基于灰色关联分析的施工方案优选研究

施工方案的选择不仅直接影响项目利润的大小，更会关系到项目的成功与失败。本文在充分考虑施工方案影响因素的基础上，选择施工方案评价指标，利用层次分析法确定各评价指标的权重。建立灰色关联分析模型，构造理想方案。在分析各拟选施工方案与理想方案关联度的基础上确定最优方案，并结合实例进行了阐述。     

The Dynamics of Location in Home Price

It is well established that house prices are dynamic. It is also axiomatic that location influences such selling prices, motivating our objective of incorporating spatial information in explaining the evolution of house prices over time. In this paper, we propose a rich class of spatio-temporal models under which each property is point referenced and its associated selling price modeled through a collection of temporally indexed spatial processes. Such modeling includes and extends all house price index models currently in the literature, and furthermore permits distinction between the effects of time and location. We study single family residential sales in two distinct submarkets of a metropolitan area and further categorize the data into single- and multiple-transaction observations. We find the spatial component is very important in explaining house price. Moreover, the relative homogeneity of homes within the submarket and the frequency with which homes sell affects the pattern of variation across space and time. Differences between single and repeat sale data are evident. The methodology is applicable to more general capital asset pricing when location is anticipated to be influential.     

Pricing default swaps: Empirical evidence

In this paper we compare market prices of credit default swaps with model prices. We show that a simple reduced form model outperforms directly comparing bonds' credit spreads to default swap premiums. We find that the model yields unbiased premium estimates for default swaps on investment grade issuers, but only if we use swap or repo rates as proxy for default-free interest rates. This indicates that the government curve is no longer seen as the reference default-free curve. We also show that the model is relatively insensitive to the value of the assumed recovery rate.     

Financial Stress Regimes and the Macroeconomy

Some financial stress events lead to macroeconomic downturns, while others appear to be isolated to financial markets. We identify financial stress regimes using a model that explicitly links financial variables to macro‐economic outcomes. The stress regimes are identified using an unbalanced panel of financial variables with an embedded method for variable selection. Our identified stress regimes are associated with corporate credit tightening and with NBER recessions. An exogenous deterioration in our financial conditions index has strong negative effects in economic activity, and negative amplification effects on inflation in the stress regime. These results are obtained with a novel factor‐augmented vector autoregressive model with smooth‐transition regimes (FASTVAR).     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

Using clustering ensemble to identify banking business models

The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.     

International commitment,post-entry growth and survival of international new ventures

This paper makes several contributions to the emerging literature on the post-entry behavior of international new ventures. Based on an extensive longitudinal data set, we investigate the dynamics of commitment, growth and survival of different types of newly internationalizing Belgian firms. Global start-ups have the highest initial and rapidly rising export commitment per market and are also more likely to continue exporting over time than geographically focused start-ups, and traditional staged exporters. However, global start-ups also display the highest failure rate. This high failure rate appears to result primarily from the ‘liability of newness’ and less from the added complexity associated with rapid and wide scope internationalization.     

An Empirical Investigation of Composite Product Choice

Prior ingredient branding research has examined the influence of “stated” factors such as fit between partner brands on composite product (e.g., Tide with Downy fabric softener) attitudes. This research focuses on choice of composite products, and addresses three managerially relevant questions: Which consumer segments are more likely to adopt the composite product? Will the choice of the composite product have positive or negative reciprocal effects on partner brands? Will the introduction of the composite product benefit the primary or the secondary brand more? The authors use a brand choice model to investigate the “revealed” choice of complements-based composite products. Study results indicate that (i) despite high fit between the composite product and the primary brand, consumer segments may have different choice likelihoods for these products, whereas prior research suggests equal likelihood; (ii) the choice of a composite product may not provide a positive reciprocal effect to the secondary brand; and (iii) the introduction of a composite product may benefit the primary brand more than the secondary brand, whereas prior research suggests a symmetrical benefit for the partner brands. Finally, the finding that introducing a composite product may not cannibalize the sale of the primary brand extends the ingredient branding literature, which has been silent on this issue.     

经济学视角下自然保护区有效管理行为研究——以甘肃白水江自然保护区李子坝社区为例

以甘肃白水江自然保护区李子坝社区巡护监测为例,从经济学的视角,运用比较优势和竞合模型分析该社区巡护监测与专业巡护监测的两种行为。模型分析显示两者各有优势,在专业巡护监测的基础上,如果辅之以社区巡护监测能更有效地促进环境与生物的多样性的保护,既可以提升有效管理的水平,又可以达到社区保护利益的最大化。     

Numerical Maximisation of Likelihood: A Neglected Alternative to EM?

There is by now a long tradition of using the EM algorithm to find maximum‐likelihood estimates (MLEs) when the data are incomplete in any of a wide range of ways, even when the observed‐data likelihood can easily be evaluated and numerical maximisation of that likelihood is available as a conceptually simple route to the MLEs. It is rare in the literature to see numerical maximisation employed if EM is possible. But with excellent general‐purpose numerical optimisers now available free, there is no longer any reason, as a matter of course, to avoid direct numerical maximisation of likelihood. In this tutorial, I present seven examples of models in which numerical maximisation of likelihood appears to have some advantages over the use of EM as a route to MLEs. The mathematical and coding effort is minimal, as there is no need to derive and code the E and M steps, only a likelihood evaluator. In all the examples, the unconstrained optimiser nlm available in R is used, and transformations are used to impose constraints on parameters. I suggest therefore that the following question be asked of proposed new applications of EM: Can the MLEs be found more simply and directly by using a general‐purpose numerical optimiser?