The impact of public regulation on housing quality and on structural changes

This paper represents a theoretical investigation of profit maximizing behavior of a landlord under rent control. The situation envisioned is one that the landlord owns, free and clear, either two housing units ready to merge or one large one ready to convert into two small ones; in each case the units are already in place, such that capital costs are sunk and treated as bygones. Each unit has a technology of production of housing services with a fixed input of quantity of space (shelter) and a variable input of quality, that is affected by physical ageing (non-controllable) and by maintenance (controllable). At starting time t₀, we have a state of quality and a historical state of initial quality Q?_i, at the time the building was built, with the implicit constraint that Q_i(t) < Q?_i, for all t> t₀. The analysis addresses the general question of housing structural changes—conversion or merger—and how these changes are being accelerated under the threat of rent control.     

Stochastic model specification search for Gaussian and partial non-Gaussian state space models

Model specification for state space models is a difficult task as one has to decide which components to include in the model and to specify whether these components are fixed or time-varying. To this aim a new model space MCMC method is developed in this paper. It is based on extending the Bayesian variable selection approach which is usually applied to variable selection in regression models to state space models. For non-Gaussian state space models stochastic model search MCMC makes use of auxiliary mixture sampling. We focus on structural time series models including seasonal components, trend or intervention. The method is applied to various well-known time series.     

A Fuzzy clustering approach to the key sectors of the Spanish economy

The search for key sectors in an economy has been and still is one of the more recurrent themes in input–output analysis. When using clustering techniques, sectors can only belong to a group, having a particular performance. But, actually, the same sector could be important from different perspectives at the same time, to a different degree. So, a fuzzy clustering approach is needed. In this work we propose a multidimensional approach to classify the productive sectors of the Spanish input–output table for 1995, based on three groups of variables: those related to their productive integration, others measuring their specific weight in the economy and finally some showing their economic dynamic. We also incorporate into the analysis the technological level, which being a categorical variable presents special methodological problems. All these questions are tackled applying a robust and fuzzy clustering analysis, which gives as a result a classification of sectors illustrating the role that each one plays in the Spanish economy.     

Do missing values exist? Incomplete data handling in cross-national longitudinal studies by means of continuous time modeling

In cross-national longitudinal studies it is often impossible to administer the same measurement instruments at the same occasions to all sample units in all participating countries. This quickly results in large quantities of missing data, due to (a) missing measurement instruments in some countries, (b) missing assessment waves within or across countries, (c) missing data for individual sample units. As compared to cross-sectional studies, the problem of missing values is further aggravated by the fact that missing values are always associated with different time intervals between repeated observations. In the past, this has often been dealt with by the use of phantom-variables, but this approach is limited to simple designs with few missing value patters. In the present paper we propose a new way to think of, and deal with, missing values in longitudinal studies. Instead of conceiving of a longitudinal study as a study with \(T\) discrete time points of which some are missing, we propose to conceive of a longitudinal study as a way to measure an underlying process that develops continuously over time, but is only observed at some selected discrete time points. This transforms the problem of missing values into a problem of unequal time intervals. After a quick introduction to the basic idea of continuous time modeling, we demonstrate how this approach provides a straightforward solution to missing measurement instruments in some countries, missing assessment waves within or across countries, and missing data for individual sample units.     

Certificate-of-need regulation and the diffusion of innovations: A random coefficient model

In this paper, we examine empirically the effect that certificate-of-need regulation by state health planning organizations has had on the speed of diffusion of a relatively new medical technology—haemodialysis. Specifically, we test the hypothesis that a requirement that investments be subject to certificate-of-need review has significantly slowed the rate of adoption of this particular treatment modality. In subjecting this hypothesis to empirical verification, we estimate a random coefficient model. This approach allows us to make more efficient use of the available data than the traditional two-stage approach to modelling diffusion processes wherein separate logistic functions are first estimated over the time series observations followed by hypothesis tests conducted over the cross-sectional observations. We find evidence that certificate-of-need regulation slows the spread of haemodialysis technology.     

Use of minimum risk approach in the estimation of regression models with missing observations

This article considers a linear regression model with some missing observations on the response variable and presents two estimators of regression coefficients employing the approach of minimum risk estimation. Small disturbance asymptotic properties of these estimators along with the traditional unbiased estimator are analyzed and conditions, that are easy to check in practice, for the superiority of one estimator over the other are derived. Received May 2001     

Cross-sectional surveys: Inferring total eventual time in current state using only elapsed time-to-date

We focus on snapshot surveying of sub-populations whose members are in a temporary state and where one of the questions asked is the elapsed time already spent in that state. From these answers we develop probabilistic and statistical procedures to estimate the distribution of total time that will eventually be spent in that state by any random individual who enters the state. The method relies on a selection bias often found in temporal sampling, sometimes called “random incidence” or “longevity bias.” We develop results for several types of sampling, including random and fixed times of surveying, random and fixed times of entering the state, and sampling only those who have already spent some minimal specified time in the targeted state. An example with post-doc data is included to demonstrate the steps.     

Time-series–cross-section Data

This article treats the analysis of 'time-series–cross-section' (TSCS) data. Such data consists of repeated observations on a series of fixed units. Examples of such data are annual observations on the political economy of OECD nations in the post-war era. TSCS data is distinguished from 'panel' data, in that asymptotics are in the number of repeated observations, not the number of units.
The article begins by treating the complications of TSCS data in an 'old-fashioned' manner, that is, as a nuisance which causes estimation difficulties. It claims that TSCS data should be analyzed via ordinary least squares with 'panel correct standard errors' rather than generalized least squares methods. Dynamics should be modeled via a lagged dependent variable or, if appropriate, a single equation error correction model.
The article then treats more modern issues, in particular, the modeling of spatial effects and heterogeneity. It also claims that heterogeneity should be assessed with 'panel cross-validation' as well as more standard tests. The article concludes with a discussion of estimation in the presence of a binary dependent variable.     

LABOUR DEMAND IN GERMANY BY INDUSTRIAL SECTOR,OCCUPATIONAL FIELD AND QUALIFICATION UNTIL 2025 – MODEL CALCULATIONS USING THE IAB/INFORGE MODEL

By means of a trend extrapolation of microcensus structures (undertaken by the German Federal Statistical Office) for the time period 1996–2007, the projections for labour demand by industrial sector which the IAB already has at its disposal can be transferred to demand by occupational field and subsequently by qualification level until 2025. The findings which have been claimed for some time now are upheld: production-related occupations will lose in significance, while further increases in employment particularly in occupations in the service sector are to be expected. Accordingly, the demand for personnel with a degree from a university or a university of applied sciences will go on rising, while the labour market opportunities for unskilled workers will continue to fall. However, vocational training or its academic counterparts still remain the dominant form of training in Germany. A continuing employment trend is to be expected here.     

Neyman–Pearson Lemma for Fuzzy Hypotheses Testing with Vague Data

In hypotheses testing, such as other statistical problems, we may confront imprecise concepts. One case is a situation in which both hypotheses and observations are imprecise. This paper tries to develop a new approach for testing fuzzy hypothesis when the available data are fuzzy, too. First, some definitions are provided, such as: fuzzy sample space, fuzzy-valued random sample, and fuzzy-valued random variable. Then, the problem of fuzzy hypothesis testing with vague data is formulated. Finally, we state and prove a generalized Neyman–Pearson Lemma for such problem. The proposed approach is illustrated by some numerical examples.     

The inverse operationalisation of concepts for the secondary analysis of quantitative data: an example from the study of parental collaboration

Properly validated scalar variables are often viewed as the gold standard for the operationalisation of concepts in quantitative data. This is a sensible approach at the planning stage of the survey process. However, when working with data that has already been collected for another purpose, such variables cannot always be expected. This is particularly the case when one wishes to analyse a concept that has not previously been studied in a particular context. This paper provides an example of the construction of a binary variable for the concept of parental collaboration, using data from the Growing Up in Scotland study. It examines the decision-making process for the “inverse operationalisation” of the concept, an innovative method which starts with the assumption that all cases in the dataset demonstrate a particular property (parental collaboration), and gradually chips away at those which provide sufficient evidence to suggest otherwise, until a working variable is created.     

Factors affecting entrepreneurial intention levels: a role for education

A considerable agreement exists about the importance of promoting entrepreneurship to stimulate economic development and employment generation. In particular, entrepreneurship education has been considered one of the key instruments to increase the entrepreneurial attitudes of both potential and nascent entrepreneurs. Nevertheless, the factors that determine the individual’s decision to start a venture are still not completely clear. Cognitive approaches have attracted considerable interest recently. But the explaining capacity of personality traits or demographic characteristics is still considered. Therefore, there is a need to clarify which elements play the most influential role in shaping the personal decision to start a firm. This paper tries to contribute to filling this gap by providing empirically-based suggestions for the design of improved entrepreneurship education initiatives. The empirical analysis is based on two essential elements: firstly, an already validated instrument (EIQ); secondly, a statistical method (factor-regression procedure) which is not dependent on any theoretical approach. It uses all the information collected through the questionnaire items, selecting them solely based on their capacity to explain the dependent variable. Results will allow the design of more effective education initiatives. They suggest that personal attitude and perceived behavioural control are the most relevant factors explaining entrepreneurial intentions. Thus, based on these results, a number of considerations about the most effective role of education in promoting and developing attitudes and intentions towards entrepreneurship are considered. Besides, the EIQ could be used as an evaluation instrument for entrepreneurial education programmes.     

Variable pricing business solutions in a decomposed business environment

Evolving business models and technology advances have facilitated the creation of innovative pricing strategies. Variable pricing represents the ability to configure a pricing schedule from a set of pricing options such as fixed cost, usage, shared benefit, and performance. The objective of variable pricing is to improve a pricing schedule for the mutual benefits of the provider and consumer, based on an evaluation of criteria that results in the setting of a price as a function of the expected value to be derived, as well as the time and materials used. In this paper, we focus on the variable pricing of ‘business solutions’, which is abstractly defined as the capabilities that enable or add value to the purposes of an enterprise. In a decomposed business environment, the structure of a business is partitioned into discrete business components that are assigned specific purposes and are endowed with resources to meet them. Business components interact to achieve business goals, and do so by exposing their capabilities through business services they offer. Business services have suitable levels of granularity offering constituent units of function, which, when selectively chosen and composed, form business solutions. We assert that business services are also suitable units for variable pricing, the implication being that pricing for a given business solution is an evaluation of the variable pricing of its assemblage of business services. The benefits of this ‘variable price composition’ approach offer greater accuracy for the pricing plan, coupled with increased flexibility to compose, modify, calculate and articulate pricing for business solutions.     

Migration and public policies: a further empirical analysis

This study of internal migration at the state level empirically investigates the Tiebout hypothesis (as extended by Tullock) of “voting with one’s feet.” In addition to its adoption of more current data (net migration from July, 2000 through July, 2008) than other related studies to date, the model differs from most previous comparable studies by including a separate cost of living variable and a measure of per capita state income tax burdens. We also test the hypothesis using two alternative specifications: one linear and the other semi-log. Finally, the analysis also provides both OLS and 2SLS estimates. The advantage of this multi-faceted approach is that it permits an assessment of how sensitive the results are to specification changes and to different estimation procedures. After controlling for economic factors and a quality of life/climate variable, migrants (consumer-voters) appear to prefer lower state income tax burdens, lower state plus local property tax burdens, and higher per pupil outlays on primary and secondary public education.     

Many-valued Logic in Multistate and Vague Stochastic Systems

The state of the art in coherent structure theory is driven by two assertions, both of which are limiting: (1) all units of a system can exist in one of two states, failed or functioning; and (2) at any point in time, each unit can exist in only one of the above states. In actuality, units can exist in more than two states, and it is possible that a unit can simultaneously exist in more than one state. This latter feature is a consequence of the view that it may not be possible to precisely define the subsets of a set of states; such subsets are called vague . The first limitation has been addressed via work labeled 'multistate systems'; however, this work has not capitalized on the mathematics of many-valued propositions in logic. Here, we invoke its truth tables to define the structure function of multistate systems and then harness our results in the context of vagueness. A key contribution of this paper is to argue that many-valued logic is a common platform for studying both multistate and vague systems but, to do so, it is necessary to lean on several principles of statistical inference.     

汉化“关系营销”中的多期动态博弈机制分析

关系营销作为当前国内外市场营销理论界的一大热点,其理念内核虽然已经十分明了,但是其理论依据和研究方法则由于专家们观点出发不同,至今仍然莫衷一是。本文作者试图利用博弈论中多期动态博弈的观点,推出关系营销的一种有效的理论假设,并通过探讨我国传统模式关系营销实践的理性机制,分析其中的所谓"中国特色"的社会本质和理性依据,希望能将此理论体系真正用于我国的社会主义市场,为当前的经济建设服务。     

How should a local regime-switching model be calibrated?

Local regime-switching models are a natural consequence of combining the concept of a local volatility model with that of a regime-switching model. However, even though Elliott et al. (2015) have derived a Dupire formula for a local regime-switching model, its calibration still remains a challenge, primarily due to the fact that the derived volatility function for each state involves all the state price variables whereas only one market price is available for model calibration, and a direct implementation of Elliott et al.’s formula may not yield stable results. In this paper, a closed system for option pricing and data extraction under the classical regime-switching model is proposed with a special approach, splitting one market price into two “market-implied state prices”. The success of our approach hinges on the recovery of the two local volatility functions being transformed into an optimal control problem, which is solved through the Tikhonov regularization. In addition, an efficient algorithm is proposed to obtain the optimal solution by iteration. Our numerical experiments show that different shapes of local volatility functions can be accurately and stably recovered with the newly-proposed algorithm, and this algorithm also works quite well with real market data.     

Nonparametric discrimination of time series data

A normality assumption is usually made for the discrimination between two stationary time series processes. A nonparametric approach is desirable whenever there is doubt concerning the validity of this normality assumption. In this paper a nonparametric approach is suggested based on kernel density estimation firstly on (p+1) sample autocorrelations and secondly on (p+1) consecutive observations. A numerical comparison is made between Fishers linear discrimination based on sample autocorrelations and kernel density discrimination for AR and MA processes with and without Gaussian noise. The methods are applied to some seismological data.     

Combining random sampling and census strategies - Justification of inclusion probabilities equal to 1

Very often values of a size variable are known for the elements of a population we want to sample. For example, the elements may be clusters, the size variable denoting the number of units in a cluster. Then, it is quite usual to base the selection of elements on inclusion probabilities which are proportionate to the size values. To estimate the total of all values of an unknown variable for the units in the population of interest (i.e. for the units contained in the clusters) we may use weights, e.g. inverse inclusion probabilities. We want to clarify these ideas by the minimax principle. Especially, we will show that the use of inclusion probabilities equal to 1 is recommendable for units with high values of the size measure. AMS Classification 2000: Primary 62D05. Secondary 62C20