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Найдено 11 публикаций
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Статья
Maggi M., Fantazzini D., DeGiuli M. et al. Applied Economics. 2010. No. 42 (25). P. 3267-3277.
Добавлено: 14 января 2011
Статья
V. Alevskii, V.Chetverikov. Applied Economics. 2016. Vol. 48. No. 25. P. 2333-2340.
Добавлено: 2 июля 2017
Статья
Victor Lapshin, Vadim Kaushanskiy. Applied Economics. 2016. Vol. 48. No. 58. P. 5654-5666.
Добавлено: 23 мая 2016
Статья
Poldin O. V., Valeeva D., Yudkevich M. M. Applied Economics. 2015. Vol. 47. No. 44. P. 4728-4740.

Social influence is an important factor in learning and decision-making. We estimate peer influence on student choice of specialization using data on undergraduate students of a Russian university. Information about individual social ties has been gathered from a questionnaire survey. We show that specialization choice is significantly influenced by friends as well as by study partners. The strongest effect is produced by friends who are study partners and those who have similar academic achievements. Reciprocal friendship ties have a stronger influence on the choice than nonreciprocal ones. Also, the decision is affected by classmates with similar academic achievement. The results allow us to better understand the mechanisms of peer effects in the specialization choice.

Добавлено: 2 июля 2015
Статья
Gerry C., Li C. A. Applied Economics. 2010. Vol. 42. No. 16.
Добавлено: 25 ноября 2015
Статья
Djankov S., La Porta R., López-de-Silanes F. et al. Applied Economics. 2010. Vol. 2 (2). P. 179-209.
Добавлено: 30 сентября 2014
Статья
Liu H. Y., Deseatnicov I. Applied Economics. 2016. Vol. 48. No. 51. P. 4961-4976.
Добавлено: 18 октября 2018
Статья
Besstremyannaya G. Applied Economics. 2015. Vol. 47. P. 6331-6361.
Добавлено: 27 сентября 2019
Статья
Parshakov P., Zavertiaeva M. A., Coates D. C. Applied Economics. 2018. Vol. 50. No. 47. P. 5064-5075.
Добавлено: 26 июня 2018
Статья
Zubanov N., Webbink H. D., Martin N. G. Applied Economics. 2013. Vol. 45. No. 12. P. 1583-1599.
Добавлено: 6 апреля 2021
Статья
Kholodilin K., Siliverstovs B. Applied Economics. 2017. Vol. 49. No. 42. P. 4271-4297.

In this article, we examine whether the local indicators are able to predict the city-level housing prices and rents better than national indicators. For this purpose, we assess the forecasting ability of 126 indicators and 21 types of forecast combinations using a sample of 71 large German cities. There are several predictors that are especially useful, namely price-to-rent ratios, national-level business confidence, and consumer surveys. We also find that combinations of individual forecasts are among the top forecasting models. On average, the forecast improvements attain about 20%, measured by a reduction in root mean square error, compared to the naive models.

Добавлено: 22 марта 2017