Predicció de la taxa d’atur espanyola: un anàlisi regional

Treballs Finals del Grau de d'Administració i Direcció d'Empreses, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2017-2018 , Tutor: Oscar Claveria González(cat) Aquest projecte d’investigació té per objectiu principal la realització i estudi previ de les prediccions de l’atur espanyol, posant èmfasi a l’anàlisi regional. Per un costat, es fa ús dels mètodes deterministes per la taxa de desocupació nacional. Mitjançant els contrastos de Daniel i Kruskal-Wallis es detecta que la sèrie temporal presenta tendència però no component estacional. És per això que els mètodes emprats en la primera fase del exercici són: la TL (Tendència Lineal), les DMM (Dobles Mitjanes Mòbils) i l’AEH (Allisat Exponencial de Holt). Per l’altre costat, es generen prediccions a través de models ARIMA (Model Autoregressiu de Mitjana Mòbil) a nivell nacional i per a cada una de les disset CCAA (Comunitats Autònomes). En darrer lloc, s’avalua la capacitat predictiva dels mètodes i models utilitzats. La conclusió principal d’aquest estudi subratlla la importància de generar prediccions independents per a cada regió.(eng) The goal of this research project is to conduct a forecasting experiment for the Spanish unemployment rate at a regional level. We use a sampling size that starts at the first quarter of the year 2002 and ends at the last quarter of the year 2017. First, we apply a deterministic approach at the national level. We run Daniel and Kruskal-Wallis tests and find that the unemployment growth rate presents a significant trend component although the seasonal component is not significant. As a result, we use the Linear Trend, Double Moving Average and Holt Exponential Smoothing methods. Second, we generate forecasts by means of ARIMA models at the national and regional level. At the national exercise we use the Box-Jenkins Methodology with Gretl and compare it to the output generated with the auto-ARIMA function with R designed by Hyndman and Khandakar (2008). We find that the auto-ARIMA function provides better outcomes, for this reason we use it for model selection at the regional level. We obtain different optimal ARIMA models in each region. This finding suggests that unemployment forecasting should be implemented by means of region-specific models. Finally, we compute the out-of-sample forecast accuracy for the four quarters of the year 2017. We obtain the best results for Galicia and Valencian Community, as opposed to Aragon

Herramientas para la toma de decisiones en la planificación financiera de la jubilación

La desfavorable evolución de las variables demográficas y económicas supone una agravación de la sostenibilidad del sistema público de pensiones español, lo cual también dificulta poder mantener la suficiencia de las pensiones públicas para la jubilación. En consecuencia, los trabajadores se plantean invertir en instrumentos de ahorro privado para complementar la pensión pública y así garantizar la suficiencia de ingresos en el momento de la jubilación. El trabajo plantea herramientas alternativas para seleccionar el producto de ahorro para la jubilación más conveniente. Estas herramientas se basan en el uso del operador linguistic ordered weighted averaging (LOWA), el operador induced linguistic ordered weighted averaging (ILOWA), el operador linguistic ordered weighted averaging distance (LOWAD) y el operador linguistic induced ordered weighted averaging distance (LIOWAD). Al final del trabajo, se presenta un ejemplo ilustrativo de la aplicación de los operadores de agregación LOWA, ILOWA, LOWAD y LIOWAD en procesos de planificación financiera para la jubilación. Los resultados demuestran la utilidad de esta clase de operadores de agregación lingüísticos en la toma de decisiones para la jubilación

Models for dealing with uncertainty in decision-making

[eng] All aspects of life revolve around making decisions. From the very basic, such as choosing each morning what clothes to wear, to the most transcendental, as might be deciding what bachelor’s degree to study. Also, all decisions are subject to different types and degrees of uncertainties. In recent decades, major theoretical advances have taken place in the field of decision-making under uncertainty. Moreover, a large variety of applications. However, there are still outstanding research gaps. Some of these unresolved problems have been successfully addressed in this doctoral thesis, under the title of “Models for dealing with uncertainty in decision-making”. Specifically, the applicability of Yager’s ordered weighted averaging (OWA) operator in issues related to retirement and the need of new comparative tools for particular and complex situations. This doctoral thesis is divided into five chapters. Chapter 1 gives an introduction, discusses the objectives, explains the followed methodology, and provides information regarding the structure of the thesis work. Chapter 2 offers a comprehensive review of mathematical methods for decision-making under uncertain environments, particularly of the OWA operator. The OWA operator is a nonlinear function for aggregating information that has gained much popularity. Also, a bibliometric analysis is conducted in order to quantitatively explore a large volume of publications related to the OWA operator. This is done by using the Web of Science (WoS) Core Collection data source and the Visualization of Similarities (VOS) viewer software. Likewise, the main theoretical concepts of pensions as well as the knowledge domain are described. Chapter 3 presents a compendium of five significant research contributions related to the general objective of the doctoral thesis. Two of these contributions show new aggregation operators based on the OWA operator, the adequacy coefficient, the linguistic variable, and the interval number. These novel aggregation operators are proven to be very useful in real-life decision-making problems under a high degree of uncertainty, particularly when the decision maker wants to compare different alternatives with an ideal but without giving any penalty or reward in the case that the ideal levels are exceeded. Two extensive illustrative examples are offered, one regarding business internationalization and another one regarding human resource practices in football. The three remaining research contributions explore the use of the OWA operator and some of its prime extensions in pension decision-making. One contribution measures the future average pension adjusted for inflation for all autonomous communities of Spain. Similarly, the same index is calculated in a further contribution but in this case for each state of the United States (U.S.). Another contribution designs two algorithms to choose the most suitable product for supplementing the public pension when a person retires and develops a practical example for a better understanding. These three research contributions seek to positively impact the lives of the current and future retirees by making available practical tools for pension decision-making. Chapter 4 points out the final conclusions, limitations, and future research opportunities of this thesis work. Finally, Chapter 5 includes as an annex an additional research contribution, where basic uncertain information (BUI) is used to assess different types of enterprise risks, as it allows to effectively model uncertainty. Then the BUI assessments are aggregated through an extension of the OWA operator, thus facilitating the prioritization of the identified risks

Models for dealing with uncertainty in decision-making

Programa de Doctorat en Empresarials[eng] All aspects of life revolve around making decisions. From the very basic, such as choosing each morning what clothes to wear, to the most transcendental, as might be deciding what bachelor’s degree to study. Also, all decisions are subject to different types and degrees of uncertainties. In recent decades, major theoretical advances have taken place in the field of decision-making under uncertainty. Moreover, a large variety of applications. However, there are still outstanding research gaps. Some of these unresolved problems have been successfully addressed in this doctoral thesis, under the title of “Models for dealing with uncertainty in decision-making”. Specifically, the applicability of Yager’s ordered weighted averaging (OWA) operator in issues related to retirement and the need of new comparative tools for particular and complex situations. This doctoral thesis is divided into five chapters. Chapter 1 gives an introduction, discusses the objectives, explains the followed methodology, and provides information regarding the structure of the thesis work. Chapter 2 offers a comprehensive review of mathematical methods for decision-making under uncertain environments, particularly of the OWA operator. The OWA operator is a nonlinear function for aggregating information that has gained much popularity. Also, a bibliometric analysis is conducted in order to quantitatively explore a large volume of publications related to the OWA operator. This is done by using the Web of Science (WoS) Core Collection data source and the Visualization of Similarities (VOS) viewer software. Likewise, the main theoretical concepts of pensions as well as the knowledge domain are described. Chapter 3 presents a compendium of five significant research contributions related to the general objective of the doctoral thesis. Two of these contributions show new aggregation operators based on the OWA operator, the adequacy coefficient, the linguistic variable, and the interval number. These novel aggregation operators are proven to be very useful in real-life decision-making problems under a high degree of uncertainty, particularly when the decision maker wants to compare different alternatives with an ideal but without giving any penalty or reward in the case that the ideal levels are exceeded. Two extensive illustrative examples are offered, one regarding business internationalization and another one regarding human resource practices in football. The three remaining research contributions explore the use of the OWA operator and some of its prime extensions in pension decision-making. One contribution measures the future average pension adjusted for inflation for all autonomous communities of Spain. Similarly, the same index is calculated in a further contribution but in this case for each state of the United States (U.S.). Another contribution designs two algorithms to choose the most suitable product for supplementing the public pension when a person retires and develops a practical example for a better understanding. These three research contributions seek to positively impact the lives of the current and future retirees by making available practical tools for pension decision-making. Chapter 4 points out the final conclusions, limitations, and future research opportunities of this thesis work. Finally, Chapter 5 includes as an annex an additional research contribution, where basic uncertain information (BUI) is used to assess different types of enterprise risks, as it allows to effectively model uncertainty. Then the BUI assessments are aggregated through an extension of the OWA operator, thus facilitating the prioritization of the identified risks

Herramientas para la toma de decisiones en la planificación financiera de la jubilación

The unfavorable development of the demographic and economic variables has a negative impact on the sustainability of the public pension system of Spain and consequently on the pension adequacy. Therefore, it encourages workers to invest in alternative savings products in order to supplement the state pension and thereby ensure an adequate retirement income. This study suggests different methods that may help citizens to choose the most suitable product for supplementing the state pension when they retire. These methods are based on the use of the linguistic ordered weighted averaging (LOWA) operator, the induced linguistic ordered weighted averaging (ILOWA) operator, the linguistic ordered weighted averaging distance (LOWAD) operator and the linguistic induced ordered weighted averaging distance (LIOWAD) operator. At the end of the work, an illustrative example will be developed using the LOWA, ILOWA, LOWAD and LIOWAD aggregation operators for retirement financial planning. The results show the usefulness of this type of linguistic aggregation operators in retirement decision-making.La desfavorable evolución de las variables demográficas y económicas supone una agravación de la sostenibilidad del sistema público de pensiones español, lo cual también dificulta poder mantener la suficiencia de las pensiones públicas para la jubilación. En consecuencia, los trabajadores se plantean invertir en instrumentos de ahorro privado para complementar la pensión pública y así garantizar la suficiencia de ingresos en el momento de la jubilación. El trabajo plantea herramientas alternativas para seleccionar el producto de ahorro para la jubilación más conveniente. Estas herramientas se basan en el uso del operador linguistic ordered weighted averaging (LOWA), el operador induced linguistic ordered weighted averaging (ILOWA), el operador linguistic ordered weighted averaging distance (LOWAD) y el operador linguistic induced ordered weighted averaging distance (LIOWAD). Al final del trabajo, se presenta un ejemplo ilustrativo de la aplicación de los operadores de agregación LOWA, ILOWA, LOWAD y LIOWAD en procesos de planificación financiera para la jubilación. Los resultados demuestran la utilidad de esta clase de operadores de agregación lingüísticos en la toma de decisiones para la jubilación

The uncertain ordered weighted averaging adequacy coefficient operator

This article introduces the uncertain ordered weighted averaging adequacy coefficient (UOWAAC) operator. This novel operator uses the ordered weighted averaging (OWA) operator, the adequacy coefficient, and the interval numbers in a single formulation. This article also extends the UOWAAC operator by using order-inducing variables in the reordering process of the input arguments. This new extension is called the uncertain induced ordered weighted averaging adequacy coefficient (UIOWAAC) operator. The article also presents an application of the new approach in a multi-criteria group decision making (MCGDM) problem about international expansion. In addition, a comparative analysis is conducted with the purpose of demonstrating the superiority of the UOWAAC and UIOWAAC aggregation operators in specific situations. Likewise, the use of basic uncertain information (BUI) is discussed. The results show the usefulness of these new aggregation operators in real-life decision making problems under uncertainty, particularly when the decision maker wants to compare different alternatives with an ideal but without giving any penalty or reward in the case that the ideal levels are exceeded

A Bibliometric Review of the Ordered Weighted Avaraging Operator

The ordered weighted averaging (OWA) operator was proposed by Yager back in 1988 and constitutes a parameterized family of aggregation functions between the minimum and the maximum. The purpose of this paper is to perform a bibliometric review of this aggregation operator during the last 35 years through the Web of Science (WoS) Core Collection database and the Visualization of Similarities (VOS) viewer software. The results show that the OWA operator is an increasingly popular aggregation operator, especially in Computer Science. The results also allow the assertion that Yager, as expected, is still the most influential and productive author. Moreover, the study reveals that institutions from over 80 countries have contributed to OWA research, highlighting the high presence of Chinese universities and the emergence of Pakistani ones. Other interesting findings are presented to provide a comprehensive and up-to-date analysis of the OWA operator literatur