城市中央商务区(CBD)发展的国际比较

首先界定CBD及其对城市经济发展的重要性,然后比较国内外CBD发展.通过比较,国际都市如纽约、新加坡、伦敦等的CBD城市密度高、土地利用效率高、基本没有城市绿地.相反,中国许多城市的新建和已经建成的CBD密度低、城市绿地比重大.同国际都市CBD发展模型相比,也正是由于中国城市的CBD绿地的比重大,使CBD内的摩天大楼高度可以相比,但是土地开发密度和强度都显著偏低.中国城市CBD的另外一个特征是就业密度低,从而降低了城市经济的集聚效益.     

Forecasting cryptocurrency volatility

This paper studies the behavior of cryptocurrencies’ financial time series, of which Bitcoin is the most prominent example. The dynamics of these series are quite complex, displaying extreme observations, asymmetries, and several nonlinear characteristics that are difficult to model and forecast. We develop a new dynamic model that is able to account for long memory and asymmetries in the volatility process, as well as for the presence of time-varying skewness and kurtosis. The empirical application, carried out on 606 cryptocurrencies, indicates that a robust filter for the volatility of cryptocurrencies is strongly required. Forecasting results show that the inclusion of time-varying skewness systematically improves volatility, density, and quantile predictions at different horizons.     

Are professional forecasters overconfident?

Do professional forecasters have an accurate sense of the uncertainties surrounding their own forecasts? This paper examines forecaster overconfidence by comparing ex ante, surveyed forecaster uncertainty with ex post, realised uncertainty based on the dispersion of an individual’s forecast errors. Unlike the literature that focuses on consensus forecasts, our focus is at the level of the individual forecaster. Using microdata from the three major surveys of professional forecasters (Euro Area, US and UK), we examine real GDP growth forecasts over the period 1999–2015. Our findings show that overconfidence dominates among individual forecasters, particularly for longer forecast horizons, and that individual forecasters appear to have little understanding of their own uncertainty.     

Measuring urban sprawl using land use data

Digital land use data, generally derived by remote sensing operations, have become widely available for even the most remote areas of the globe. Here we investigate how to use land use data to measure three of the most characteristic aspects of urban sprawl: low density, low continuity of land use type (scatteredness), and low compactness of the shape of the city. For each of these categories we present multiple urban sprawl indicators. Some of these indicators have been used in the literature before, others we developed ourselves. For density measurements we illustrate how simple changes to common density indicators can improve their meaningfulness. With respect to scatteredness we show that the interpretation of entropy measures can be ambiguous. A variant on Moran’s I index does a better job at measuring scatteredness than entropy metrics. A problem that has not yet been discussed in the literature is that the grid structure of land use data can inflate the boundary of the measured area. This is particularly a problem when measuring urban compactness. We introduce new compactness indices that correct for this problem. To illustrate the discussed indices, we apply them to Graz, the second largest city in Austria, using data from the CORINE Land Cover (CLC) Project (European Environment Agency, 2010).     

Forecasting of density functions with an application to cross-sectional and intraday returns

This paper is concerned with the forecasting of probability density functions. Density functions are nonnegative and have a constrained integral, and thus do not constitute a vector space. The implementation of established functional time series forecasting methods for such nonlinear data is therefore problematic. Two new methods are developed and compared to two existing methods. The comparison is based on the densities derived from cross-sectional and intraday returns. For such data, one of our new approaches is shown to dominate the existing methods, while the other is comparable to one of the existing approaches.     

Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections

The Eurosystem staff forecasts are conditional on the financial markets, the global economy and fiscal policy outlook, and include expert judgement. We develop a multi-country BVAR for the four largest countries of the euro area and we show that it provides accurate conditional forecasts of policy relevant variables such as, for example, consumer prices and GDP. The forecasting accuracy and the ability to mimic the path of the Eurosystem projections suggest that the model is a valid benchmark to assess the consistency of the projections with the conditional assumptions. As such, the BVAR can be used to identify possible sources of judgement, based on the gaps between the Eurosystem projections and the historical regularities captured by the model.     

Modeling and forecasting return jumps using realized variation measures

This paper proposes a simple HAR-RV-based model to predict return jumps through a conditional density of jump size with time-varying moments. We model jump occurrences based on a version of the autoregressive conditional hazard model that relies on past continuous realized volatilities. Applying our methodology to seven equity indices on the U.S. and Chinese stock markets, we reach the following key findings: (i) jump occurrence and size are dependent on past realized volatility, (ii) the proposed model yields superior in- and out-of-sample jump size density forecasts compared to an ARMA(1,1)-GARCH(1,1) model, (iii) and the occurrence and sign of return jumps are predictable to some extent.     

Social distancing: The effect of density and power on restaurant consumers

The present research examines the joint effects of density and power on consumers’ attitudes and revisit intentions in a restaurant context. A 2 (Density: high vs. low) x 2 (Power: high vs. low) quasi-experimental design was employed. The restaurant’s built density was manipulated by keeping (high density condition) or removing (low density condition) the extra tables in the restaurant. In addition, individuals’ sense of power was measured and served as a moderator. A total of 327 general restaurant consumers were participants in this study. Results indicated that powerless people responded to a restaurant with high built density more positively (vs. low built density), whereas powerful people exhibited a similar level of attitudes and revisit intentions across the density conditions. Additionally, perceived territoriality was identified as the mediator of the effect of density and power on consumer responses.     

基于熵权灰色关联法的新疆铁路网扩张规律研究

为提高铁路网规划的合理性,选择铁路网发展状况、经济发展状况、社会发展状况3个维度的21个评价指标,运用熵权灰色关联法对影响新疆铁路网扩张的因素进行分析。以铁路网密度表征铁路网的扩张,驱动因子可以概括为GDP、收入水平、社会保障和人口数量,依据Cobb-Douglas生产函数建立模型,预测新疆15个地州(市)的铁路网密度。结果表明,新疆北疆铁路网密度优于南疆;在研究铁路网扩张规律时,除了考虑GDP、收入水平、社会保障和人口数量等因素外,还应顾及自然资源、建设历史、城市定位及政策偏好的区域差异。     

中国人工智能发展水平、区域差异及分布动态演进

科学有效测度人工智能发展水平是响应国家科技战略的有益尝试。运用熵权法，从制度环境、基础设施、技术创新和生产应用4个维度测度2003—2018年中国省际人工智能发展指数，并采用Dagum基尼系数和Kernel密度估计对其区域差异和动态演进进行进一步探究。结果表明，北京、广东、江苏、上海、浙江属于人工智能发展领跑者，山东、辽宁、天津、四川、重庆、湖南、湖北、陕西、福建属于追赶者，其余省份属于落后者；中国人工智能发展区域差异较大，其中，东—西、东—中、东—东北差异是发展差异的主要来源，东部地区内差异和西部地区内差异为次要来源；中国人工智能发展可以分为2003—2006年、2007—2010年、2011—2014年和2015—2018年4个阶段；考察期内，东部地区人工智能发展呈现出扩展效应，东北地区呈现出两极分化效应，中部地区扩展效应和回程效应相当，西部地区呈现出多极化效应。结论可为健全科技评价体系提供理论参考，为研究人工智能对经济社会的影响提供实证数据，为系统布局人工智能发展规划、构筑我国人工智能发展优势提供政策依据。