Econometría

This exciting new volume examines the development of market performance from Antiquity until the dawn of the Industrial Revolution.
Efficient market structures are agreed by most economists to serve as evidence of economic prosperity, and to be prerequisites for further economic growth. However, this is the first study to examine market performance as a whole, over such a large time period. Presenting a hitherto unknown and inaccessible corpus of data from ancient Babylonia, this international set of contributors are for the first time able to offer an in-depth study of market performance over a period of 2,500 years.
The contributions focus on the market of staple crops, as they were crucial goods in these societies. Over this entire period, all papers provide a similar conceptual and methodological framework resting on a common definition of market performance combined with qualitative and quantitative analyses resting on new and improved price data. In this way, the book is able to combine analysis of the Babylonian period with similar work on the Roman, Early-and Late Medieval and Early Modern period.
Bringing together input from assyriologists, ancient historians, economic historians and economists, this volume will be crucial reading for all those with an interest in ancient history, economic history and economics.
This is part of a research project conducted at the VU University (Vrije Universiteit) Amsterdam, funded by the Netherlands Organisation for Scientific Research (NWO) and the Royal Netherlands Academy of Arts and Sciences (KNAW) on "The efficiency of markets in Pre-Industrial Societies: the case of Babylonia, 485-61 BC".

In general the undergraduate curriculum in statistics offers one semester basic course on linear programming (LP). The conventional course on LP includes simplex algorithm, duality theory, sensitivity analysis and some applications like the feedmix and transport problems. This outline seems to be distant from the mainstream curriculum in statistics. In order to partially overcome this shortcoming, we propose to introduce a technique based on linear programming called Data Envelopment Analysis (DEA) on the LP courses for students of statistics. DEA has gained momentum as a powerful complementary method for statistical tools in the research agenda as well as in practical activities of economic efficiency evaluation.

Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models when applied to the time series filtered by SSA is investigated in this paper. Three different methodologies are evaluated in the SSA approach: Principal Component Analysis (PCA), Cluster Analysis and Graphical Analysis of Singular Vectors. In order to illustrate and compare the methodologies, in this paper, we also present the main results of a computational experiment with the monthly residential consumption of electricity in Brazil.

The aim of this paper is to explain how car prices vary depending on the characteristics of the vehicle,
developing a multivariate regression model.
We all have a car and in the moment of purchasing we can choose among different models. So the prices
vary according to the different optional or different engine, different needs.
But nobody knows that our choices make the prices. It seems a banal assertion, but we can also rephrase it
in : “nobody knows how and in which measure our choices influence prices”.
Our intention is to quantify the effect of our choices by a model that includes the most important variable
that should influence prices.
The paper starts with a short description of the parent population, so follows the description of the data
sample. Subsequently the aim of the model will be illustrated both in qualitative terms and in quantitative
terms.
Every data processing is done with Gretl.

http://www.sciencedirect.com/science/article/pii/S0038012117300617
The aim of this paper is to investigate the impact of internal and external research collaborations on the scientific performance of academic institutions. The data are derived from the international SCOPUS database. We consider both quantity (the number of publications) and quality indicators (the field-weighted citation impact and the share of publications in the 10% most-cited articles) to evaluate universities' performance in some European countries (Germany, France, Italy, the UK and Russia). To this end, we develop a non-overlapping generations model to evidence the theoretical idea of research externalities between academic institutions. Moreover, we implement an empirical model to determine the extent to which the impact of internal and external collaborations on universities' performance is sensitive to the geographical dimension of the data.

Highlights
• The paper investigates the impact of internal and external research collaborations on the scientific performance of academic institutions.
• It highlights the importance of both quantity (the number of publications) and quality indicators (the field-weighted citation impact and the share of publications in the 10% most-cited articles) to evaluate universities' performance.
• It explores universities' performance in Germany, France, Italy, the UK and Russia for the period 1996–2015.
• Empirical findings show that international collaborations help universities' quality index but the results are differentiated by groups of countries.

http://dx.doi.org/10.1016/j.seps.2017.05.003

Multiregional dynamic models of economic growth rarely capture the interdependencies among regions that are geographically distant and/or often underestimate the importance of these linkages. This bias has become more and more serious because travel and transportation costs continue to decrease, while new telecommu-nication and information technologies enable business activities to readily take place between geographically remote locations. The conceptual framework in this study–modelling the network of regions–is based on well-known spatial economet-ric methods and provides alternative ways to inte-grate network interdependencies of economic ac-tivities into many fields, as well as modelling tech-niques such as spatial computable general equilib-rium, input-output, and dynamic econometric models.

The world's economic centre of gravity Recently, an analysis has been conducted on the world's economic centre of gravity (Economist 2012, Grether-Mathys 2010, Quah 2011). Our aim is to examine all the countries of the world's economic centre of gravity together, while examining the continents separately. In our study, the geometric centre of the area for all countries is weighted by nominal GDP obtained from the World Bank database for the years 1970–2014 (GDP data at current US $ market prices). Although this method can be problematic, particularly for wide-area countries, it can address the most important trends of spatial displacements. Here, we used the official map of Eurostat as a base map with ETRS89 projection. During the study, analyses on all the countries were carried out, while the continents were analysed separately (America, Africa, Asia [including Australia and Oceania], and Europe). In our model, Russia was considered as a part of Europe. In the model, changes in the country's borders related to influencing factors including the interpretation of the illustration on the gravitational movement trends were examined, and not just the fluctuations from one year to another. We illustrated each year for reasons of visibility, in the global study; however, in the case of continents, only a period of six years was taken into account. Globally, the economic centre of gravity moved from the northwest to the southeast, essentially without a significant overshoot. The movement from the

In the study, the authors aim to rank Turkey’s provinces through the lens of innovativeness, entrepreneurship, and human capital. They use
company establishment and liquidation statistics, industrial capacity report statistics, intellectual property statistics, national education statistics, and higher education statistics for this purpose.
The presented 16 variables are grouped into 3 categories: entrepreneurship, innovativeness, and human capital. As a ranking technique, the preference ranking organization method for enrichment
evaluation (PROMETHEE) is used. The 81 provinces of Turkey are ranked in terms of innovativeness, entrepreneurship and human capital performance, and their advantages are shown relative to one another. According to the results, Istanbul takes the first place followed by
Ankara, İzmir, Konya, and Kocaeli provinces, while Sinop, Iğdır, Tunceli, Ardahan, and Bayburt provinces perform the worst.

Ante la relevancia tanto social como académica que recientemente
han suscitado los fenómenos asociados a la estratificación social, el
presente artículo pretende realizar un análisis sincrónico que sirva de
“estampa” de los factores asociados a la pobreza en 2018, una vez
transcurridos diez años desde el inicio de la Gran Depresión. Con este
objeto, se parte de una concepción relativa de la pobreza, que nos permite
tratar la insuficiencia relativa de ingresos a partir de la diferenciación entre
pobres y no pobres. A partir de ahí, se obtiene un perfil de los individuos
con bajos ingresos en España, para posteriormente exponer los factores
socioeconómicos y demográficos asociados a este fenómeno a través de
un análisis bivariante y de regresión logística. Las conclusiones expuestas
señalan que no existe una relación directa entre la pobreza y la residencia
en el norte y sur de España, si bien los datos evidencian una asociación
entre ésta y factores como la edad, nivel de estudios, situación profesional
y laboral o sector productivo.

Given the social and academic relevance recently aroused by the
social stratification, this paper aims to carry out a synchronic analysis that
will serve as a "picture" of the factors associated with economic poverty
in Spain, after ten years have elapsed since beginning of the Great
Depression. With this purpose, we start from a relative conception of
poverty, which allows us to treat the relative income insufficiency in terms
of “poor” and “non-poor”. Therefore, a profile of individuals with low
income in Spain is obtained, to later expose the socioeconomic and
demographic factors related to this phenomenon through a bivariate
analysis and several logistic regression models. Ultimately, our results
show that there is no direct relationship between poverty and residence in
the north or in the south of Spain, although the data show an association
between it and other individual factors such as age, educational level,
professional and employment situation or productive sector.

We evaluate the predictive performance of a variety of value-at-risk (VaR) models for a portfolio consisting
of five assets. Traditional VaR models such as historical simulation with bootstrap and filtered historical
simulation methods are considered. We suggest a new method for estimating Value at Risk: the filtered
historical simulation GJR-GARCH method based on bootstrapping the standardized GJR-GARCH residuals.
The predictive performance is evaluated in terms of three criteria, the test of unconditional coverage,
independence and conditional coverage and the quadratic loss function suggested. The results show that
classical methods are in efficient under moderate departures from normality and that the new method
produces the most accurate forecasts of extreme losses.

This  paper  proposes  a  new  measure  for  worldwide  economic  distress,  which  can  be  described  as  the  proportion  of  the  gross  domestic  product  (GDP)  affected  by  country-level financial crises. It proves that this measure  has  the  desirable  properties  of  fair  and 
representative indices. It also adequately justifies the beliefs of some economists that financial  crises  have  negative  effects  on  economic 
performance.