A Smooth Coefficient Quantile Regression Approach to the Social Capital-Economic Growth Nexus

[[abstract]]This analysis assesses the role of social capital in generating heterogeneity in growth processes across U.S. counties by estimating growth regressions, using the novel semiparametric smooth coefficient quantile regression method in which parameters are unspecified functions of a measure of social capital. The results indicate substantial differences across the quantiles of economic growth in the profile shapes of the coefficient estimates over the level of social capital. Moreover, the coefficient function estimates are highly nonlinear over the level of social capital, providing evidence that the growth process that links initial income, education attainment, ethnic diversity, inequality, population density, and government activity to growth varies with social capital in a nonlinear way.[[notice]]補正完畢[[incitationindex]]SSCI[[booktype]]紙本[[booktype]]電子

A study of local linear ridge regression estimators

In the case of the random design nonparametric regression, to correct for the unbounded nite-sample variance of the local linear estimator (LLE), Seifert & Gasser (J. Amer. Statist. Assoc. 91 (1996) 267{275) apply the idea of ridge regression to the LLE, & propose the local linear ridge regression estimator (LLRRE). However, the nite sample & the asymptotic properties of the LLRRE are not discussed there. In this paper, upper bounds of the nite-sample variance & bias of the LLRRE are obtained. It is shown that if the ridge regression parameters are not properly selected, then the resulting LLRRE has some drawbacks. For example, it may have a nonzero constant asymptotic bias, may suer from boundary eects, or may be unable to share the nice asymptotic bias quality of the LLE. On the other hand, if the ridge regression parameters are properly selected, then the resulting LLRRE does not suer from the above problems, & has the same asymptotic mean-square error as the LLE. For this purpose, the ridge regression parameters are allowed to depend on the sample size, & converge to 0 as the sample size increases. In practice, to select both the bandwidth & the ridge regression parameters, the idea of cross-validation is applied. Simulation studies demonstrate that the LLRRE using the cross-validated bandwidth & ridge regression parameters could have smaller sample mean integrated square error than the LLE using the cross-validated bandwidth, in reasonable sample sizes

Determinants of Municipal-level Household Food Waste

Using time series data on municipal-level household food scrap drawn from a city-wide food scrap recycling program from 2007m1 to 2018m8, we proxy the household food wastage rate (FWR) by the edible-to-inedible food scrap ratio and then apply both linear and semiparametric varying-coefficient cointegration tests to examine the municipal-level long-run relationship between socioeconomic factors and household FWR. The linear model shows that food price, old-age population share, and average household size are positively related to FWR, whereas working-age population share is negatively related to FWR. Consistent with the environmental Kuznets curve hypothesis, the varying-coefficient model further reveals that the direct relationship between income and FWR is inverted-U shaped. Moreover, income alters the relationship between socioeconomic factors and FWR. The FWR-increasing effect of population aging (food price) likely aggravates (diminishes) with economic growth. With these in mind, for policymakers trying to reduce food waste, the steady increases in food price and old-age share pose challenges, whereas the declining trend in average household size brings relief. Given the results, one way to reduce food waste is subsidizing both the design of smart packaging and the development of semi-prepared convenience food with flexible portion sizes and portion sizes suitable for older adults.補正完畢TW

[[alternative]]利用半參數法估計技術效率隨時間改變的隨機邊際模型

[[abstract]]本研究根據Fan et al. (1996)所提出的半參數隨機邊際模型，朝向兩個方向延伸：一、從橫斷面模型推廣至縱橫資料模型。第二、參照Battese and Coelli (1992)的作法，容許技術效率隨時間改變。本文進行Monte Carlo模擬研究，發現本研究提出的估計方法，具有一致性，故適合應用於生產、成本及利潤函數的估計，進而探討相關生產效率議題。最後採用台灣86家電子業上市公司1995至2001年的資料，進行實證分析，並與參數估計法相互比較。 This article extends the semiparametric stochastic frontier model developed by Fan et al. (1996) in two ways. First, it proposes a semiparametric estimation procedure suitable for the case when panel data are available. Second, the strong assumption of time-invariant technical efficiency is relaxed such that Battese and Coelli's (1992) model can be applied empirically. Our procedure is particularly useful in the examination of technical efficiency with respect to production, cost, and profit frontiers. Monte Carlo experiments and an empirical application of the proposed procedure employing panel data on 86 Taiwanese electronic firms over the period 1995-2001 are exemplified.[[notice]]補正完畢[[journaltype]]國內[[incitationindex]]TSSC

Heterogeneity in the relationship between subjective well-being and its determinants over the life cycle: A varying-coefficient ordered probit approach

[[abstract]]We examine the evolution of the mechanism behind subjective well-being (SWB) across the lifespan using data from the 1972–2010 waves of the U.S. General Social Survey. By estimating a semiparametric varying-coefficient partially linear ordered probit model, we find that the influence of race, income, reference income, labor market status, marriage, and number of children on SWB varies greatly and nonlinearly along the life cycle. Among our results, we find that the effect of being black on the representative male's probability of being in the lowest happiness category falls from 9.399 percentage points (pps) at age 29 to 1.709 pps at age 55, turning insignificant afterwards. Being unemployed is associated with an increase in the representative male's probability of being in the lowest happiness category by 6.322 to 15.896 pps, with the largest effect occurring at age 40. The varying-coefficient model enhances our understanding of when life events are most detrimental to a person's well-being. The heterogeneity found highlights that, in order to promote well-being effectively, public policies should be differentiated across people depending on their age.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SSCI[[ispeerreviewed]]Y[[countrycodes]]NL

A Note on the Frequency Polygon Based on the Weighted Sums of Binned Data

[[abstract]]We revisit the generalized midpoint frequency polygons of Scott (1985), and the edge frequency polygons of Jones et al. (1998) and Dong and Zheng (2001). Their estimators are linear interpolants of the appropriate values above the bin centers or edges, those values being weighted averages of the heights of r, r ∈ N, neighboring histogram bins. We propose a simple kernel evaluation method to generate weights for binned values. The proposed kernel method can provide near-optimal weights in the sense ofminimizing asymptotic mean integrated square error. In addition, we prove that the discrete uniform weights minimize the variance of the generalized frequency polygon under some mild conditions. Analogous results are obtained for the generalized frequency polygon based on linearly prebinned data. Finally, we use two examples and a simulation study to compare the generalized midpoint and edge frequency polygons.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SSCI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]US

Averaged shifted chi-square test

[[abstract]]A simple procedure based on the average of shifted chi-square statistics (ASCS) is proposed to improve the classical chi-square procedure for testing whether a random sample has been drawn from a specified continuous distribution. We repeatedly partition the sample space, say, ℓ times to obtain ℓ respective chi-square statistics. The proposed test statistic is defined as the average value of the resultant ℓ shifted chi-square statistics. We prove that the ASCS is asymptotically distributed as a weighted sum of a finite number of chi-square variables by the theory of U-statistics. The proposed procedure is shown to be markedly less sensitive to the choice of the anchor position and Monte Carlo experiments demonstrate that it leads to noticeable gains in power.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[countrycodes]]GB

A study of local linear ridge regression estimators

[[abstract]]In the case of the random design nonparametric regression, to correct for the unbounded finite-sample variance of the local linear estimator (LLE), Seifert and Gasser (J. Amer. Statist. Assoc. 91 (1996) 267–275) apply the idea of ridge regression to the LLE, and propose the local linear ridge regression estimator (LLRRE). However, the finite sample and the asymptotic properties of the LLRRE are not discussed there. In this paper, upper bounds of the finite-sample variance and bias of the LLRRE are obtained. It is shown that if the ridge regression parameters are not properly selected, then the resulting LLRRE has some drawbacks. For example, it may have a nonzero constant asymptotic bias, may suffer from boundary effects, or may be unable to share the nice asymptotic bias quality of the LLE. On the other hand, if the ridge regression parameters are properly selected, then the resulting LLRRE does not suffer from the above problems, and has the same asymptotic mean-square error as the LLE. For this purpose, the ridge regression parameters are allowed to depend on the sample size, and converge to 0 as the sample size increases. In practice, to select both the bandwidth and the ridge regression parameters, the idea of cross-validation is applied. Simulation studies demonstrate that the LLRRE using the cross-validated bandwidth and ridge regression parameters could have smaller sample mean integrated square error than the LLE using the cross-validated bandwidth, in reasonable sample sizes.[[notice]]補正完畢[[incitationindex]]SC

Parameter Heterogeneity in The Foreign Direct Investment-Income Inequality Relationship: A Semiparametric Regression Analysis

[[abstract]]This article uses the generalized likelihood ratio test to formally test whether the relationship between foreign direct investment (FDI) and income inequality varies with the level of human capital and then uses a flexible semiparametric smooth coefficient partially linear model to provide estimates of the inequality effect of FDI that are specific to the level of human capital in a country. Based on the data of 102 countries over the period 1970–2007, we find the following. First, there exists substantial heterogeneity in the inward FDI-inequality relationship. Inward FDI is inequality-ameliorating in low-income countries where human capital is scarce but is inequality-raising in middle- and high-income countries where human capital is abundant. Second, contrary to the conventional mindset, outward FDI has no significant impact on inequality in low-and high-income countries. Nevertheless, outward FDI is inequality-raising in middle-income countries with low levels of human capital. Our results demonstrate that accounting for parameter heterogeneity is critical to identify the key mechanisms through which FDI affects inequality. Omitting parameter heterogeneity could lead to misspecification and incorrect policy prescriptions.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SSCI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]DE