Simulates a time-varying treatment effect that starts at zero in time period zero, then linearly increases to a 'full treatment' effect, based on analyst-provided choices concerning time until full treatment effect and 'speed'
Usage
exposure_list(
sampled_time_period,
mo,
available_periods,
policy_speed,
n_implementation_periods
)Arguments
- sampled_time_period
Year that treatment is first enacted
- mo
Month that treatment is first enacted
- available_periods
Maximum number of time periods in the data (e.g. if policy is between 1950-2000, then available_periods == 50)
- policy_speed
A string which is either "instant" for the policy going into immediate effect or "slow" for the policy effect phasing in linearly across n_implement_periods
- n_implementation_periods
Number of periods until full treatment effect is applied. Only used if policy_speed is 'slow'.
Value
A list, containing a vector of policy years of implementation, an integer of the starting policy implementation month, and the effect of treatment within a given implementation year (as a fraction of the total policy effect)
Examples
# Set up a policy that starts in first-year of data, in July and takes
# 2 years for full implementation:
exposure_list(1, 7, 3, policy_speed = 'slow', n_implementation_periods = 2)
#> $policy_years
#> [1] 1 2 3
#>
#> $policy_month
#> [1] 7
#>
#> $exposure
#> [1] 0.0625000 0.5625000 0.9270833
#>
# Same scenario but effect happens instantaneously:
exposure_list(1, 7, 3, policy_speed = 'instant')
#> $policy_years
#> [1] 1 2 3
#>
#> $policy_month
#> [1] 7
#>
#> $exposure
#> [1] 0.5 1.0 1.0
#>