TY - CPAPER
KW - Technology
KW - Experience curves
KW - Innovation
KW - Technological innovation
KW - Hindcasting
KW - Learning curves
KW - Performance curve
KW - Production models
KW - Technological improvements
KW - Technological progress
KW - Technology evolution
KW - Technology improvement
KW - Upper Bound
KW - Commerce
KW - Technological forecasting
AU - A.G Bailey
AU - Q.M Bui
AU - J.D Farmer
AU - R.M Margolis
AU - Ramamoorthy Ramesh
AB - Using a database of sixty-two different technologies, we study the issue of forecasting technological progress. We do so using the following methodology: pretending to be at a given time in the past, we forecast technology prices for years up to present day. Since our forecasts are in the past, we refer to it as hindcasting and analyze the predictions relative to what happened historically. We use hindcasting to evaluate a variety of different hypotheses for technological improvement. Our results indicate that forecasts using production are better than those using time. This conclusion is robust when analyzing randomly chosen subsets of our technology database. We then turn to investigating the interdependence of revenue and technological progress. We derive analytically an upper bound to the rate of technology improvement given the condition of increasing revenue and show empirically that all technologies fall within our derived bound. Our results suggest the observed advantage of using production models for forecasting is due in part to the direct relationship between production and revenue. © 2012 Gesellschaft fuer Informatk.
BT - ARCS Workshops, ARCS 2012
LA - eng
N1 - cited By 2
N2 - Using a database of sixty-two different technologies, we study the issue of forecasting technological progress. We do so using the following methodology: pretending to be at a given time in the past, we forecast technology prices for years up to present day. Since our forecasts are in the past, we refer to it as hindcasting and analyze the predictions relative to what happened historically. We use hindcasting to evaluate a variety of different hypotheses for technological improvement. Our results indicate that forecasts using production are better than those using time. This conclusion is robust when analyzing randomly chosen subsets of our technology database. We then turn to investigating the interdependence of revenue and technological progress. We derive analytically an upper bound to the rate of technology improvement given the condition of increasing revenue and show empirically that all technologies fall within our derived bound. Our results suggest the observed advantage of using production models for forecasting is due in part to the direct relationship between production and revenue. © 2012 Gesellschaft fuer Informatk.
PY - 2012
SN - 9781467319133
T2 - ARCS Workshops, ARCS 2012
T3 - ARCS Workshops, ARCS 2012
TI - Forecasting technological innovation
ER -