When and How to Support Renewables?—Letting the Data Speak - Green Energy and Efficiency: An Economic Perspective - page 301

Table 4 Results for solar photovoltaic
(Intercept) 2.849
pat_total_rooted 0.183
pat_total_rooted_lag2 0.071
pat_total_rooted_partsum1_lag1 0.003
pat_total_rooted_partsum2_lag1 0.022
rdd_solar_squared: dep_total_partsum3_lag3 0.050
dep_tech_lag3: rdd_res_squared_lag5 0.022
rdd_res_squared_lag5: dep_solar_partsum1_lag2 0.007
rdd_res_squared_lag5: dep_solar_partsum2_lag2 0.036
rdd_res_rooted_lag5: rdd_solar_rooted_lag4 0.336
rdd_res_rooted_lag5: rdd_solar_rooted_partsum1_lag3 0.000
Note Model chosen from >47,000 variables based on the lowest mean square error in predicting
the n ' th observation based on n − 1 data. Coef cients rounded at the third decimal digit. Number
of included variables limited to 25 during model selection

Fig. 8 Coefcients for solar patents at different lambda

patenting. RD&D between the second to sixth year (partsum5_lag2) seems to be
most effective.
The most interesting

nding in our view is that the effect of RD&D spending on
wind technologies gets substantially augmented when the deployment of wind
turbines on the continent is high (continent_dep_wind: rdd_wind). Again timing
matters, current deployment based on past RD&D spending coincides best with
patenting.