Spatial Analysis of 5-Star Hotels in Istanbul

Turkey has achieved great success in the tourism sector which started a greater than ever trend towards hotel investments. While, new investors are entering to the market, international brands are pursuing strategies to increase their existing supply. Istanbul, Turkey’s largest city, besides being a world-famous tourist attraction, also draws substantial foreign investment which escalates both the demand and supply in the hotel market. In the light of previous researches conducted by Dökmeci and Balta (1999) this research focuses on the supply side and the spatial development of high-end hotels in Istanbul. By revisiting the works of von Thünen and Alonso, compares rings of urban location for hotels in Istanbul in terms of rent as overnight room rates. Location data are accumulated from Ministry of Tourism, local municipalities, chambers and unions. Overnight hotel rates were collected through internet booking sites, telephone inquiries and visits during October, November and December of 2010. As the polycentric development of the city has increased over the last decade and many functions have been decentralized or shifted, the analysis reveals valuable insight into urban tourism pattern. The results coincide with the concentric rings described by the previous researches although many new hotels were constructed, new sub-centers had formed and the importance of sub-centers has increased dramatically.

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Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments

We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units. The approach is valid in high dimensional settings, where the effects are proxied by machine learning methods. We post-process these proxies into the estimates of the key features. Our approach is generic, it can be used in conjunction with penalized methods, deep and shallow neural networks, canonical and new random forests, boosted trees, and ensemble methods. It does not rely on strong assumptions. In particular, we don't require conditions for consistency of the machine learning methods. Estimation and inference relies on repeated data splitting to avoid overfitting and achieve validity. For inference, we take medians of p-values and medians of confidence intervals, resulting from many different data splits, and then adjust their nominal level to guarantee uniform validity. This variational inference method is shown to be uniformly valid and quantifies the uncertainty coming from both parameter estimation and data splitting. We illustrate the use of the approach with two randomized experiments in development on the effects of microcredit and nudges to stimulate immunization demand.Comment: 53 pages, 6 figures, 15 table

Generic machine learning inference on heterogenous treatment effects in randomized experiments

We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units. The approach is valid in high dimensional settings, where the effects are proxied by machine learning methods. We post-process these proxies into the estimates of the key features. Our approach is generic, it can be used in conjunction with penalized methods, deep and shallow neural networks, canonical and new random forests, boosted trees, and ensemble methods. Our approach is agnostic and does not make unrealistic or hard-to-check assumptions; we don’t require conditions for consistency of the ML methods. Estimation and inference relies on repeated data splitting to avoid overfitting and achieve validity. For inference, we take medians of p-values and medians of confidence intervals, resulting from many different data splits, and then adjust their nominal level to guarantee uniform validity. This variational inference method is shown to be uniformly valid and quantifies the uncertainty coming from both parameter estimation and data splitting. The inference method could be of substantial independent interest in many machine learning applications. An empirical application to the impact of micro-credit on economic development illustrates the use of the approach in randomized experiments. An additional application to the impact of the gender discrimination on wages illustrates the potential use of the approach in observational studies, where machine learning methods can be used to condition flexibly on very high-dimensional controls.https://arxiv.org/abs/1712.04802First author draf

Spatial Analysis of 5-Star Hotels in Istanbul

Turkey has achieved great success in the tourism sector which started a greater than ever trend towards hotel investments. While, new investors are entering to the market, international brands are pursuing strategies to increase their existing supply. Istanbul, Turkey's largest city, besides being a world-famous tourist attraction, also draws substantial foreign investment which escalates both the demand and supply in the hotel market. In the light of previous researches conducted by Dökmeci and Balta (1999) this research focuses on the supply side and the spatial development of high-end hotels in Istanbul. By revisiting the works of von Thünen and Alonso, compares rings of urban location for hotels in Istanbul in terms of rent as overnight room rates. Location data are accumulated from Ministry of Tourism, local municipalities, chambers and unions. Overnight hotel rates were collected through internet booking sites, telephone inquiries and visits during October, November and December of 2010. As the polycentric development of the city has increased over the last decade and many functions have been decentralized or shifted, the analysis reveals valuable insight into urban tourism pattern. The results coincide with the concentric rings described by the previous researches although many new hotels were constructed, new sub-centers had formed and the importance of sub-centers has increased dramatically

A Conversation on Academic Freedom in Turkey

This event will provide the occasion to share information on the Turkish situation and explore both the local roots and the international context of the assault on academic freedom (with comparisons to the recent crackdown on academics in Egypt, India, and elsewhere). Ohio State alumnus Yücel Demirer, one of the professors affected, will share his experience, and students and faculty at Ohio State will discuss the implications of the situation for their own careers and research. We will also consider the practical challenges of international solidarity in such situations.Ohio State University. Mershon Center for International Security StudiesEvent web pag

Semi-Parametric Efficient Policy Learning with Continuous Actions

We consider off-policy evaluation and optimization with continuous action spaces. We focus on observational data where the data collection policy is unknown and needs to be estimated. We take a semi-parametric approach where the value function takes a known parametric form in the treatment, but we are agnostic on how it depends on the observed contexts. We propose a doubly robust off-policy estimate for this setting and show that off-policy optimization based on this estimate is robust to estimation errors of the policy function or the regression model. Our results also apply if the model does not satisfy our semi-parametric form, but rather we measure regret in terms of the best projection of the true value function to this functional space. Our work extends prior approaches of policy optimization from observational data that only considered discrete actions. We provide an experimental evaluation of our method in a synthetic data example motivated by optimal personalized pricing and costly resource allocation

Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram

open access articleThis paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models

An experimental study on double-glazed flat plate solar water heating system in Turkey

Domestic hot water preparation systems with flat plate solar collectors are widely used in Turkey. In this collector, the temperature difference between the required water temperature and the ambient air temperature increase causes a decrease in the efficiency of the collector. In this study, the use of double glass in order to increase the efficiency of the collector is studied experimentally. The location is in Isparta South West Turkey. Experimental study is conducted in May 2013 at the Suleyman Demirel University, Isparta. The system components are solar simulator, solar collector, tank, circulation pump, flowmeter, thermocouples, data acquisition device and solar sensor. Solar collector system's operating temperature is 50°C for winter also summer. The difference between the collector temperature and the ambient air temperature exceeds 25°C in many cases, were found to be more efficient double-glazed collectors. When the temperature difference is 40°C, using double glazing collector is 24% more efficient than using single glazing collector