with parametric methods despite their relatively strong assumptions about the data generating process…
Abstract
Purpose
Public management researchers commonly model dichotomous dependent variables with parametric methods despite their relatively strong assumptions about the data generating process. Without testing for those assumptions and consideration of semiparametric alternatives, such as maximum score, estimates might be biased, or predictions might not be as accurate as possible.
Design/methodology/approach
To guide researchers, this paper provides an evaluative framework for comparing parametric estimators with semiparametric and nonparametric estimators for dichotomous dependent variables. To illustrate the framework, the article estimates the factors associated with the passage of school district bond referenda in all Texas school districts from 1998 to 2015.
Findings
Estimates show that the correct prediction of a bond passing increases from 77.2 to 78%, with maximum score estimation relative to a commonly used parametric alternative. While this is a small increase, it is meaningful in comparison to the random prediction base model.
Originality/value
Future research modeling any dichotomous dependent variable can use the framework to identify the most appropriate estimator and relevant statistical programs.
Details
Keywords
Hedibert Freitas Lopes, Matthew Taddy and Matthew Gardner
Heavy-tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with internet transaction datasets, and machine…
Abstract
Heavy-tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with internet transaction datasets, and machine learners often analyze such data without considering the biases and risks associated with the misuse of standard tools. This chapter outlines a procedure for inference about the mean of a (possibly conditional) heavy-tailed distribution that combines nonparametric analysis for the bulk of the support with Bayesian parametric modeling – motivated from extreme value theory – for the heavy tail. The procedure is fast and massively scalable. The work should find application in settings wherever correct inference is important and reward tails are heavy; we illustrate the framework in causal inference for A/B experiments involving hundreds of millions of users of eBay.com.
Ba Hung Nguyen, Nhat Bao Quyen Pham and Thi Hong Ha Do
As small and medium-size enterprises (SMEs) rely on board heterogeneity to raise capital and establish credit relationships with suppliers, it is crucial to investigate the board…
Abstract
Purpose
As small and medium-size enterprises (SMEs) rely on board heterogeneity to raise capital and establish credit relationships with suppliers, it is crucial to investigate the board heterogeneity effect on their survival. In this study, the first research objective is to provide further insights on the discriminatory power of survival approaches, specifically on semiparametric approaches in survival analysis that take into consideration both fixed and time-varying covariates. The second objective is to examine the relationship between board size and SME liquidation by using resource-based theories that focus on measuring board heterogeneity through board size.
Design/methodology/approach
This paper uses survival approaches for modelling SMEs survival by examining the survival of more than 68,000 SMEs in the UK covering the before, onset and post 2008 crisis periods and with firms’ demographic characteristics and financial indicators. Survival analysis is effective to examine multiple causes of default/failure and how do particular circumstances or characteristics increase or decrease the probability of survival. Survival analysis brings more advantages than linear-based regression approaches by effectively handling the censoring of observations.
Findings
Motivated by resource-based theories, the authors find that the likelihood of a firm being liquidated robustly increases with a reduction in its board heterogeneity measured through board size. This finding is held under non-parametric, parametric, and semiparametric approaches using survival analysis. The research shows better causal explanation and discriminatory power on using the semiparametric-based survival analysis approach considering both fixed and time-varying covariates.
Practical implications
This study demonstrates the better performance and causal explanation of the survival model using time-varying covariates compared with those using fixed covariates. In addition, the authors delve into board heterogeneity, measuring through the board size to investigate how the number of board directors affects the firm liquidation, it is also a factor worth considering when a small and medium firm is forming its board.
Originality/value
This research investigates the board heterogeneity effect on firm survival using survival analysis approaches. The authors contribute to the knowledge on board heterogeneity of SMEs. Specifically, the size of more than three directors could help reduce SMEs liquidation risk. This result gives a recommendation to firms or start-ups when forming their director board. This research also provides further insights on the applicability of survival models with unique UK SMEs data covering the before, onset and post 2008 crisis periods.
Details
Keywords
M.P. Martínez‐Ruiz, A. Mollá‐Descals, M.A. Gómez‐Borja and J.L. Rojo‐Álvarez
To analyze the impact of temporary retail price discount on a consumer goods product category using semiparametric regression and considering different promotional price discount…
Abstract
Purpose
To analyze the impact of temporary retail price discount on a consumer goods product category using semiparametric regression and considering different promotional price discount characteristics as well as brand characteristics.
Design/methodology/approach
A semiparametric regression model using Support Vector Machines, which aim to evaluate retailers' decisions about temporary price discounts, has been developed. The model is derived from the analysis of historical sales data, which provide precise evaluation of previous temporary price discounts periods. The model is also consistent with ample empirical evidence showing that historical retail sales data can be used to evaluate the impact of past promotions.
Findings
Provides an estimation of the shape of the deal effect curve, indicating which temporary price discounts are more effective to increase sales and showing the existence of different threshold and saturation levels. Confirms that promotional price discounts accelerate sales especially during week ends. Evidences that promoting high‐priced (high‐quality) brands has a stronger impact on sales of low‐priced (low‐quality) brands than the reverse and that cross‐price effects are stronger on the sales of brands with similar prices. Suggests the convenience of the use of the proposed semiparametric methodology to the study of the promotional effects considered.
Research limitations/implications
It is not possible to generalize the modelled shapes of the deal effect curves. There is no information available on feature advertising nor displays. It is important to determine the generalizability of these results to the study of additional promotional effects. It would also be interesting to assume that the retailer's deal policy is exogenous.
Originality/value
Provides a relevant tool to assess the set of price promotional periods by the grocery retailer. With a more precise and accurate knowledge about the performance of past temporary price cuts, retailers can implement more effective promotional periods.
Details
Keywords
Ming Kong, Jiti Gao and Xueyan Zhao
This chapter re-examines the determinants of health care expenditure (HCE), using a panel of 32 Organization for Economic Cooperation and Development (OECD) countries from 1990 to…
Abstract
This chapter re-examines the determinants of health care expenditure (HCE), using a panel of 32 Organization for Economic Cooperation and Development (OECD) countries from 1990 to 2012. In particular, a panel semiparametric technique (i.e., a partially linear model) is employed, with cross-sectional dependence allowed. Beside the study of coefficients, this chapter investigates the trending functions of HCE. After the common and individual trends of HCE are estimated via semiparametric methods, the authors calibrate them with polynomial specifications, leading to out-of-sample forecasting. The validities of the calibration are tested as well. Contrary to those studies that do not take into account time series properties, our finding suggests that medical care is not a luxury commodity. Other determinants, such as public financing, and the supply of doctors, are all positively related to HCE. Moreover, the calibrated trending models perform well in out-of-sample forecasting.
Details
Keywords
This study investigates the performance of Indian states based on infrastructural investment in social and economic sectors using data envelopment analysis (DEA). Most of the…
Abstract
Purpose
This study investigates the performance of Indian states based on infrastructural investment in social and economic sectors using data envelopment analysis (DEA). Most of the studies in the literature are based on how different elements of infrastructure such as transport, energy, education, healthcare system affect the economy of different countries/regions. In this study, we consider these elements under two different sub-systems, namely, social and economic infrastructure and measure the cooperative efficiency for competitive growth.
Design/methodology/approach
A four-stage DEA approach is proposed for the analysis of a sample of 28 Indian states for the years 2011, 2013 and 2015 under consideration. First stage calculates the per capita GDP contribution, while stage-2 evaluates the efficiency of investments in social infrastructure followed by the efficiency analysis in economic infrastructure in stage-3. Finally, fourth stage evaluates the co-operative efficiency for the overall performance.
Findings
The findings of three different cases based on population sizes, viz., highly populated, moderately populated and less populated regions suggest that the government can identify the top and poor performers. It also studies the variations in efficiency tally of states using Malmquist indices.
Practical implications
This kind of study will vigilant government and local authorities on the investments made in all the states for social and economic infrastructure and establish a competitive environment among state governments to compete for improved infrastructural growth.
Originality/value
This study is the first of its kind in developing countries like India, which focuses on efficiency analysis using DEA based on two sub-sectors of social–economic infrastructural investments.
Details
Keywords
Saida Mancer, Abdelhakim Necir and Souad Benchaira
The purpose of this paper is to propose a semiparametric estimator for the tail index of Pareto-type random truncated data that improves the existing ones in terms of mean square…
Abstract
Purpose
The purpose of this paper is to propose a semiparametric estimator for the tail index of Pareto-type random truncated data that improves the existing ones in terms of mean square error. Moreover, we establish its consistency and asymptotic normality.
Design/methodology/approach
To construct a root mean squared error (RMSE)-reduced estimator of the tail index, the authors used the semiparametric estimator of the underlying distribution function given by Wang (1989). This allows us to define the corresponding tail process and provide a weak approximation to this one. By means of a functional representation of the given estimator of the tail index and by using this weak approximation, the authors establish the asymptotic normality of the aforementioned RMSE-reduced estimator.
Findings
In basis on a semiparametric estimator of the underlying distribution function, the authors proposed a new estimation method to the tail index of Pareto-type distributions for randomly right-truncated data. Compared with the existing ones, this estimator behaves well both in terms of bias and RMSE. A useful weak approximation of the corresponding tail empirical process allowed us to establish both the consistency and asymptotic normality of the proposed estimator.
Originality/value
A new tail semiparametric (empirical) process for truncated data is introduced, a new estimator for the tail index of Pareto-type truncated data is introduced and asymptotic normality of the proposed estimator is established.
Details