Empirical Study of Volatility Process on Error Correction Model Estimation

Ada dua tujuan yang ingin dicapai dalam penelitian ini. Pertama, adalah untuk menyelidiki apakah dalam estimasi model koreksi kesalahan atau error correction model (ECM) terdapat proses volatilitas. Jika ternyata ada, maka model estimasi koreksi kesalahan seharusnya diestimasi dengan menggunakan model volatilitas. Hasil empirik estimasi ECM ternyata mengindikasikan adanya proses volatilitas yang ditunjukkan oleh signifikannya pengujian Autoregressive Conditional Heteroscedasticity (ARCH).Tujuan kedua adalah untuk menentukan model yang paling baik antara estimasi ECM dan estimasi ECM yang diikuti dengan proses volatilitas. Setelah dilakukan estimasi terhadap kedua model tersebut ternyata dapat disimpulkan bahwa estimasi model ECM dengan proses Generalized ARCH (EC-GARCH) lebih baik dibandingkan dengan estimasi model ECM. Sebagai contoh kasus digunkan model estimasi indeks harga saham gabungan di bursa efek Jakarta (BEJ)

Pedestrian route choice: an empirical study

There has been relatively little work done on route choice for pedestrians. The present paper addresses this issue by using a sample survey of daily walks in a UK urban area. The walks undertaken are reconstructed using a geographical information system and compared with the shortest available route. It was found that about 75 per cent of walkers in the sample chose the shortest available route. Two strategies were used to synthesise sets from which pedestrians could have chosen their routes. These choice sets can then be used in discrete choice modelling to study route choice and to determine which factors are important to pedestrians in this. At the time of writing, it is proposed to proceed with this modelling. The structure of the paper is as follows. Section 2 describes the various sources of data used in this work, section 3 discusses the choice set generation strategies that were developed, section 4 briefly compares the walks with the corresponding shortest routes, while section 5 presents the conclusions that were drawn from this

Empirical Study: Moroccan Information systems specificities for better IT Governance

The spread of information systems (IS) use has become an essential criterion for judging today's overall development level of a country and its attractiveness for capital and investment. Many International rankings evaluate the performance of different countries at this level. And Morocco occupies a disappointing position compared to its potential. Paradoxically, Morocco is lagging behind, although in the telecom sector, it is ahead of many developing countries, Thus, in 2015, the index NRI (Networked Readiness Index), measuring the preparation of an economy to make effective use of new information technologies (IT) published by the World Economic Forum, ranks Morocco in the 87th place. Indeed, with the exception of large companies that have implemented a set of tools to automate the process, a large number of SMEs and SMIs are very late as far as IT use is concerned. It means that IS in Morocco is still unable to achieve business perspectives for benefits and processes optimization. The aim of this article is to understand the particularities of Moroccan IS to understand the week points to correct in order to govern well enterprise Information technologies

Microfinance social performance: A global empirical study

Over the years, microfinance has been purported to have experienced enormous progress and is seen to contribute towards poverty reduction by extending finance to people previously excluded from formal financial markets. However, the question on how microfinance social performance is assessed remains unresolved. The paper develops an original social performance rating for 878 microfinance institutions (MFIs), across all geographic regions in the world for a period of 11 years (2000-2010). Furthermore, the paper investigates whether or not the age, assets, regulation status, loans per loan officers, as well as the profit status of MFIs affect MFIs’ ability to perform socially

Music in electronic markets: an empirical study

Music plays an important, and sometimes overlooked part in the transformation of communication and distribution channels. With a global market volume exceeding US$40 billion, music is not only one of the primary entertainment goods in its own right. Since music is easily personalized and transmitted, it also permeates many other services across cultural borders, anticipating social and economic trends. This article presents one of the first detailed empirical studies on the impact of internet technologies on a specific industry. Drawing on more than 100 interviews conducted between 1996 and 2000 with multinational and independent music companies in 10 markets, strategies of the major players, current business models, future scenarios and regulatory responses to the online distribution of music files are identified and evaluated. The data suggest that changes in the music industry will indeed be far-reaching, but disintermediation is not the likely outcome

Popular Ensemble Methods: An Empirical Study

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees

The Economics of Suicide: An Empirical Study

This study uses economic theory to investigate the impact of socioeconomic factors on the suicide rate in the United States. Using a utility maximization framework based on Hamermesh and Soss’ 1974 model, a panel data set from 2000-2010 is constructed for the 50 states and District of Columbia. This research adds to the literature in the field by focusing on the more recent past and providing additional variables consistent with today’s challenges. The results from the multiple regression analysis can be used to advocate policies that may reduce the suicide rate in the future