Real Economic Data Research Articles

Comparing permutation entropy functions to detect structural changes in time series

Entropy can be taken as a measure of the complex dynamical behavior. In this paper, we consider different entropy functions and the permutation symbolic dynamics and we apply them to find structural changes in time series. We analyze what entropy functions are more suitable to show changes in simulated time series where the structural changes are know. Applications to seismic real data and economic data series are shown to illustrate how this type of tools can be used.

La desaprobación del Gobierno mexicano. Más que ineficiencia

This paper puts to test the hypothesis that Mexican citizens are dissatisfied with its government because it has not fulfilled expectations, mainly in regard to economy and security, since political discontent with Mexican government has become a problem for democracy in this country. Using data from the Latinobarometro, it was found that those with worse opinions of economy and security are actually those who held the worse opinion of the government. However, this relationship did not appear when real economic and security data were considered instead of the perception of these, so qualitative interviews were conducted, and it was found that citizens have a moral expectation of their officials that seems to determine their opinions and expectations. This moral demand doesn’t appear to be related to pragmatic government performance.

On a new methodology for ranking fuzzy numbers and its application to real economic data

Ranking fuzzy numbers plays an important role in the decision process. Several strategies can be found in the abundant literature existing on this topic, which can be basically classified into two main groups: approaches based on the so-called ranking indices and based on fuzzy binary relations. Ranking indices are functions from fuzzy numbers to real values. These defuzzification methods are critiqued by its implicit information loss which leads to results that might not be consistent with human intuition in certain cases. In a fuzzy context, approaches based on binary relations can be considered more suitable solutions. Following this strategy, this paper introduces a new technique for ranking fuzzy numbers which is applicable to the whole set of fuzzy numbers. It is proved that many desired properties (in particular, we give several new properties) are satisfied and numerical examples show reasonable results. The developed technique is very easy to compute and interpret in practice, and it overcomes certain shortcomings that appear when applying other more complex algorithms. In the case of triangular or trapezoidal fuzzy numbers, the procedure is particularly simple and intuitive. Finally, a real-life example involving data of Chinese Economy is also carried out.

Sale of profitable but unaffordable PV plants in Spain: Analysis of a real case

The Spanish photovoltaic industry was stunningly successful during 2007–2010, fostered by a favourable feed-in tariff system. Nevertheless, the cost overrun of this promotion policy led to government legislation against existing PV plants. Although these investments will be profitable when the subsidy ends, according to the last law enacted in Spain (IRR = 7.4%), either a massive sale to vulture funds or the abandonment of PV plants is being planned. Owners are unable to cover the loans through which they were originally financed. In this scenario, investors find it more profitable to cancel all operational expenditures and allocate this working capital to cover their loans, although this measure implies a 22% energy reduction.This study analyses a representative Spanish PV plant based on real energy and economic data. The analysis shows the turn from an attractive IRR = 10.14% to a situation with limitations where the owner injects money annually to overcome potential bankruptcy of the investment. This paper reflects the influence of promotion policies in the profitability of PV plants. Additionally, the adverse legal framework in Spain identifies a profitable but unaffordable scenario, highlighting the differences between the economic and financial performance of a PV investment.

Network reconstruction via density sampling

Reconstructing weighted networks from partial information is necessary in many important circumstances, e.g. for a correct estimation of systemic risk. It has been shown that, in order to achieve an accurate reconstruction, it is crucial to reliably replicate the empirical degree sequence, which is however unknown in many realistic situations. More recently, it has been found that the knowledge of the degree sequence can be replaced by the knowledge of the strength sequence, which is typically accessible, complemented by that of the total number of links, thus considerably relaxing the observational requirements. Here we further relax these requirements and devise a procedure valid when even the the total number of links is unavailable. We assume that, apart from the heterogeneity induced by the degree sequence itself, the network is homogeneous, so that its (global) link density can be estimated by sampling subsets of nodes with representative density. We show that the best way of sampling nodes is the random selection scheme, any other procedure being biased towards unrealistically large, or small, link densities. We then introduce our core technique for reconstructing both the topology and the link weights of the unknown network in detail. When tested on real economic and financial data sets, our method achieves a remarkable accuracy and is very robust with respect to the sampled subsets, thus representing a reliable practical tool whenever the available topological information is restricted to small portions of nodes.

Internet Service Providers vs. Over-the-Top Companies

The recent appearance of Over-the-Top (OTT) providers, who offer similar services (e.g., voice and messaging) to those of the existing Internet Service Providers (ISPs), was the main reason for a long-standing conversation with regard to the network neutrality, i.e., the prioritization of different types of data in the network. In particular, ISPs oppose network neutrality, claiming that OTT companies: (i) have conflicting interests and provide competitive services, thus constituting a threat to their own growth, and (ii) distort incentives for investment, as they essentially exploit the network already deployed by ISPs, acting as free riders. The importance of the net neutrality debate has motivated the research community to study the interaction among the different tenants from a theoretical point of view [1,2]. Despite the interesting theoretical conclusions of the existing works, an empirical econometric study on the interaction of the new stakeholders was not possible hitherto, as the main evolution of the OTT companies took place at the end of the last decade and, therefore, real economic data from the actual progress of these firms were not available until recently. In this article, we provide a detailed econometric study to analyze the relationship between the OTT companies and the ISPs. The empirical analysis has been conducted for seven countries in the period 2008-2013, considering ten major ISPs and three OTT companies that offer communication services (i.e., Skype, Facebook and WhatsApp), while we focus on five different parameters: (i) the revenues of the ISPs, (ii) the revenues of the OTT providers, (iii) the Capital Expenditure (CAPEX) of the ISPs, (iv) the Internet penetration, and (v) the real Gross Domestic Product (GDP) that determines the economic performance of each country. For the analysis of our cross-sectional time series (countries and year) panel data, we propose two econometric models (based on the fixed effects model) with two different dependent variables: (i) Model A with the ISP revenues as the dependent variable and (ii) Model B with the OTT revenues as the dependent variable. The interpretation of the results of Model A reveals two very intriguing insights. First, we see that the revenues of the ISPs and the OTT companies are positively correlated with a particular coefficient of 9.81, i.e., the increase of one unit (e.g., USD) in the revenue of the OTT providers causes an average increase of approximately ten units in the revenues of ISPs. Second, the CAPEX of the ISPs has also a positive effect in their revenue with a coefficient of 3.21. The positive correlation between the revenues of the OTT companies and the ISPs is also verified in Model B with a coefficient of 0.03, which implies that the growth of ISPs has a positive (although small) impact on the growth of OTT providers. However, the most important conclusion that can be extracted by Model B is the negative impact that the CAPEX has on the OTT profits. More specifically, the revenue of the OTT companies is reduced by 0.13 units for every unit that the ISPs invest on the network infrastructure. The observations of our empirical analysis are very important, as they provide tangible arguments and answers to the claims of the net neutrality opponents. In particular, our study has shown that the economic prosperity of the OTT firms has a positive influence in the financial performance of the ISPs. Consequently, it can be concluded that these two important stakeholders fruitfully coexist in the telecommunications and Internet market and they should probably work more closely together to achieve a mutually profitable cooperation. In addition, our empirical results have also demonstrated that the network investments have a positive effect on the ISPs revenue and a negative impact on the revenue of the OTT providers, thus refuting the accusations towards OTT companies for free riding. Finally, although not exhaustive, our study stresses the need for additional similar studies that will further clarify the interaction among the different entities in the evolving Internet ecosystem.

Energy statistics of pre-revolutionary Russia

The article is devoted to the problem of creation and development of the energy statistics of the Russian Empire of the initial stage of electrification and the formation of the energy economy, which is related to: 1) the economic upsurge of the 1890s; 2) the new economic recovery of 1907–1913 and 3) the militarization of industry in 1914–1916. The real technical and economic indicators and complex statistical data of the pre-revolutionary electric power industry were either hushed up or deliberately distorted during the Soviet era. Even in the encyclopaedic literature it was considered that pre-revolutionaryRussia“was on one of the last places in the world” for the production of electricity. The author analyzes statistical surveys (censuses) of the manufacturing industry for 1900 and 1908 (the “varzar censuses”), which gave the first material on the state of its energy sector, namely: the composition, quantity and power of primary engines and electric motors. For the first time in historiography, the data of the “energy censuses” of the Ministry of Finance for 1905 and 1913 on the number and capacity of central public power stations and private power stations (block stations) of industrial enterprises, organizations and institutions are cited. The data of the census were conducted with the participation of the apparatus of factory and factory inspections in 1906 and in1916 inall provinces of the Russian Empire, with the exception of six provinces of the frontline zone. A lot of work was done to record electricity production / consumption, which was conducted by the Russian electrotechnical community. According to incomplete data published in 1917 by the Secretariat of the Standing Committee of the VII All-Russia Electrotechnical Congress, from 1905 to 1913 (that is, for 8 years) the total number of power stations in the Russian Empire increased by 1.7 times, and the amount of electricity produced by them Has grown in 3,8 times. The last objective study of the state of the pre-revolutionary electric power industry in 1920 was carried out by the developers of the GOELRO plan from the 6th (Petrograd) group (engineers T.F. Makariev, A.I. Firsov, Y.A. Samoilovich and professor N.N. Georgievsky). With the help of expert assessments, they established that in 1916 all public power stations and block stations of the census industry produced approximately 4,730 million kWh of electric power-twice as much as in 1913. Based on the revealed range of historical statistical sources, it is proved that in 1913–1916, The Russian Empire in terms of electricity production was one of the top five industrialized countries. The dynamic development of the electric power industry in the Russian Empire was interrupted by the succession of social and political cataclysms that shook the very foundations of Russian statehood. This period, which lasted until the end of 1921, was characterized by a complete degradation of the electric power industry. The fuel hunger, separation from the RSFSR of Baku and the Donbass, paralyzed the work of many power plants, both factory and general use.

Yet more on a stochastic economic model: Part 3A: stochastic interpolation: Brownian and Ornstein–Uhlenbeck (OU) bridges

AbstractIn this paper, we develop certain properties for discrete Brownian bridges and Ornstein–Uhlenbeck bridges, which we use in the successor papers Part 3B and Part 3C to analyse real economic data series, with a view to constructing stochastic interpolation models for the Wilkie asset model.

Odhad parametrů rozšířeného Kaldorova modelu a analýza stability stacionárního řešení

In this article we analyze the continuous inflation dynamics using a four-equation model. When constructing the model, the traditional Kaldorian two-equation model is extended by adding two other equations. One of them describes an adaptive inflation expectations and the other continuous dynamics of the money market. In this setting, the instability velocity of money circulation is assumed due to the effects of expected inflation on money circulation velocity. Then the parameters of the model are estimated using the real Czech economic data. As it is a non-linear model in its parameters, a non-linear estimation technique is used for this purpose. Further, the stationarity as well as the stability of the estimated model is thoroughly examined as its instability may indicate that the model can generate some complex dynamics.

Inference and testing breaks in large dynamic panels with strong cross sectional dependence

In this paper we provide a new Central Limit Theorem for estimators of the slope papers in large dynamic panel data models (where both n and T increase without bound) in the presence of, possibly, strong cross-sectional dependence. We proceed by providing two related tests for breaks/homogeneity in the time dimension. The first test is based on the CUSUM principle; the second test is based on a Hausman–Durbin–Wu approach. Some of the key features of the tests are that they have nontrivial power when the number of individuals, for which the slope parameters may differ, is a “negligible” fraction or when the break happens to be towards the end of the sample, and do not suffer from the incidental parameter problem. We provide a simple bootstrap algorithm to obtain (asymptotic) valid critical values for our statistics. An important feature of the bootstrap is that there is no need to know the underlying model of the cross-sectional dependence. A Monte-Carlo simulation analysis sheds some light on the small sample behaviour of the tests and their bootstrap analogues. We implement our test to some real economic data.

Quantitative Analysis in Economics Based on Wavelet Transform: A New Approach

Recently, a number of new techniques to analyze complex, non-linear and non-stationary economic and financial data have been introduced. One of the techniques that may substitute conventional approaches based on a Fourier transform (FT) is wavelet transform (WT). On the other hand, despite the fact that wavelets have a huge potential enabling accurate representation of relationships between economic variables in the time-scale space, their use in economics is still rather limited with apparent reasons. In this paper, we will examine the use of the wavelets for the analysis of complex economic events and introduce the so-called truncated wavelets and an additional metric that may be valuable for processing of real economic and financial data. The presented approach may also contribute to the enhancement of our understanding of economic phenomena. The results are illustrated on a real example.

The Economic Man

This paper questions how economic systems are using knowledge to support economic decision making. Scientific knowledge collected by centralized planners is not the right type of information to base economic decisions on since it does not have the capacity to consider local opportunity factors. These concerns, originally espoused by Frederick Hayek when analyzing the economy as a whole, can be decomposed to focus on individual businesses. A large corporation, encumbered with a traditional vertical organizational structure, typically has an executive management team taking on the role of the centralized planners in Hayek’s model, and is forced to make decisions without the local opportunity knowledge that may be available in their low-level business units. Economic decisions must fundamentally be based on accurate information that considers local opportunities, and must be made rapidly in today’s dynamic market place. To get access to local knowledge from their low-level business units, executive management teams should consider implementing learning organizations with horizontal management structures that encourage collaboration. Companies with centralized organizational structures simply move too slowly to be competitive. The “Economic Man” is an economist’s creation designed by Mr. Hayek to show how unlikely it is that economic decision makers could ever accurately predict future economic conditions. A hypothetical “Economic Man” with “perfect foresight” was described, in essence, to allow readers to visualize the futility of the assumptions underlying our current economic theories. Yet in today’s competitive business environment, perfect foresight is precisely the standard we impose on our executive managers. The perfect knowledge assumption is said to mislead these economic decision makers into a false believe the problem is simpler, from the perspective of the information needed to support such decisions, than it actually is. This results in poor investment decisions by managers that collect the wrong economic data. The division of labor concept notices that complex jobs can be more efficiently completed by a number of workers performing specialized tasks, than by one worker trying to complete the entire job. By dividing up the required skills on complex tasks across multiple workers, they can become specialists and work their assigned area more efficiently. The level of knowledge required to make workers skilled specialists is essential for the division of labor concept to be effective. Further knowledge is required to quantify economic input variables to the extent that decision makers in executive management can accurately predict how the marketplace will unfold and, in so doing, achieve an "economic man" like stature. No matter how we materialize or describe the problem; it seems the effective acquisition and use of knowledge is the correct answer. Peter Drucker relates the economic successes a corporation has with their ability to create “core competencies” that lead to market leadership positions. He begins to abstractly define the type of data that should be collected to help managers of these corporations monitor the strength and relative position of their core competencies. This relates to Mr. Hayek’s concern that managers need accurate, but relevant, information to base their decisions on. Just when things were looking bleak for our hypothetical “Economic Man,” these important steps define feasible types of real time information processing that can support economic decision makers monitoring the strength and position of core competencies. While it’s not quite as good as the perfect foresight imposed on our “Economic Man,” relevant, near real time economic data is a giant leap forward.

Maize Price Stabilization in Ghana: An Application of a Continuous-Time Delay Differential Equation Model with Buffer Stock

The paper is intended to use mathematical models for controlling fluctuations in the price of maize by employing a nonlinear continuous time delay differential equation derived from linear demand and nonlinear supply functions of price. The delay parameters reflect the realities prevailing at the local market. These models are formulated from parameters estimated from real economic data of maize price demand and production in the Ashanti Region of Ghana through the use of regression methods. The data is obtained from the Ministry of Food and Agriculture, Statistical Directorate Kumasi-Ghana, pertaining to years from 1994 to 2013. The results of the study are connected to the assertion that commodity price is dependent on planting time, storage time, relaxation time and total production time. If all these individual time segments are combined as one for supply delay to make up total storage time, which is the delay for the buffer, then price oscillations would be drastically reduced as shown in this paper. The study is, also, an improvement on the work done by earlier workers, whose discrete time models are limiting cases of the delay buffer stock model used in this study. The efficiency of a buffer system is proven to be dependent on delay variation suitably enough to be used by buffer stock operators. It is noted that, the farther the buffer stock delay and supply delay are reduced, the more stable the price becomes and the effects of buffer stock are felt more by stakeholders. The results of the analysis provide an average stable price of maize as GH¢ 30.49 compared to the actual average price of GH¢30.27. The equilibrium price in turn provides the average equilibrium weight of 2931.6 and 8217.6 metric tons for demand and supply respectively. The excess supply is kept in stock and when needed it is released in the next market period. The standard deviation also is reduced to 0.1602 compared to the original 29.48 before the application of buffer stock scheme. Thus, before the application of buffer stock scheme, price oscillated between two price points and could not converge. This affirms the fact that buffer stock acts as a reserve against short-term shortages and dampens excessive fluctuations. We draw inferences from this study that researchers should rather use continuous time nonlinear delay models as they reflect the realities in most real-life economic problems.

Quantitative Modelling in Economics with Advanced Artificial Neural Networks

Abstract In this paper, authors present a new approach in forecasting economic time series - application of artificial neural networks. Authors apply feed forward artificial neural network of the RBF type into the process of forecasting the financial data. Except for the standard RBF, authors also test their own new versions of this neural network combined with other techniques of the ML. These models represent new and more advanced version of the standard neural network. Authors add the evolutionary approach into the ANN and also combine the standard algorithm for adapting weights of the ANN with an unsupervised clustering algorithm called K-means. Finally, all of these methods are compared and contrasted with standard (statistical) approach on real economic data to show the potential of using artificial neural network in modelling economic variables.

A Rolling Grey Model Optimized by Particle Swarm Optimization in Economic Prediction

Grey system theory has been widely used to forecast the economic data that are often nonlinear, irregular, and nonstationary. Current forecasting models based on grey system theory could adapt to various economic time series data. However, these models ignored the importance of the model parameter optimization and the use of recent data, which lead to poor forecasting accuracy. In this article, we propose a novel forecasting model, called particle swarm optimization rolling grey model (PSO‐RGM(1,1)), based on a rolling mechanism GM with optimized parameters by using the particle swarm optimization algorithm. The simple model is shown to be very effective in forecasting the tertiary industry data sequences, which are short and noisy but regular in secular trend. The experimental results show that PSO‐RGM(1,1) outperforms other commonly used forecasting models on three real economic data sets. Our empirical study shows that PSO is found to be the best overall algorithm to optimize the parameter of RGM compared with other well‐known metaheuristics. Furthermore, we evaluated other variant PSOs and found that single particle PSO outperforms others overall in terms of prediction accuracy, convergence speed, and degree of certainty.

International Real Estate Review

Securitized equity interests in commercial property (the so-called listed REIT market) across a number of countries are analyzed. In so doing, we have pulled together financial as well as real economic data from a large number of different sources. On the real side, we focus particularly on office property construction, as data are fairly available in that sector. The comparison of REIT markets across countries reveals significant variation in terms of institutional characteristics, but many similarities in terms of financial performance. Office construction activity also displays some synchronicity across countries, with a general pattern of reduced levels and volatility in construction activity over the past two decades. Linkages between REIT market development and the dynamics of new construction activity are explored.

Cost analysis of the stroke volume variation guided perioperative hemodynamic optimization - an economic evaluation of the SVVOPT trial results.

BackgroundPerioperative goal directed therapy (GDT) can substantially improve the outcomes of high risk surgical patients as shown by many clinical studies. However, the approach needs initial investment and can increase the already very high staff workload. These economic imperatives may be at least partly responsible for weak adherence to the GDT concept. A few models are available for the evaluation of GDT cost-effectiveness, but studies of real economic data based on a recent clinical trial are lacking. In order to address this we have performed a retrospective analysis of the data from the “Intraoperative fluid optimization using stroke volume variation in high risk surgical patients” trial (ISRCTN95085011).MethodsThe health-care payers perspective was used in order to evaluate the perioperative hemodynamic optimization costs. Hospital invoices from all patients included in the trial were extracted. A direct comparison between the study (GDT, N = 60) and control (N = 60) groups was performed. A cost tree was constructed and major cost drivers evaluated.ResultsThe trial showed a significant improvement in clinical outcomes for GDT treated patients. The mean cost per patient were lower in the GDT group 2877 ± 2336€ vs. 3371 ± 3238€ in controls, but without reaching a statistical significance (p = 0.596). The mean cost of all items except for intraoperative monitoring and infusions were lower for GDT than control but due to the high variability they all failed to reach statistical significance. Those costs associated with clinical care (68 ± 177€ vs. 212 ± 593€; p = 0.023) and ward stay costs (213 ± 108€ vs. 349 ± 467€; p = 0.082) were the most important differences in favour of the GDT group.ConclusionsIntraoperative fluid optimization with the use of stroke volume variation and Vigileo/FloTrac system showed not only a substantial improvement of morbidity, but was associated with an economic benefit. The cost-savings observed in the overall costs of postoperative care trend to offset the investment needed to run the GDT strategy and intraoperative monitoring.Trial registrationISRCTN95085011

Analysis of Economic Resiliency of Communities Affected by Natural Disasters: The Bay Area Case Study

The paper focuses on the economic impact of natural disasters on different economic sectors in a given community considering their interdependencies. In the last twenty years, several catastrophe models have been developed for measuring economic losses. Nevertheless, due to the lack of data and the complexity of the problem, significant levels of uncertainties are still present. In fact, the factors that drive the economic recovery process before, during, and after natural disasters needs to be determined to select the optimal resources allocation and preparedness measures right after an extreme event. In most of the previous catastrophe models interdependencies between physical and nonphysical infrastructures are mostly neglected. This paper is proposing a model that describes the economic effects and characteristics that should be taken into account to predict the monetary impact of natural disasters, focusing in particular on the economic interdependencies of industries and lifelines. Different types of losses are considered using real economic data provided by surveys on natural disasters such as Northridge earthquake, Des Moines flood, etc. The data associated with the physical damages are obtained from HAZUS database. The Economic Resilience Index provided in the PEOPLES framework is adopted and applied to the specific case study of the San Francisco Bay Area. Sensitivity analysis is performed for each economic sector considered in the analysis.

Estimating total economic merit for the Portuguese Holstein cattle population under new economic conditions

The objective of this study was to develop a total economic merit index that identifies more profitable animals using Portugal as a case study to illustrate the recent economic changes in milk production. Economic values were estimated following future global prices and EU policy, and taking into consideration the priorities of the Portuguese dairy sector. Economic values were derived using an objective system analysis with a positive approach, that involved the comparison of several alternatives, using real technical and economic data from national dairy farms. The estimated relative economic values revealed a high importance of production traits, low for morphological traits and a value of zero for somatic cell score. According to several future market expectations, three scenarios for milk production were defined: a realistic, a pessimistic and an optimistic setting, each with projected future economic values. Responses to selection and efficiency of selection of the indices were compared to a fourth scenario that represents the current selection situation in Portugal, based on individual estimated breeding values for milk yield. Although profit resulting from sale of milk per average lactation in the optimistic scenario was higher than in the realistic scenario, the volatility of future economic conditions and uncertainty about the future milk pricing system should be considered. Due to this market instability, genetic improvement programs require new definitions of profit functions for the near future. Effective genetic progress direction must be verified so that total economic merit formulae can be adjusted and selection criteria redirected to the newly defined target goals.

A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.