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Regular version of the site
Contacts

St. Petersburg, 194100, Russia
3, Kantemirovskaya st.

Tel.: +7 (812) 644-59-11

Administration
Book
Innovating for the Middle of the Pyramid in Emerging Countries

Ljubica J.

Cambridge: Cambridge University Press, 2021.

Book chapter
How SaaS Companies Price Their Products: Insights from an Industry Study

Saltan A., Smolander K.

In bk.: Software Business. ICSOB 2020. Vol. 407. Springer, 2021. Ch. 1. P. 1-13.

Working paper
On the effects of income heterogeneity in monopolistically competitive markets

Kichko S., Picard P. M.

Economics/EC. WP BRP. Высшая школа экономики, 2020. No. 239.

Brown Bag SEMinar+ November 26th, 2020 15:00 – 16:00 ZOOM

All SEM faculty and students are welcome to join the next Brown Bag SEMinar at November 26. Stanislav Rogachev, second year doctoral student of SEM, will do a talk related to the issues of nonlinearity in labour share forecasting.

Preregistration is needed via link:  https://forms.gle/CX61xMufTkcgjsur8

Zoom meeting link:

https://us02web.zoom.us/j/86361701148?pwd=dmhmRUJEbzBtUE1aRC9jVzJCbEJIQT09

Conference ID: 863 6170 1148

Code: 922407

November 26

15:00 - 16:00 // Research in progress presentation

Title:  Nonlinearity in Labour Share Forecasting – Intersectoral Approach

Author: Stanislav Rogachev, 2nd-year PHD student, HSE St.Petersburg

Area of Scientific Interests: Labour Economics, Econometrics

Abstract: We forecast labour share for 18 KLEMS-classified economic sectors, 12 European countries. The choice is driven by data availability. For each sector 11 specifications of time component in CES production function with factor augmenting technical change are tested. This includes comparing models with linear, nonlinear time and the same with structural breaks. Then, three degrees of models ‘power’ are proposed to characterize whether a model is consistent and valid for prediction. Here, residuals stationarity and autocorrelation are investigated as well as regressors and structural breaks statistical significance. Finally, 3D cube is visualized (dimensions: country-sector-model) to outline predictive power of models valid for forecasting.

To sum up main results, models with structural break in nonlinear time component show better predictive power according to the derived criteria. Next, overall labour share decline cannot be stated as only 7 sectors out of 18 have decreasing trend in more than one third of cases (countries). Additionally, each country sectors are grouped by LS forecast average value into four interval categories. The modal values of these intervals are derived to obtain general understanding of average future value for a certain sector. The last but not the least, forecasts with identical LS trends were combined for the purpose of generalizing.

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Event Calendar: 

https://calendar.google.com/calendar/u/0/embed?src=lrrvusehk59oii9l4paccvdot8@group.calendar.google.com&ctz=Europe/Moscow

Forthcoming events:

November – Topic TBA, Dmitry Rudenko, Associate Professor of the Department of Economics HSE StP