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on Information and Communication Technologies |
| By: | Sebastian von Engelhardt (University of Jena, Faculty of Economics) |
| Abstract: | Software ist ein Gut mit besonderen ökonomischen Eigenschaften. In diesem Artikel werden, ausgehend von einer allgemeinen Definition des Gutes Software, systematisch zentrale Eigenschaften herausgearbeitet, welche Implikationen für die Produktion und Kostenstruktur, die Nachfrage, der Bestreitbarkeit von Softwaremärkten und der Allokationseffizienz haben. Dabei hat es sich als sinnvoll erwiesen, die einzelnen Eigenschaften unter folgende Oberbegriffe zu subsummieren: Software als System zur Datenverarbeitung, Software als System von Befehlen bzw. Anweisungen, Software als rekombinierbares System, Software als ein nur in diskreten Einheiten nutzbares Gut, Software als komplexes System und Software als ein immaterielles Gut. Es zeigt sich, dass Software eine Fülle von ökonomisch relevanten Eignschaften aufweist, die von Netzwerkeffekten über subadditiver Nutzenfunktion bis hin zur Nichtrivalität reichen. Besonders hervorzuheben ist, dass Software sich von anderen Informationsgütern fundamental unterscheidet: Zum Einen fehlt ein aus Kundensicht relevanter additiver Nutzen, zum Anderen ist der durchschnittliche Nutzer/Konsument lediglich an dem Funktionieren der Algorithmen interessiert, nicht aber an der zugrundeliegenden Information. |
| Keywords: | additiver Nutzen, binäre Nachfrage, digitale Güter, Erfahrungsgut, Humankapital, Informationgut, Kompatibilität, Komplexität, Netzwerkeffekte, Nichtrivalität im Konsum, Open Source, Rekombinierbarkeit, Software, subadditive Kostenfunktion, Wissen |
| JEL: | D82 D83 D62 D85 K11 L86 |
| Date: | 2006–03–20 |
| URL: | http://d.repec.org/n?u=RePEc:jen:jenasw:2006-14&r=ict |
| By: | Essama-Nssah, B. |
| Abstract: | Effective development policymaking creates a need for reliable methods of assessing effectiveness. There should be, therefore, an intimate relationship between effective policymaking and impact analysis. The goal of a development intervention defines the metric by which to assess its impact, while impact evaluation can produce reliable information on which policymakers may base decisions to modify or cancel ineffective programs and thus make the most of limited resources. This paper reviews the logic of propensity score matching (PSM) and, using data on the National Support Work Demonstration, compares that approach with other evaluation methods such as double difference, instrumental variable, and Heckman ' s method of selection bias correction. In addition, it demonstrates how to implement nearest-neighbor and kernel-based methods, and plot program incidence curves in E-Views. In the end, the plausibility of an evaluation method hinges critically on the correctness of the socioeconomic model underlying program design and implementation, and on the quality and quantity of available data. In any case, PSM can act as an effective adjuvant to other methods. |
| Keywords: | Poverty Monitoring & Analysis,Poverty Impact Evaluation,Statistical & Mathematical Sciences,Scientific Research & Science Parks,Science Education |
| Date: | 2006–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wbrwps:3877&r=ict |
| By: | Manabu Asai; Michael McAleer |
| Abstract: | This paper proposes and analyses two types of asymmetric multivariate stochastic volatility (SV) models, namely: (i) SV with leverage (SV-L) model, which is based on the negative correlation between the innovations in the returns and volatility; and (ii) SV with leverage and size effect (SV-LSE) model, which is based on the signs and magnitude of the returns. The paper derives the state space form for the logarithm of the squared returns which follow the multivariate SV-L model, and develops estimation methods for the multivariate SV-L and SV-LSE models based on the Monte Carlo likelihood (MCL) approach. The empirical results show that the multivariate SV-LSE model fits the bivariate and trivariate returns of the S&P 500, Nikkei 225, and Hang Seng indexes with respect to AIC and BIC more accurately than does the multivariate SV-L model. Moreover, the empirical results suggest that the univariate models should be rejected in favour of their bivariate and trivariate counterparts. |
| Keywords: | Multivariate stochastic volatility, asymmetric leverage, dynamic leverage, size effect, numerical likelihood, Bayesian Markov chain Monte Carlo, importance sampling. |
| Date: | 2005–11 |
| URL: | http://d.repec.org/n?u=RePEc:ubi:deawps:12&r=ict |
| By: | Chin-Shien Lin (National Chung Hsing University); Haider A. Khan (GIGS, University of Denver); Ying-Chieh Wang (Providence University); Ruei-Yuan Chang (Providence University) |
| Abstract: | This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises. |
| Date: | 2006–04 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2006cf411&r=ict |