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Tüketici davranışları, değişen yaşam koşulları doğrultusunda farklılaşmakta ve sanal market kullanımı her zamankinden daha yüksek bir hızla artmaktadır. Bu durum, pazarda rekabet eden firmaları yeni iş modelleri arayışına yöneltmekte ve sanal market sektöründe rekabet eden ve/veya sektöre yeni girmeyi hedefleyen firmaların hedef müşteri kitlesini anlayabilmeleri, pazar stratejilerini belirlemeleri açısından önem arz etmektedir. Bu çalışmada, sanal market kullanımına yönelen tüketici yapısını anlayabilmek amacı ile bir anket çalışması uygulanmış olup; anket sonuçları istatistiksel çıkarım yöntemleri ile incelenerek, sanal market kullanımını tercih eden tüketicilerin demografik yapısı belirlenmiştir. Daha sonra belirlenen demografik özellikler üzerinden, sınıflandırma problemleri için kullanılan çeşitli makine öğrenmesi teknikleri ile bireylerin sanal market kullanımları tahmin edilmiş ve kullanılan tahmin modelleri doğruluk performans ölçütü ile kıyaslanmıştır. Karşılaştırmalar sonucunda rasgele orman tekniği en yüksek doğruluk oranına ulaşmış olup bireylerin kullanım tercihleri %78 doğrulukla tahmin edilebilmiştir. Bunlara ek olarak, anket cevapları ışığında sanal market sektöründe faaliyet gösteren/gösterecek olan firmalara müşteri memnuniyeti üzerinde etken olabilecek faktörler hakkında bazı iç görüler sunulmuştur. Elde edilen sonuçlar, hızlı ve kullanımı kolay mobil aplikasyonlarla satış sonrası hizmetlerin müşterilerin sanal market kullanımını artırdığını, minimum tutar ve sınırlı teslimat bölgelerinin kullanımı azalttığını göstermiştir., Altering life styles urge consumers to diversify their behaviours towards an ever increasing demand for online market use. This current trend makes market players to seek for contemporary business models. In that respect, understanding their target customers deeply has become prominent for the companies that has been in online market for a while and also the companies that are planning to enter the market in terms of developing effective marketing strategies. In this study, we apply an online survey with the goal of exploring the consumer profile in online market. We investigate the answers with statistical inference techniques and identify key demographic determinants of the consumers who opt for online grocery shopping. Then, we use machine learning techniques, particularly classification techniques, to estimate ‘buy’ or ‘do not buy’ decisions of the consumers over the predetermined demographic factors and we compare performance of different estimation methodologies according to their accuracy levels. Among others, the random forest technique has yielded the best accuracy, where we have achieved 78% accuracy on the test set. Furthermore, we provide current and potential companies in online grocery market with some insights regarding the influential factors on customer satisfaction. We have found that user friendly mobile applications and efficient after-sales services had a positive impact on the ‘buy’ decision of a customer, while setting a minimum amount to checkout and designing a restricted delivery area had negative impact on the ‘buy’ decision of a customer.

Efficient mass transit provision should be responsive to the behaviour of passengers. Operators often conduct surveys to elicit passenger perspectives, but these can be expensive to administer and can suffer from hypothetical biases. With the advent of smart card and automated vehicle location data, operators have reliable sources of revealed preference (RP) data that can be utilized to estimate transit riders’ valuation of service attributes. To date, effective use of RP data has been limited due to modelling complexities. We propose a dynamic choice model (DCM) for population-level longitudinal RP data to address prominent challenges. In the DCM, riders are assumed to follow different decision rules (compensatory and inertia/habit) and temporal switching between decision rules based on experience-based learning is also formulated. We develop an expectation–maximization algorithm to estimate the DCM and apply our model to estimate passenger valuation of crowding. Using large-scale data of 2 months with over four million daily trips by an Asian metro, our DCM estimates show an increase of 47% in passenger’s valuation of travel time under extremely crowded conditions. Furthermore, the average passenger follows the compensatory rule on only 25.5% or fewer trips. These results are valuable for supply-side decisions of transit operators.

We study statistical aspects of state-dependent Hawkes processes, which are an extension of Hawkes processes where a self- and cross-exciting counting process and a state process are fully coupled, interacting with each other. The excitation kernel of the counting process depends on the state process that, reciprocally, switches state when there is an event in the counting process. We first establish the existence and uniqueness of state-dependent Hawkes processes and explain how they can be simulated. Then we develop maximum likelihood estimation methodology for parametric specifications of the process. We apply state-dependent Hawkes processes to high-frequency limit order book data, allowing us to build a novel model that captures the feedback loop between the order flow and the shape of the limit order book. We estimate two specifications of the model, using the bid-ask spread and the queue imbalance as state variables, and find that excitation effects in the order flow are strongly state-dependent. Additionally, we find that the endogeneity of the order flow, measured by the magnitude of excitation, is also state-dependent, being more pronounced in disequilibrium states of the limit order book., Comment: 33 pages, 13 figures, v3: added a comparison with other related models plus some other minor amendments, to appear in Quantitative Finance

Invariance of the measurement model (MM) between subjects and within subjects over time is a prerequisite for drawing valid inferences when studying dynamics of psychological factors in intensive longitudinal data. To conveniently evaluate this invariance, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects' observations into a few states and state-specific factor analyses reveal what the MMs look like. However, to estimate the model, Vogelsmeier, Vermunt, van Roekel, and De Roover (2019) introduced a one-step (full information maximum likelihood; FIML) approach that is counterintuitive for applied researchers and entails cumbersome model selection procedures in the presence of many covariates. In this paper, we simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state memberships by splitting the estimation in three intuitive steps: (1) obtain states with mixture factor analysis while treating repeated measures as independent, (2) assign observations to the states, and (3) use these states in a discrete- or continuous-time latent Markov model taking into account classification errors. A real data example demonstrates the empirical value. ispartof: MULTIVARIATE BEHAVIORAL RESEARCH vol:58 issue:2 ispartof: location:United States status: published

We examine the role of trustworthiness and trust in statistical inference, arguing that it is the extent of trustworthiness in inferential statistical tools which enables trust in the conclusions. Certain tools, such as the p-value and significance test, have recently come under renewed criticism, with some arguing that they damage trust in statistics. We argue the contrary, beginning from the position that the central role of these methods is to form the basis for trusted conclusions in the face of uncertainty in the data, and noting that it is the misuse and misunderstanding of these tools which damages trustworthiness and hence trust. We go on to argue that recent calls to ban these tools tackle the symptom, not the cause, and themselves risk damaging the capability of science to advance, as well as risking feeding into public suspicion of the discipline of statistics. The consequence could be aggravated mistrust of our discipline and of science more generally. In short, the very proposals could work in quite the contrary direction from that intended. We make some alternative proposals for tackling the misuse and misunderstanding of these methods, and for how trust in our discipline might be promoted.

Data and text mining is the process of analyzing a large amount of semi-structured, unstructured and structured data based on users' demands to distinguish meaningful relationships and trends. By using the data and text mining techniques, companies can produce effective solutions to the problems within their business and with their competitors. Thus, they can turn this information into competitive advantage. In this study, it is aimed to evaluate the strengths and weaknesses of airline companies in competitive market according to the demands and needs of customers through using the data and text mining algorithms. In this research, online user reviews on TripAdvisor which is one of the social media sites was included in this research. 26 airlines which are the members of Star Alliance Global Airline Association were evaluated. The criteria used in this research was determined based on the comments and scores of each user. After determining the polarities from customer comments with Sentiment Analysis, the results obtained by classification and estimation with Support Vector Machines, Naive Bayes, Deep Learning Algorithms were compared. In addition, the results were compared before and after the Covid-19 pandemic. In the comparison made, it was determined that Deep learning gives better results., Veri ve metin madenciliği, anlamlı ilişkileri ve eğilimleri ayırt etmek için kullanıcıların taleplerine göre yapılandırılmış, yarı yapılandırılmış ve yapılandırılmamış büyük bir veri miktarını analiz etme sürecidir. İşletmeler, veri ve metin madenciliği teknikleri kullanarak hem kendi işletmeleri içerisinde hem de rakipleri ile rekabette yaşadıkları sorunlarına etkili çözümler üretebilmektedirler. Böylece elde ettikleri bilgiyi rekabet avantajına çevirebilmektedirler. Bu araştırmada, veri ve metin madenciliği algoritmaları kullanılarak rekabete dayalı pazarda müşterilerin istek ve ihtiyaçlarına göre hava yolu firmalarının üstün ve zayıf yönlerinin değerlendirilmesi amaçlanmıştır. Bu araştırmada, sosyal medya sitelerinden olan TripAdvisor’daki çevrimiçi seyahat incelemeleri araştırma kapsamına alınmıştır. Star Alliance küresel hava yolu birliğine üye 26 hava yolu firması değerlendirilmiştir. Araştırmada kullanılan kriterler; her bir kullanıcının yorum ve skorları temel alınarak belirlenmiştir. Duygu Analizi ile müşteri yorumlarından polariteleri belirlendikten sonra Destek Vektör Makineleri, Naive Bayes, Derin Öğrenme Algoritmaları ile sınıflandırma ve tahminleme yapılarak elde edilen sonuçlar karşılaştırılmıştır. Bunun yanı sıra sonuçlar, Covid-19 pandemisi öncesi ve sonrası olarak da karşılaştırılmıştır. Yapılan karşılaştırmada Derin Öğrenmenin daha iyi sonuç verdiği saptanmıştır.

Despite its importance in terms of survey participation, the literature is sparse on how face-to-face interviewers differentially affect specific groups of sample units. This paper demonstrates how an alternative parametrization of the random components in multilevel models, so-called separate coding, delivers valuable insights into differential interviewer effects for specific groups of sample members. In the example of a face-to-face recruitment interview for a probability-based online panel, we detect small interviewer effects regarding survey participation for non-Internet households, whereas we find sizable interviewer effects for Internet households. We derive practical guidance for survey practitioners to address differential interviewer effects based on the proposed variance decomposition.

Bu çalışmanın amacı İslami bankacılık konusunu inceleyen çalışmaların bibliyometrik analizini yapmaktır. Web of Science atıf dizininde 1982-2020 Haziran aylarını kapsayan tarama sonucunda 307 adet makaleye ulaşılmıştır. Makaleler görsel haritalama tekniği ile analiz edilmiştir. Konunun durumu ve eğilimi, konu üzerinde çalışan yazarlar, kaynaklar, dokumanlar, ülkeler ve terimler arasındaki ilişkiler VOSviewer programı kullanılarak görselleştirilmiştir. 2010 yılından itibaren konuyla ilgili makalelerin artmaya başladığı gözlenmiş Malezya’nın en çok makale üretmesine karşın ABD’nin en güçlü bağlantılara sahip olduğu tespit edilmiştir. Dokümanlarda İslamic Banking, İslamic Banks ve Pakistan anahtar kelimelerinin daha çok kullanıldığı, en fazla atıf alan yazar Beck ve diğ(2013), en fazla atıf alan dergi Journal of Banking & Finance olduğu belirlenmiştir.

Bu çalışmada banka müşterilerinin memnuniyet düzeylerinin belirlenmesi için bir ölçek geliştirilmesi amaçlanmış ve yapısal eşitlik modellemesi ile analiz edilmiştir. Geliştirilen ölçek aracılığıyla banka müşterilerinin memnuniyet düzeylerini ortaya koyan faktörler belirlenmiş ve bu faktörler aracılığıyla müşterilerin mevcut memnuniyet düzeyleri tespit edilmeye çalışılmıştır. Geliştirilen ölçekte banka müşterilerinin memnuniyet düzeylerini etkileyen “Fiziki Görünüm”, “Güvenilirlik”, “Heveslilik”, “Duyarlılık”, “Kuruma Bağlılık” ve “Yeterli Güvence” şeklinde 6 alt faktör bulunmaktadır. 7’si demografik değişken, 27’si geliştirilen ölçeğin maddeleri olmak üzere toplam 34 maddeden oluşan anket 250 banka müşterisine uygulanmıştır. Elde edilen verilere SPSS ve AMOS paket programları yardımıyla yapısal eşitlik modellemesi altında doğrulayıcı faktör analizi yapılmış ve uyum indeksleri x²/sd 2,291, In this study, it was aimed to develop a scale to determine the satisfaction levels of bank customers and analyzed with structural equation modeling. With the developed scale, the factors that reveal the satisfaction levels of the bank customers were determined and the current satisfaction levels of the customers were tried to be determined through these factors. In the developed scale, there are 6 sub-factors such as "Physical appearance, Reliability, Enthusiasm, Sensitivity, Organizational Loyalty and Adequate Assurance" that affect the satisfaction levels of bank customers. The questionnaire, consisting of a total of 34 items, 7 of which was demographic variable and 27 items of the developed scale, was applied to 250 bank customers. Confirmatory factor analysis was performed on the obtained data under structural equation modeling with the help of SPSS and AMOS package programs and the fit indexes were calculated as x²/sd 2.291

In 2019, the World Health Organization (WHO) named "Vaccine Hesitancy" one of the top 10 threats to global health. Shortly afterward, the COVID-19 pandemic emerged as the world's predominant health concern. COVID-19 vaccines of several types have been developed, tested, and partially deployed with remarkable speed; vaccines are now the primary control measure and hope for a return to normalcy. However, hesitancy concerning these vaccines, along with resistance to masking and other control measures, remains a substantial obstacle. The previous waves of vaccine hesitancy that led to the WHO threat designation, together with recent COVID-19 experience, provide a window for viewing new forms of social amplification of risk (SAR). Not surprisingly, vaccines provide fertile ground for questions, anxieties, concerns, and rumors. These appear in new globalized hyperconnected communications landscapes and in the context of complex human (social, economic, and political) systems that exhibit evolving concerns about vaccines and authorities. We look at drivers, impacts, and implications for vaccine initiatives in several recent historical examples and in the current efforts with COVID-19 vaccination. Findings and insights were drawn from the Vaccine Confidence Project's decade long monitoring of media and social media and its related research efforts. The trends in vaccine confidence and resistance have implications for updating the social amplification of risk framework (SARF); in turn, SARF has practical implications for guiding efforts to alleviate vaccine hesitancy and to mitigate harms from intentional and unintentional vaccine scares. ispartof: RISK ANALYSIS vol:42 issue:7 pages:1409-1422 ispartof: location:United States status: published

Çevresel sorunların küresel ölçekte gündeme gelmesiyle birlikte çevresel sorunların, ekonomik kalkınmadan ve toplumsal faktörlerden bağımsız olarak ele alınamayacağı, bu faktörlerin birbirleriyle ilişkilerinin boyutlarının ve yönünün bir bütün olarak incelenmesi gerekliliği ortaya çıkmıştır. Bu doğrultuda çevre kalitesi ve ekonomik büyüme arasındaki ilişkinin varlığı ve biçimine ilişkin Çevresel Kuznets Eğrisi (ÇKE) hipotezi ortaya konulmuştur. Bu hipotez temelde Çevresel bozulma ile ekonomik büyüme arasında bir ilişkinin olduğunu ve ekonomik büyümenin ilk evresinde çevresel bozulmalar gözlemlenirken, ekonomik büyümenin belirli bir noktaya ulaşmasından sonra çevresel bozulmanın giderek azaldığını ileri sürmektedir.G7 ülkeleri (Almanya, Amerika Birleşik Devletleri, İngiltere, İtalya, Fransa, Japonya ve Kanada) için Çevresel Kuznets Eğrisi hipotezinin geçerli olup olmadığı doğrusal panel veri yöntemleri ile araştırılması bu çalışmanın amacını oluşturmaktadır. 1981-2018 dönemini kapsayan, kişi başı GSYH, kişi başı CO2 emisyonu ve nüfus artış hızı değişkenleri veri seti olarak ele alınmıştır. İlk olarak, çalışmada kullanılan değişkenlere ait serilerin yatay kesit bağımlılıkları incelenmiş ve durağanlıkları panel birim kök testleri ile analiz edilmiştir. Panelde değişkenler arasında uzun dönemli ilişkinin olup olmadığını test etmek için panel eş bütünleşme testleri uygulanmış ve uzun dönem katsayıların tahmini yapılarak ÇKE geçerliliği sınanmıştır. Yapılan analizler sonucunda G7 ülkelerinde Çevresel Kuznets eğrisinin geçerli olduğu belirlenmiştir. Buna göre kişi başına GSYH, 34453,59$ değerine ulaştığında çevresel bozulmada azalış yönünde eğilimin olacağı saptanmıştır., With environmental problems coming to the fore on a global scale, it has emerged that environmental problems cannot be handled independently from economic development and social factors, and that the dimensions and direction of the relations of these factors with each other should be examined as a whole. In this direction, the Environmental Kuznets Curve (ÇKE) hypothesis has been put forward regarding the existence and form of the relationship between environmental quality and economic growth. This hypothesis basically argues that there is a relationship between environmental degradation and economic growth, and while environmental degradation is observed in the first phase of economic growth, environmental degradation gradually decreases after economic growth reaches a certain point.The aim of this study is to investigate whether the Environmental Kuznets Curve hypothesis is valid for the G7 countries (Germany, United States of America, England, Italy, France, Japan and Canada) using linear panel data methods. Covering the 1981-2018 period, GDP per capita, CO2 emissions per capita and population growth rate variables are considered as data set. First, the cross-section dependencies of the series of the variables used in the study were examined and their stationarities were analyzed by panel unit root tests. In order to test whether there is a long-term relationship between the variables in the panel, panel cointegration tests were applied and the validity of the EKC was tested by estimating the long-term coefficients. As a result of the analysis, it was determined that the Environmental Kuznets curve is valid in G7 countries. Accordingly, when the per capita GDP reaches $34453.59, it is determined that there will be a decreasing trend in environmental degradation.

The low contribution of economic sectors (industry, agriculture and services) to the gross domestic product. The decrease in bank loans to productive sectors, the high risk of banking in productive sectors and the low competitiveness of domestic production. A bank loan is one of the main sources of financing for stimulating and increasing local production. The concept of banking risk management is important. When determining the risk effect, it is necessary to calculate the long-term effects of macroeconomic factors on the banking sector separately. Some researchers dealing with banking risks are well aware that markets do not directly affect banks. Economic, political and social conditions in the banking sector is always the possibility of incurring losses in the future and danger on one side of the last transfer, increasing the likelihood of banking crises affects one side of natural and legal persons, financial market can make it tough. In the study, Derman (2002)’s theories posed and Ulusoy (2008)’s econophysics analysis and financial calculations in the framework of the concept of entropy developed in econophysics using temperature has been studied and the calculations performed in this study have been revealed.

The regulator in Europe calls for the market-consistent valuation of the insurance liabilities that usually are not (fully) tradable. An example of such liabilities is the participating pension contract that is generally long-dated and vulnerable to the medium-time dynamics of the underlying risk drivers. Dealing with these characteristics requires time-consistent pricing. However, the well-known non-linear premium principles, often used as pricing operators, are not time-consistent. Based on this motivation, we study the time-consistent and market- consistent (TCMC) actuarial valuation of the participating pension contracts with hybrid payoff. We use a standard profit-sharing mechanism with guaranteed interest rate, and generalize it to a hybrid profit-sharing mechanism with the actuarial and hedgeable financial risks, over the course of the contract. Market-consistency is maintained by "two-step actuarial valuation" in a one-period setting. Time-consistency is obtained by a "backward iteration" of these one-period two-step valuations over the predetermined sub-intervals of the valuation period. We use the Least-Square Monte-Carlo method to implement the conditional operators in the backward iteration. We compare the results of TCMC price to the expected value of the discounted payoff and measure the relative risk loading and time-consistency risk premium. Besides, we investigate the effect of the stochastic interest rate as compared to the deterministic one.

This paper analyzes the effect of a discrete treatment Z on a duration T. The treatment is not randomly assigned. The confounding issue is treated using a discrete instrumental variable explaining the treatment and independent of the error term of the model. Our framework is nonparametric and allows for random right censoring. This specification generates a nonlinear inverse problem and the average treatment effect is derived from its solution. We provide local and global identification properties that rely on a nonlinear system of equations. We propose an estimation procedure to solve this system and derive rates of convergence and conditions under which the estimator is asymptotically normal. When censoring makes identification fail, we develop partial identification results. Our estimators exhibit good finite sample properties in simulations. We also apply our methodology to the Illinois Reemployment Bonus Experiment.

In recent years, Data Envelopment Analysis (DEA) technique has been used quite frequently in determining container terminal efficiency. When the studies reviewed, conducted on the subject in the recent past, it is seen that the details such as the parameter on which the activity model focuses, sample selection, sample size and input-output selection have not yet been clarified enough, additionally, problems and confusion are encountered in practice. In this study, a critical analysis was carried out regarding the determinations in the use of the DEA technique, which is frequently used in the measurement of container terminal efficiency, and on which issues should be considered in order to establish the model better. In the consequences of the study, it was seen that data accessibility was an obstacle to reaching more robust results in efficiency analysis. It is very important to make evaluations between ports that are close to each other in terms of regional or infrastructure, demand and technological development in order to obtain more reliable and consistent results. Moreover, future studies should consider reliable inputs such as the number of equipment in the terminal that divided by two group, the terminal area, maximum depth, pier length.

Meta-analytic datasets can be large, especially when in primary studies multiple effect sizes are reported. The visualization of meta-analytic data is therefore useful to summarize data and understand information reported in primary studies. The gold standard figures in meta-analysis are forest and funnel plots. However, none of these plots can yet account for the existence of multiple effect sizes within primary studies. This manuscript describes extensions to the funnel plot, forest plot and caterpillar plot to adapt them to three-level meta-analyses. For forest plots, we propose to plot the study-specific effects and their precision, and to add additional confidence intervals that reflect the sampling variance of individual effect sizes. For caterpillar plots and funnel plots, we recommend to plot individual effect sizes and averaged study-effect sizes in two separate graphs. For the funnel plot, plotting separate graphs might improve the detection of both publication bias and/or selective outcome reporting bias.

Küresel planda ortaya çıkan rekabet ve ticaret savaşları, ülkeleri mal ve hizmet üretimlerinde verimliliklerini ve çıktılarını arttıracak Ar-Ge ve inovasyon faaliyetlerini gerçekleştirmeye itmiştir. Fakat bu durum, söz konusu faaliyetlerin finansmanının nasıl sağlanacağı konusunda birtakım sorunları da beraberinde getirmiştir. Bu süreçte krediler; ülkelerin, kurumların ve firmaların inovasyon sürecinde karşılaşabilecekleri finansman sorunlarının giderilmesinde anahtar bir rol oynamıştır. Bu öneme binaen bu çalışmada, özel sektöre yönelik olarak verilen kredilerin inovasyon üzerindeki etkileri 24 Avrupa Birliği üyesi ülke ve Türkiye bağlamında incelenmektedir. 2001 ve 2017 dönemi yıllık verilerini kapsayan çalışmada; Pedroni Eşbütünleşme Analizi, Granger Nedensellik Analizi, FMOLS ve DOLS tahmincileri kullanılarak değişkenler arasındaki ilişki analiz edilmektedir. Çalışmadan elde edilen bulgular, özel sektöre verilen kredilerdeki artışların inovasyonu arttırdığını göstermektedir. FMOLS tahmin sonuçlarına göre; özel sektöre verilen kredi hacmindeki, eğitim endeksindeki ve Ar-Ge harcamalarındaki %1’lik artış, patent başvurularını sırasıyla yaklaşık olarak; %0.17, %0.44 ve %1.24 oranında arttırmaktadır. DOLS modeli için bu oranlar aynı sırayla yaklaşık olarak; %0.32, %0.54 ve %0.85’dir.

Copulas are used to reveal the dependency structure between random variables. Measuring dependency with copula functions in both parametric and non-parametric situations, methods that can be an alternative to many methods and allow much simpler calculation of these calculations have been proposed. In this study, the dependency structure between the electricity index and the dollar rate was examined and interpreted using the copula function. The relationship between the two indices was made with MSE, AIC and BIC calculations. In the results of these calculations, it was determined that the most appropriate modeling according to MSE is with Clayton, and when viewed according to AIC and BIC, the most appropriate modeling will be done with Gumbel.

Accurate and rigorous applications of the econometric analysis is crucial when writing a high-quality analytical research paper in social sciences. This article provides the basic framework on how to construct econometric analysis for heterogenous non-stationary dynamic panel datasets. Panel data econometrics is a very broad field, naturally it will not be possible to include all methods in this study. Thus, we present details of the specific selected highly used standard panel data tests and estimations (Im-Pesaran-Shin unit root testing, Pedroni’s cointegration test, panel data ordinary least squares) in this paper and explain why and under which conditions these methods are applied. We employ theoretical formulations of mentioned tests and estimations along with Engle and Granger’s error correction mechanism in order to determine the order of integration and long-run relationship between the panel variables. In summary, we aim to explain basic steps for a straightforward empirical panel data research process for new researchers in social sciences.

Factorial surveys are a prominent tool in the social sciences. Reanalyzing a literature sur��vey on the factorial survey approach (Wallander, 2009), I show that about a quarter of ap��plied factorial surveys asks respondents to provide multiple ratings on the same vignette. This paper is the first to propose a statistical modeling approach for precisely this situation. Data from factorial surveys with multiple ratings per vignette are afflicted with two sourc��es of statistical dependencies. First, each respondent answers multiple vignettes, which is typically accounted for via random effects models, and, second, each vignette prompts multiple ratings. The first problem is common for almost any factorial survey and has been addressed decades ago. The second problem is addressed here. I propose to apply a seem��ingly unrelated regression approach to account for the statistical dependencies between multiple ratings per vignette. Due to the use of a structural equation modeling approach, the model allows not only to correctly compare coefficients across ratings but also to ana��lyze the factor structure underlying these ratings. The proposed model is illustrated by two examples from recent research. All data and syntax are available online and allows for an easy adaption of the proposed model to readers��� own research., methods, data, analyses, Online First

Using hierarchical classification methods, the relationship between temperature and precipitation amount of the tea producing regions of the leading countries in tea production was investigated. Using the minimum spanning tree method in this article has allowed us to very clearly visualize the difficult and complex relationship. Countries that show similar average temperature and precipitation changes are linked in the same group. According to the results, it is understood that monthly temperature change and monthly precipitation rates is effective in tea production., Bu çalışmada Hiyerarşik sınıflandırma yöntemleri kullanılarak çay üretiminde önde gelen ülkelerin çay üretimi yapılan bölgelerinin aylık ortalama sıcaklık ve aylık ortalama toplam yağış miktarı arasındaki ilişki araştırıldı. Bu makalede en küçük örten ağaç yöntemi kullanmak, zor ve karmaşık ilişkiyi çok açık bir şekilde görselleştirmemizi sağlamıştır. Benzer ortalama sıcaklık ve yağış miktarı değişimleri gösteren ülkeler aynı grupta birbirlerine bağlanmaktadırlar. Çay üretimi yapılan ülkelerin genellikle sıcaklık değişimleri bakımından benzer karakteristikler gösterdiği ve aynı grupta yer aldığı görülmüştür. Elde edilen sonuçlara göre çay üretiminde aylık sıcaklık değişimi ve aylık ortalama yağış miktarının etkili olduğu anlaşılmaktadır.

In the article examining the factors affecting the profitability indicators of the banking sector, which has a large share in the financial sector in Turkey, the analysis of the factors affecting the return on assets and return on equity ratios of the top 10 deposit banks in 2020, ranked according to their asset size in 2020, was made with the lasso regression method. As a result of the analysis, separate models were established for return on assets and return on equity. Among the independent variables, only the deposit variable was excluded from the model and it was seen that the effect on profitability was not as much as the variables of securities portfolio, equity, NPL ratio, asset share, inflation and gross domestic product.

We define the concept of good trade execution and we construct explicit adapted good trade execution strategies in the framework of linear temporary market impact. Good trade execution strategies are dynamic, in the sense that they react to the actual realisation of the traded asset price path over the trading period; this is paramount in volatile regimes, where price trajectories can considerably deviate from their expected value. Remarkably however, the implementation of our strategies does not require the full specification of an SDE evolution for the traded asset price, making them robust across different models. Moreover, rather than minimising the expected trading cost, good trade execution strategies minimise trading costs in a pathwise sense, a point of view not yet considered in the literature. The mathematical apparatus for such a pathwise minimisation hinges on certain random Young differential equations that correspond to the Euler-Lagrange equations of the classical Calculus of Variations. These Young differential equations characterise our good trade execution strategies in terms of an initial value problem that allows for easy implementations., 31 pages, 3 figures

Asset returns incorporate new information via the effects of independent and possibly identically distributed random shocks. They may also incorporate long memory effects related to the concept of self‐similarity. The two approaches are here combined. In addition, methods are proposed for estimating the contribution of each component and evidence supporting the presence of both components in both the physical and risk‐neutral distributions is presented. Furthermore, it is shown that long‐horizon returns may be nonnormal when there is a self‐similar component. The presence of a self‐similar component also questions positive equity biases over the longer term.

Hedging techniques have been widely adopted in market-consistent or fair valuation approach required by recent solvency regulations, to take into account the market prices of the hedgeable parts of insurance liabilities. In this study, we investigate the fair dynamic valuation of insurance liabilities, which are model-consistent (mark-to-model), market-consistent (mark-to-market), and time-consistent, as proposed by Barigou et al. (2019) in a multi-period setting. We introduce the loss averse convex hedging technique, which `punishes' loss outcomes more than gain outcomes. We prove that fair dynamic valuations are equivalent to the class of loss averse convex hedge-based valuation. Moreover, we propose and provide a complete characterization of loss averse mean-variance hedging and show how to implement loss averse mean-variance hedge-based dynamic valuations using numerical examples.

Since factor analysis is one of the most often used techniques in psychometrics, comparing or combining solutions from different factor analyses is often needed. Several measures to compare factors exist, one of the best known is Tucker's congruence coefficient, which is enjoying newly found popularity thanks to the recent work of Lorenzo-Seva and ten Berge (2006), who established cut-off values for factor congruence. While this coefficient is in most cases very good in comparing factors in general, it also has some disadvantages, which can cause trouble when one needs to compare or combine many analyses. In this paper, we propose a modified Tucker's congruence coefficient to address these issues. Lovik, A (corresponding author), Univ Leuven, I BioStat, KU Leuven, Kapucijnenvoer 35 Blok D, B-3000 Leuven, Belgium. aniko.lovik@kuleuven.be

In this paper, we develop econometric tools to analyze the integrated volatility of the efficient price and the dynamic properties of microstructure noise in high-frequency data under general dependent noise. We first develop consistent estimators of the variance and autocovariances of noise using a variant of realized volatility. Next, we employ these estimators to adapt the pre-averaging method and derive a consistent estimator of the integrated volatility, which converges stably to a mixed Gaussian distribution at the optimal rate $n^{1/4}$. To refine the finite sample performance, we propose a two-step approach that corrects the finite sample bias, which turns out to be crucial in applications. Our extensive simulation studies demonstrate the excellent performance of our two-step estimators. In an empirical study, we characterize the dependence structures of microstructure noise in several popular sampling schemes and provide intuitive economic interpretations; we also illustrate the importance of accounting for both the serial dependence in noise and the finite sample bias when estimating integrated volatility.

Summary Concerns over women's safety on public transport systems are commonly reported in the media. We develop statistical models to test for gender differences in the perception of safety and satisfaction on urban metros and buses by using large-scale unique customer satisfaction data for 28 world cities over the period 2009–2018. Results indicate a significant gender gap in the perception of safety, with women being 10% more likely than men to feel unsafe in metros (6% for buses). This gender gap is larger for safety than for overall satisfaction (3% in metros and 2.5% in buses), which is consistent with safety being one dimension of overall satisfaction. Results are stable across specifications and robust to inclusion of city level and time controls. We find heterogeneous responses by sociodemographic characteristics. Data indicate that 45% of women feel secure in trains and metro stations (and 55% in buses). Thus the gender gap encompasses more differences in transport perception between men and women rather than an intrinsic network fear. Additional models test for the influence of metro characteristics on perceived safety levels and find that more acts of violence, larger carriages and emptier vehicles decrease women's feeling of safety.

Default risk significantly affects the corporate policies of a firm. We develop a model in which a limited liability entity subject to default at an exponential random time jointly sets its dividend policy and capital structure to maximize the expected lifetime utility from consumption of risk‐averse equity investors. We give a complete characterization of the solution to the singular stochastic control problem. The optimal policy involves paying dividends to keep the ratio of firm's equity value to investors' wealth below a critical threshold. Dividend payout acts as a precautionary channel to transfer wealth from the firm to investors for mitigation of losses in the event of default. Higher the default risk, more aggressively the firm leverages and pays dividends.

This paper explicitly derives the optimal dynamic consumption and portfolio choice of an individual with prospect theory preferences. The individual is loss averse, endogenously updates his reference level over time, and distorts probabilities. We show that the optimal consumption strategy is rather insensitive to economic shocks. In particular, in case the individual sufficiently overweights unlikely unfavorable events, our model generates an endogenous floor on consumption. As a result, an individual with prospect theory preferences typically implements a (very) conservative portfolio strategy. We discuss implications of our results for the design of investment-linked annuity products.

A classic approach to financial risk management is the use of scenario analysis to stress test portfolios. In the case of an S&P 500 options portfolio, for example, a scenario analysis might report a P&L of −$1m in the event the S&P 500 falls 5% and its implied volatility surface increases by 3 percentage points. But how accurate is this reported value of −$1m? Such a number is typically computed under the (implicit) assumption that all other risk factors are set to zero. But this assumption is generally not justified as it ignores the often substantial statistical dependence among the risk factors. In particular, the expected values of the non-stressed factors conditional on the values of the stressed factors are generally non-zero. Moreover, even if the non-stressed factors were set to their conditional expected values rather than zero, the reported P&L might still be inaccurate due to convexity effects, particularly in the case of derivatives portfolios. A further weakness of this standard approach to scenario analysis is that the reported P&L numbers are generally not back-tested so their accuracy is not subjected to any statistical tests. There are many reasons for this but perhaps the main one is that scenario analysis for derivatives portfolios is typically conducted without having a probabilistic model for the underlying dynamics of the risk factors under the physical measure P. In this paper we address these weaknesses by embedding the scenario analysis within a dynamic factor model for the underlying risk factors. Such an approach typically requires multivariate state-space models that can model the real-world behavior of financial markets where risk factors are often latent, and that are sufficiently tractable so that we can compute (or simulate from) the conditional distribution of unstressed risk factors. We demonstrate how this can be done for observable as well as latent risk factors in examples drawn from options and fixed income markets. We show how the two forms of scenario analysis can lead to dramatically different results particularly in the case of portfolios that have been designed to be neutral to a subset of the risk factors.

This study predicts the cash holdings policy of Turkish firms, given the 20 selected features with machine learning algorithm methods. 211 listed firms in the Borsa Istanbul are analyzed over the period between 2006 and 2019. Multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), decision trees (DT), extreme gradient boosting algorithm (XGBoost) and multi-layer neural networks (MLNN) are used for prediction. Results reveal that MLR, KNN, and SVR provide high root mean square error (RMSE) and low R2 values. Meanwhile, more complex algorithms, such as DT and especially XGBoost, derive higher accuracy with a 0.73 R2 value. Therefore, using advanced machine learning algorithms, we may predict cash holdings considerably. WOS:000796993600001 2-s2.0-85130279042 PMID: 35601748 Social Sciences Citation Index Q1 Article Uluslararası işbirliği ile yapılmayan - HAYIR Mayıs 2022 YÖK - 2021-22 Mayıs

We provide a unifying treatment of pathwise moderate deviations for models commonly used in financial applications, and for related integrated functionals. Suitable scaling enables us to transfer these results into small‐time, large‐time, and tail asymptotics for diffusions, as well as for option prices and realized variances. In passing, we highlight some intuitive relationships between moderate deviations rate functions and their large deviations counterparts; these turn out to be useful for numerical purposes, as large deviations rate functions are often difficult to compute.

A well-defined agent-based asset pricing model able to match the widely observed properties of financial time series is valuable for testing the implications of various biases associated with investors' behaviour. Extending one of the most successful models in capturing traders behaviour, we present a new behavioural agent-based asset pricing model. Specifically, we introduce a well-known behavioural bias in the model, loss aversion, and evaluate its implications. First, measuring how close the simulated time series are to its empirical counterparts, we show that the model with loss aversion better matches and explains the properties of real-world financial data, compared with the base model without the behavioural bias. Secondly, we assess the impact of different levels of loss aversion not only on the agents' switching mechanisms, but also on the properties of the time series generated by the model. We demonstrate how for different levels of the loss aversion parameter, the biased agents tend to be driven out of the market at different points in time. Since even the simplest strategies have been shown to survive the competition in an agent-based setting, we can link our findings with the behavioural finance literature, which states that investors' systematic biases lead to unexpected market behaviour, instabilities and systematic errors. Finally, we provide an in-depth analysis of the simulated time series and show the resulting dynamics replicate a rich set of the stylized facts including: absence of autocorrelation, heavy tails, volatility clustering and conditional heavy tails of returns, long memory of absolute returns, as well as volume–volatility relations, gain–loss asymmetry, power-law behaviour and long memory of volume.

While the globalization process makes the world a big market without borders, it offers important opportunities to the economies. On the other hand, it increases the risk of fragility for economies that cannot manage the process properly. If the export mix is dependent on a small number of products and markets, the balance of the economy may deteriorate when there is a problem specific to these products or markets. In this direction, it is aimed to reduce the economic and political risks that may arise from being dependent on several products or markets with diversification in exports. Of course, first of all, it is necessary to determine whether such a situation exists or not. In this study, the product and market concentration level in the export of Mersin province in Turkey was determined by using the Concentration Ratio and the Gini-Hirschman Index. The findings showed that the product concentration in Mersin province exports is at the highest level and the country concentration is at the medium level. It has been observed that the concentration, which had an increasing trend between 2011 and 2014 in both product and country concentration, showed a decreasing trend in the 2014-2020 period. In the agricultural sector, which accounts for more than half of Mersin's exports, the option of increasing product diversity by turning to processed agricultural products and diversifying the market with geographical indications, branding and innovative products should be considered.

Nearly 90% of world trade is transported by sea. Maritime piracy is a major threat for maritime security, shippers, crews, cargo owners and insurance companies. Main reasons of piracy and armed robbery attacks, piracy statistics, international conventions, regulation and multinational efforts were examined and maritime piracy against ships are analyzed in the period 2015 to 2020 in the study. The Chi Square Test (χ2) was used to test statistical relationship between categorical variables such as piracy attacks by years, months and hours, types, regions and type of vessels attacked. The results of the frequeny distributions show that the most piracy attacks occurred in 2015 (20.9%), the most attacks were in March-April-May (30.2%), the most attacks were occured between the hours 24:00-04:00 (29.2%) the most attacks occurred in South East Asia (42.6%), the most type of attacks against to ships were boarded (79.1%), the most attacks were occurred against bulk carriers (28.6), Marshall Islands-flagged ships were the most attacked (17.1%). The results of Chi-Square Test show that there is a weak statistical relationship between the piracy attacks by months and regions; there is a weak statistical relationship between the piracy attacks by years and type of attacks. There is no statistical relationship between other variables. In the conclusion part of the study, some suggestions are proposed to combat the maritime piracy.

Exchange rate is an important variable in developing economies. The effectiveness of economic policies can also be easily affected by exchange rate variability. As such, determining the variables that will affect the exchange rate will contribute to the development of exchange rate strategies. For the formation of exchange rates under free market conditions, the supply and demand elements of the market should be examined. It is known that Turkey has been seriously affected by exchange rate changes over the years and continues to be affected today. In this study, it has been tried to determine whether there is any econometric relationship between the commercial loans extended in Turkey and the exchange rate, and if so, in which direction. At the end of the analysis, no cointegration relationship was found between the series, but causality from commercial loans to real exchange rate was determined., Döviz kuru gelişmekte olan ekonomilerde önemli bir değişkendir. Ekonomik politikaların etkinliği de döviz kuru değişkenliğinden kolaylıkla etkilenebilmektedir. Bu haliyle döviz kurunu etkileyecek değişkenlerin belirlenmesi, döviz kuru stratejilerinin geliştirilmesine katkı sağlayacaktır. Serbest piyasa şartları altında döviz kuru oluşumu için piyasasının arz talep unsurları incelenmelidir. Türkiye’nin de yıllar içerisinde döviz kuru değişimlerinden ciddi olarak etkilendiği, günümüzde de etkilenmeye devam ettiği bilinmektedir. Bu çalışmada Türkiye’de kullandırılan ticari kredilerin döviz kuru ile ekonometrik yönden herhangi bir ilişkisinin olup olmadığı eğer varsa hangi yönde olduğu tespit edilmeye çalışılmıştır. Analizler sonunda seriler arasında eşbütünleşme ilişkisi tespit edilmemiş fakat ticari kredilerden, reel efektif döviz kuruna nedensellik tespit edilmiştir.

Multiple baseline (MB) designs are becoming more prevalent in educational and behavioral research, and as they do, there is growing interest in combining effect size estimates across studies. To further refine the meta-analytic methods of estimating the effect, this study developed and compared eight alternative methods of estimating intervention effects from a set of MB studies. The methods differed in the assumptions made and varied in whether they relied on within- or between-series comparisons, modeled raw data or effect sizes, and did or did not standardize. Small sample functioning was examined through two simulation studies, which showed that when data were consistent with assumptions the bias was consistently less than 5% of the effect size for each method, whereas root mean squared error varied substantially across methods. When assumptions were violated, substantial biases were found. Implications and limitations are discussed.