|
on Information and Communication Technologies |
| By: | Nicholas Oulton; Sylaja Srinivasan |
| Abstract: | We use a new industry-level dataset to quantify the role of ICT in explaining productivitygrowth in the UK, 1970-2000. The dataset is for 34 industries covering the whole economy(31 in the market sector). Using growth accounting, we find that ICT capital played anincreasingly important, and in the 1990s the dominant, role in accounting for labourproductivity growth in the market sector. Econometric evidence also supports an importantrole for ICT. We also find econometric evidence that a boom in complementary investment inthe 1990s could have led to a decline in the conventional measure of TFP growth. |
| Keywords: | productivity, TFP, ICT |
| JEL: | O47 O52 D24 |
| Date: | 2005–03 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp0681&r=ict |
| By: | Dale W. Jorgenson; Kazuyuki Motohashi |
| Abstract: | In this paper we compare sources of economic growth in Japan and the United States from 1975 through 2003, focusing on the role of information technology (IT). We have adjusted Japanese data to conform to U.S. definitions in order to provide a rigorous comparison between the two economies. The adjusted data show that the share of the Japanese gross domestic product devoted to investment in computers, telecommunications equipment, and software rose sharply after 1995. The contribution of total factor productivity growth from the IT sector in Japan also increased, while the contributions of labor input and productivity growth from the Non-IT sector lagged far behind the United States. Our projection of potential economic growth in Japan from for the next decade is substantially below that in the United States, mainly due to slower growth of labor input. Our projections of labor productivity growth in the two economies are much more similar. |
| JEL: | D24 D30 O57 |
| Date: | 2005–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:11801&r=ict |
| By: | Wolfgang Härdle; Rouslan A. Moro; Dorothea Schäfer |
| Abstract: | The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power. |
| Keywords: | support vector machine, classification method, statistical learning theory, electric load prediction, optical character recognition, predicting bankruptcy, risk classification |
| JEL: | C40 G10 |
| Date: | 2005–03 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2005-009&r=ict |