Search results

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Landscape view is a crucial factor in house-buying decisions. Landscape views provide an amenity to residents, and this can influence the house or apartment owners in their residence decisions. Yet, the relative value of different types of view potentially differs. Additionally, the value of each type of view may differ depending on an apartment’s elevation above the ground level. In this study, the authors aim to estimate the value of landscape views on apartment prices in major urban areas in Indonesia.

This paper evaluates to what extent various landscape views including mountain, sea, river, lake, street, urban village, garden and sports center views affect apartment prices in major urban areas in Indonesia. Two hedonic regression approaches are used: ordinary least squares and semiparametric regression. The latter is used to accommodate a possible non-linearity in the relationship between price and apartment characteristics. The model also incorporates housing and locational characteristics as control variables.

Using online apartment market data, the estimates in this paper show some degree of heterogeneity in the value of various views to the extent of providing negative externalities. Mountain, street and sports center views are associated with higher apartment prices. Sea, lake and garden views are statistically insignificant in explaining the prices. In contrast, the unappealing nature of the rivers and their surrounding creates a negative impact on prices. The estimates also suggest that an apartment’s floor height plays a significant role in the valuation of views.

There is little research on landscape view effects on apartment prices, especially in Indonesia. In addition, the relationship between the value of views and height preferences has seldom been analyzed. This paper provides the valuation of an extensive list of landscape views in urban areas in Indonesia. The estimation results also suggest that the value of views may differ depending on the floor on which an apartment lies.

The authors propose a semiparametric approach for testing independence between two infinite-order cointegrated vector autoregressive series (IVAR(∞)). The procedures considered can be viewed as extensions of classical methods proposed by Haugh (1976, JASA) and Hong (1996b, Biometrika) for testing independence between stationary univariate time series. The tests are based on the residuals of long autoregressions, hence allowing for computational simplicity, weak assumptions on the form of the underlying process, and a direct interpretation of the results in terms of innovations (or shocks). The test statistics are standardized versions of the sum of weighted squares of residual cross-correlation matrices. The weights depend on a kernel function and a truncation parameter. Multivariate portmanteau statistics can be viewed as a special case of our procedure based on the truncated uniform kernel. The asymptotic distributions of the test statistics under the null hypothesis are derived, and consistency is established against fixed alternatives of serial cross-correlation of unknown form. A simulation study is presented which indicates that the proposed tests have good size and power properties in finite samples.

This study estimates the income elasticities of calorie, macronutrients (carbohydrate, protein and fat) and key micronutrients including cholesterol, vitamin A, vitamin C, sodium, potassium, calcium, iron, zinc and insoluble fiber separately for urban and rural adults aged 18–60, using China Health and Nutrition Survey data set from 2004 to 2011.

A semiparametric model, a two-way fixed-effects model and a quantile regression approach are employed to estimate nutrient–income elasticities.

The income elasticities of calorie, protein, fat, cholesterol and calcium are in the range of 0.059–0.076, 0.059–0.076, 0.090–0.112, 0.134–0.230, 0.183–0.344 and 0.058–0.105, respectively. The income elasticity of each of the other nutrients is less than 0.1. The income elasticities of calorie and the majority of nutrients included are larger for rural residents than for urban residents and for low-income groups than for medium- and high-income groups. Overall, in spite of having a relatively small impact, income growth is shown to still have an impact on improving the nutritional status of Chinese adults.

This study estimates nutrient–income elasticities separately for urban and rural adults, expanding the scope of the study regarding the impact of income on the nutritional status in China. Moreover, this study uses a pooled sample generated from the personal food consumption records covering foods consumed at home and away from home during 2004–2011, which is thus likely to more comprehensively reveal the causal relationship between income growth and changes in the nutritional status in China.

We employ various local generalizations of the Solow growth model that model parameter heterogeneity using human development at the beginning of the period with adult literacy rates and life expectancy at birth as a proxy. The model takes the form of a semiparametric varying coefficient model along the lines of Hastie and Tibshirani (1992). The empirical results show substantial parameter heterogeneity in the cross-country growth process, a finding that is consistent with the presence of multiple steady-state equilibria and the emergence of convergence clubs.

The size and number of employment subcenters have increased in large metropolitan areas as the spatial distribution of jobs has become increasingly decentralized. Although employment decentralization is not a new phenomenon, only recently have concentrations of employment outside the central city begun to rival the traditional central business district (CBD) in size and scope. Because of this change, neither theoretical nor empirical models in urban economics now rely solely on the traditional monocentric city model of Muth (1969) and Mills (1972). Instead, recent research incorporates some version of a polycentric model, a trend that Anas et al. (1998) document in their excellent review article.

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.

This research paper aims to analyze the stock exchanges of developed, emerging and developing countries to investigate the volatility in stock markets and to evaluate the rate of mean reversion.

The stock exchanges included in the research are NASDAQ, Tokyo stock exchange, Shanghai stock exchange, Bombay stock exchange, Karachi stock exchange and Jakarta stock exchange. Secondary daily data from Bloomberg are used to conduct the research for the period from January 2011 to December 2018. Generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model was applied to examine volatility and the half-life formula was used to calculate mean reversion in days.

The research concluded that all the stock exchanges included in the research satisfy the assumptions of mean reversion. Developing countries have the lowest volatility while emerging countries have the highest volatility which means that the rate of mean reversion is fastest in developing countries and slowest in emerging countries.

Future studies can determine the reasons for fastest rate of mean reversion in developing countries and slowest rate of mean reversion in emerging countries.

Developing countries show the lowest mean reversion in days while the emerging countries show the highest mean reversion in days indicating that developing countries take less time to revert to their mean position.

The majority of previous studies on univariate volatility models are mostly on applications of the models. Only a few researchers have taken the robustness of the models into account when applying them in emerging countries and not in developed, developing and emerging countries in one place. This makes the current study unique and more rigorous.

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.