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As the world is struggling with the coronavirus (COVID-19), it is an important research task to understand the negative impact of the disease on the tourism industry with similar examples from the past and to prepare countermeasures for it. This study examines the effects of tourist attention to Middle East respiratory syndrome (MERS) on international tourism demand for South Korea during 2009- 2016. Search volume indices for MERS in Google and Baidu were employed as a proxy for the tourist attention paid to MERS. Using a structural vector autoregression model with block exogeneity restrictions, we set up tourist attention to MERS as an exogenous variable and control for endogeneity between macroeconomic variables and tourist arrivals. Our results show that the tourist attention paid to MERS has detrimental effects on tourist arrivals and its effects are heterogeneous across visitor countries. While the negative effects of tourist attention to MERS on U.S. and European tourist arrivals were moderate, this negative information effect was more pronounced when the countries of visitors are closer to Korea, such as Japan and China. Future research directions and limitations of the study was discussed at the end of the study.

본 연구는 도시관광의 대표적인 매력 장소에 대한 관광 정체성이 매력 장소에 대한 만족 및 도시관광의 만족에 미치는 영향에 대해 검증하고자 하였다. 가설검증을 위해, 경주 대표 관광지이자 여행자 거리인 황리단길 방문객을 대상으로 설문조사를 실시하여 자료를 수집하였으며, 최종 239부의 표본을 사용하였다. 인구통계학적 및 일반적 특성 확인을 위해 빈도분석을 실시하였고, 구성개념간 집중타당성과 판별타당성을 검증하고 요인들 간의 인과관계를 검정하기 위해서 Smart PLS 프로그램을 이용하여 PLS path modeling 분석을 실시하였다. 연구결과, 첫째, 경주의 여행자 거리인 황리단길의 정체성 중, 자연환경적 정체성과 역사문화적 정체성은 여행자 거리 만족도에 미치는 영향이 유의하지 않았으며 둘째, 생활문화적 정체성과 기능적 정체성은 여행자 거리 만족도에 정(+)의 유의한 영향을 미치는 것으로 나타났다. 셋째, 여행자 거리에 대한 만족도는 경주라는 도시의 관광 만족도에 직접적이고 유의한 정(+)의 영향을 미치는 것으로 나타났다. 마지막으로 총 효과를 고려했을 때, 여행자 거리 관광 정체성 중 기능적 정체성이 생활문화적 정체성보다 경주의 도시관광 만족도에 더 큰 영향을 미치는 것으로 나타났다. 본 연구의 결과를 토대로 도시관광에서 중요한 매력물인 여행자 거리에 중요시되는 정체성과 그에 따른 지방정부의 관광정책을 위한 학술적 · 실무적 시사점을 제시하였다.

In this paper, we discuss estimation procedure and various inferential methods for varying coefficient panel data models that include spatially correlated error components. Our estimation procedure is an extension of the quasi-maximum likelihood method for spatial panel data regression to the conditional local kernel-weighted likelihood. We allow both relevant and irrelevant regressors in our model and propose a variable selection procedure that we show to perform well for models that involve spatial error dependence. We also extend our procedure so that it allows empirical modelling and testing of the so-called semi-varying coefficient specification. To ensure the statistical validity of our methods, we derive a set of asymptotic properties based on a collection of primitive assumptions that appear regularly in the nonparametric literature. Finally, we use the proposed model and methods to analyse the municipal disparities in mental health service spending by local authorities in England in order to illustrate practicability and empirical relevance.

Today, one of the biggest challenges facing the UK is the new target set when the nation became first major economy to pass net zero emissions law, which requires the country to bring all greenhouse gas emissions to net zero by 2050. On the one hand, there are already a few ideas about how we should farm and use land in order to deliver such a target. On the other hand, the government has a new strategy which is to pay farmers for providing public goods, especially for climate change mitigation through the reduction and storage of greenhouse gas emissions. The most critical task is to find a solution to such a question as "How should public spending on farm public goods be allocated?" In this paper, we argue that formulating an effective subsidy scheme cannot focus on the public need alone, but should also take into consideration what farmers must endure and the opportunities they must forgo. This requires a good understanding about the generating process behind the spatial heterogeneity of agricultural land-use at a _ne spatial scale. We aim to provide government and its agents with decision support for policy making post-Brexit in two directions. Firstly, we employ detailed spatial resolution data and establish a new statistical tool that can help: (i) to effectively capture the spatial heterogeneity of agricultural land-use, (ii) to disentangle the contributions of terrain formulations, environmental characteristics, climatic conditions, policies, and other legacy and agglomeration effects in the generating process of the land-use patterns, and (iii) accurately gauge their relative importance across different regions of GB for more targeted subsidies schemes. Secondly, we employ our new method and provide policy advice and evaluation.

Partially linear semiparametric models are advantageous to use in empirical studies of various economic problems due to a special feature that allows the parametric and nonparametric components to exist simultaneously in the model. However, systematic estimation procedures and methods have not yet been satisfactorily developed to deal effectively with a well-known endogeneity problem that may be present in some empirical applications. In the current paper, we aim to address endogeneity comprehensively, which may take place in either a parametric or a nonparametric component or both, and to provide guidance to an appropriate estimation procedure and method in the presence of such a problem. A significant difficulty we must overcome before such goals can be achieved is a generated regressor problem which arises because a critical part, known in the literature as the "control variables", is not observable in practice and hence must be estimated. We show theoretically (i.e. through the derivation of a set of important asymptotic properties) and experimentally (i.e. through the use of simulation exercises) that our newly introduced method can help in overcoming the above-mentioned endogeneity problem. For the sake of completeness, we also discuss an adaptive data-driven method of bandwidth selection and show its asymptotic optimality.