Real Economic Data Research Articles

Cost-effectiveness analysis of stroke volume variation guided perioperative hemodynamic optimization

Perioperative goal-directed therapy (pGDT) can substantially improve the outcome of high-risk surgical patients [1]. But the approach needs an initial investment and increases the staff workload. Economic factors might participate in the weak adherence to the pGDT concept. Some model studies support pGDT cost-effectiveness, but real economic data based on a recent clinical trial are lacking. We performed an economic analysis of hemodynamic optimization using the stroke volume variation trial [2] in order to elucidate this issue.

An economic and financial exploratory

This paper describes the vision of a European Exploratory for economics and finance using an interdisciplinary consortium of economists, natural scientists, computer scientists and engineers, who will combine their expertise to address the enormous challenges of the 21st century. This Academic Public facility is intended for economic modelling, investigating all aspects of risk and stability, improving financial technology, and evaluating proposed regulatory and taxation changes. The European Exploratory for economics and finance will be constituted as a network of infrastructure, observatories, data repositories, services and facilities and will foster the creation of a new cross-disciplinary research community of social scientists, complexity scientists and computing (ICT) scientists to collaborate in investigating major issues in economics and finance. It is also considered a cradle for training and collaboration with the private sector to spur spin-offs and job creations in Europe in the finance and economic sectors. The Exploratory will allow Social Scientists and Regulators as well as Policy Makers and the private sector to conduct realistic investigations with real economic, financial and social data. The Exploratory will (i) continuously monitor and evaluate the status of the economies of countries in their various components, (ii) use, extend and develop a large variety of methods including data mining, process mining, computational and artificial intelligence and every other computer and complex science techniques coupled with economic theory and econometric, and (iii) provide the framework and infrastructure to perform what-if analysis, scenario evaluations and computational, laboratory, field and web experiments to inform decision makers and help develop innovative policy, market and regulation designs.

Uno studio sulle presidenziali americane: l'economia conta?

The economy matters. This is one of the dogmas taught us by those who have studied the history of the American presidential elections. But is this collective perception of the influence of economic data on individual electoral behaviour confirmed by statistical analysis of the history of presidential elections? This study shows that the most commonly used economic indicators are poor predictors of election outcomes, and also that variation in the approval rating of the incumbent president is a variable independent from the real economic data.

Using stochastic prior information in consistent estimation of regression coefficients in replicated measurement error model

A replicated ultrastructural measurement error regression model is considered where both predictor and response variables are observed with error. Availability of some prior information regarding regression coefficients in the form of stochastic linear restrictions is assumed. Using this prior information, three classes of consistent estimators of regression coefficients are proposed. A two-stage procedure is discussed to obtain feasible version of these Stochastically Restricted estimators. The asymptotic properties of the proposed estimators are studied. No distributional assumption is imposed on any random component of the model. Monte Carlo simulations study is performed to assess the effect of sample size, replicates and non-normality on the estimators. The methods are illustrated using real economic data.

A Note on the Predicted Responses in Cubic Splines

One of the most used statistical methods in economic and business studies involving time series is related to predicted responses (ỹ values), which can be estimated with two different approaches, namely cubic spline regression method (CSR) and prediction sum of squares statistic (PRESS). This study aims to set and discuss the relation between these two approaches in estimation of predicted responses. In first approach estimated ỹ values are determined from the derived restricted model. According to the second approach, they are estimated with prediction sum of squares statistic (PRESS), and it argues that the use of this technique performs is better for cubic spline regression method (CSR). This study while introducing and discussing the relation between these approaches, also addresses to note the estimation of predicted ỹ values theoretically. The study concludes that same predicted responses can be received by employing both methods. For examining and testing this argument empirically real exchange rates data for Turkey in the period of 1987-2008 are used. Additionally another subject searched and discussed in the study was, how economic crises can be defined with spline methods. Because of the advantages provided by them in reaching minimum residual sum of squares, and achieving the result by using real economic data without changing their nature in time series analysis.

Testing population variance in case of one sample and the difference of variances in case of two samples: Example of wage and pension data sets in Serbia

Testing one population variance and the difference in variances of two populations based on the ordinary t-statistics combined with the bootstrap method are suggested in this article. Suggested techniques are combined with Hall's transformation approach. Application of presented methods in domain of real economic data set is described and analyzed. We compare the outputs of suggested methods and traditional methods for considered data set. The results show that these introduced methods have small advantages in comparisons with traditional methods especially for small samples.

Why Do Social Democrats Retrench the Welfare State? A Simulation

Why do social democrats retrench the welfare state? Next to violating core social democratic principles, recent studies show that social democratic parties - and not so much other parties - are systematically punished after retrenchment by (severe) vote losses. We propose and show that the strategy a party employs - policy-seeking, office-seeking or vote-seeking - is crucial for explaining why and when social democrats retrench, because it influences how they respond to environmental incentives (economy, public opinion, activist opinion and opposition strategy). The intra-party balance of power determines which strategy a party employs. We simulate an agent-based model to examine the implications of the different party strategies for the behavior of social democrats regarding welfare state retrenchment. This model includes 1) agents (here: parties) with specific decision heuristics that operationalize the different strategies, 2) voters who use a proximity-rule and 3) a coalition formation model. To mimic reality as much as possible, we calibrate the model with real economic data, whereby these data affect voters' policy preferences. We find that social democrats with a vote-seeking strategy retrench when inflation pushes the mean voter to the right. Office-seeking social democrats behave similarly, but respond with a strong time lag to the economy and to public opinion. Policy-seeking social democrats do not retrench. Moreover, we find that when faced with a radical left-wing competitor, social democrats shift towards the centre - irrespective of their strategy -, increasing the likelihood that social democrats retrench. An empirical illustration of the behavior of five social democratic parties corroborates the simulations' results. To the welfare state literature, we offer a dynamic model that predicts under which conditions social democrats engage in welfare state retrenchment. To the literature on simulations, we offer an application of a model that makes predictions for a specific policy field, using real economic data and party decision rules.

Economic/Ecological Tradeoffs Among Ecosystem Services and Biodiversity Conservation

An integrated economic/ecological model is developed to address tradeoffs between biodiversity conservation and two marketable rangeland ecosystem services: cattle grazing and elk hunting. The ecology is represented by an eleven species food web in which individual optimizing plants and animals engage in competitive and predator/prey relationships. A manager maximizes social welfare based on grazing profits, hunter net benefits, and conserving biodiversity in a natural ecosystem. The ecological model defines a steady-state set of sustainable grazing and hunting choices, but biodiversity conservation may reduce the feasible set. In a numerical application with real economic and biological data from the Western United States, the ecologically sustainable set constrains the short term economic optimum.

Modelling of biomass prices for bio-energy market in the Czech Republic

The article combines theoretical approaches in economic modelling with practical application of created models for the bio-energy sector in the defined area. Design and application of economic models is discussed to predict future biomass prices from new energy crops on the developed and saturated bio-energy market. The models use minimum price methodology based on application of elementary principles of economic theory to commercial activity with undeveloped production and/or the demand side of the biomass market. The model is then tested on real economic and agronomic data from pilot and experimental biomass production and utilization projects in the Czech Republic with a case study of biomass from poplar and willow short rotation coppice (SRC) and reed canary grass plantations. Selected results of this modelling of biomass prices are presented and discussed. The economic model created proved to be a practical tool for analysis of the effectiveness and competitiveness of biomass from different energy crops on the energy market, which can be used for strategic planning on the part of state administration or decision-making of companies in the biomass sector.

Prediction Using Distributed Lagged Subset Model

This article examines the problem of determining the future value of the dependent variable in the distributed lagged subset model. Unlike a distributed lag model in general, which assumes that all coefficients are not zero. In a distributed lagged subset model, some coefficients may be zero. The purpose of this study was to determine the predictive value of the dependent variable in a distributed lagged subset model. The approach used to estimate the parameters of a distributed lagged subset model is the least square method and Ck statistic. Least squares method is used to determine the estimators of the coefficient of a distributed lagged subset model. Ck Statistic is used to select the best distributed lagged subset model. Some simulations are delivered and prove the efficiency of this approach. Furthermore, this approach is implemented in real economic data. Keywords : Distributed lagged subset model, Prediction, Least square method, Ck Statistic.

Market evaluations of dimensions of design quality

After a literature review on the economics of quality, we combine the value creation logic with the hedonic price function to analyze dimensions of design quality in enterprises. Viewing the quality-based organization as a collection of processes generating quality attributes and characteristics that constitute a solution for customers, we estimate market value as a measure of process economic performance based on objectively measured real economic data. The model can help to identify the relationship between design quality and market evaluations and to manage the quality of product and service at supply chain level.

Agent-based input–output interdependency model

The modeling and analysis of critical infrastructures and their interdependencies are essential to discovering hidden vulnerabilities and the related threats to national and international security. Over the past few years, several approaches have been proposed to address this problem. The so-called holistic approaches are relatively abstract, but are easily validated using real economic data. Other approaches based on agent-based models provide deeper views of the interdependencies existing between subsystems of different infrastructures. However, agent-based models are often difficult to validate because quantitative data of the appropriate granularity may not be available. This paper presents an agent-based input–output inoperability model designed to overcome the limitations of the holistic and agent-based paradigms. In order to provide a detailed and expressive framework, the exchange of resources between infrastructures is explicitly modeled while inoperability becomes an internal parameter. Nevertheless, the model is easily transformed into a fine-grained, input–output inoperability model whose coefficients can be obtained based on real data.

The difference of growth rate distributions between sales and profits

Using numerical simulations, the authors exhibit the difference between two types of the growth rate distributions, the one of which is observed in both positive and negative data such as profits, and the other of which is in non-negative data such as sales. In the simulation, firstly the Langevin equation generates both positive and negative variables, the growth rate distributions of which are linear functions of the logarithmic growth rate. By superposing the variables not to be negative, we find that the growth rate distributions of the non-negative variables have wider tails than line shape on a log-log scale. At the same time, two types of Non-Gibrat's Laws in the middle scale range are also confirmed as observed in real economic data.

A NOTE ON THE FORMAL TREATMENT OF EXPLOITATION IN A MODEL WITH HETEROGENOUS LABOR

ABSTRACTThe paper presents a formal generalization of Marx's analysis of exploitation and prices in economies in which categories of labor are distinguished according to their unequal ability to create exchange value. Measures of exploitation of categories of labor which are consistent with both the Price of Net Product‐Unallocated Purchasing Power (PNP‐UPP) (New Interpretation) and the traditional interpretation of Marx's theory of value are defined which aggregate consistently over labor categories and sectors. In order to measure rates of exploitation from real economic data some additional assumption about relative rates of exploitation (which Marx often explicitly assumes to be equal) is required.

Genetic hybrid tuning of VARMAX and state space algorithms

The aim of the study was to monitor the system theoretic exogenous variables augmented state space algorithm of Aoki (State space modelling of time series. Springer, Heidelberg, 1987) and the VARMAX algorithm of Spliid (J Am Stat Assoc 78(384):843–849, 1983) within a geno-mathematical framework towards optimal parametric conditions/search intervals. Both algorithms were implemented as an integrated support library for a general computational platform, the Genetic Hybrid Algorithm (GHA), where some key parameters of the algorithms are defined in a search process utilizing a mixed geno-mathematical search technique. The empirical results of our tests using real economic data from the European stock market are encouraging. Specifically, the information criteria used in the VARMAX-search (Vector Autoregressive Moving Average algorithm with Exogenous variables) algorithm tend to favor parsimonious model representations automatically. Furthermore, the state space algorithm captures almost the same dynamics as the complex VARMAX-model estimated in the study. Both algorithms have encouraging in sample properties. When generating k-steps forecasts out-of-sample, k > 1, the state space algorithm seems to deteriorate faster than the VARMAX algorithm, however. The results suggest that more empirical testing is needed, especially in different situations with different degrees of model order and stationarity conditions, in order to provide more evidence on the suitability of the competing methods in particular cases. We demonstrated that the Genetic Hybrid Algorithm can be used as a generic platform for parametric search in vector valued time series modelling. Efficient procedures for optimal grouping of the individual time series processes and recognition of heteroskedasticity may improve the performance of the algorithms further.

Empirical-Likelihood-Based Difference-in-Differences Estimators

SummaryRecently there has been a surge in econometric and epidemiologic works focusing on estimating average treatment effects under various sets of assumptions. Estimation of average treatment effects in observational studies often requires adjustment for differences in pretreatment variables. Rosenbaum and Rubin have proposed the propensity score method for estimating the average treatment effect by adjusting pretreatment variables. In this paper, the empirical likelihood method is used to estimate average treatment effects on the treated under the difference-in-differences framework. The advantage of this approach is that the common marginal covariate information can be incorporated naturally to enhance the estimation of average treatment effects. Compared with other approaches in the literature, the method proposed can provide more efficient estimation. A simulation study and a real economic data analysis are presented.

Nonparametric estimation under length-biased sampling and Type I censoring: A moment based approach

Observation of lifetimes by means of cross-sectional surveys typically results in left-truncated, right-censored data. In some applications, it may be assumed that the truncation variable is uniformly distributed on some time interval, leading to the so-called length-biased sampling. This information is relevant, since it allows for more efficient estimation of survival and related parameters. In this work we introduce and analyze new empirical methods in the referred scenario, when the sampled lifetimes are at risk of Type I censoring from the right. We illustrate the method with real economic data.

Using the Nobel Laureates in Economics to Teach Quantitative Methods

Undergraduate students typically are able to regurgitate basic rules and formulas for probability. They also have little trouble following the cookbook steps for estimation and hypothesis testing. Microsoft Excel has empowered them to run regressions. We are not persuaded, however, that these advances have markedly improved student understanding of the underlying principles of statistics. Nor are we convinced that advances made by scholars working with quantitative methods and real economic data are making their way into classrooms and computer laboratories. In this piece, we show how the work of Nobel Laureates in economics can be used to enhance student understanding and bring students up to date on topics such as probability, uncertainty and decision theory, hypothesis testing, regression to the mean, instrumental variable techniques, discrete choice modeling and time-series analysis.

Plug-in bandwidth selector for local polynomial regression estimator with correlated errors

Consider the fixed regression model where the error random variables are coming from a strictly stationary, non-white noise stochastic process. In a situation like this, automated bandwidth selection methods for non-parametric regression break down. We present a plug-in method for choosing the smoothing parameter for local least squares estimators of the regression function. The method takes the presence of correlated errors explicitly into account through a parametric correlation function specification. The theoretical performance for the local linear estimator of the regression function is obtained in the case of an AR(1) correlation function. These results can readily be extended to other settings, such as different parametric specifications of the correlation function, derivative estimation and multiple non-parametric regression. Estimators of regression functionals and the error correlation based on local least squares ideas are developed in this article. A simulation study and an analysis with real economic data illustrate the selection method proposed.

LOCAL POLYNOMIAL REGRESSION ESTIMATION WITH CORRELATED ERRORS

In this paper, we study the nonparametric estimation of the regression function and its derivatives using weighted local polynomial fitting. Consider the fixed regression model and suppose that the random observation error is coming from a strictly stationary stochastic process. Expressions for the bias and the variance array of the estimators of the regression function and its derivatives are obtained and joint asymptotic normality is established. The influence of the dependence of the data is observed in the expression of the variance. We also propose a variable bandwidth selection procedure. A simulation study and an analysis with real economic data illustrate the proposed selection method.