1. Federal Planning Bureau Economic analyses and forecasts LIAM2* A sneak preview... *Life-cycle Income Analysis Model Gijs Dekkers Federal Planning Bureau and Katholieke Universiteit Leuven Sides made by Gaëtan de Menten Paper presented at the Paper to be presented at the « Tresury Brown Bag Lunch Meeting », Ministero dell'Economia e delle Finanze, Rome, February 14 th, 2011

2. Federal Planning Bureau Economic analyses and forecasts LIAM 2: the foundations  LIAM by Cathal O’Donoghue  Used in AIM-project, developing MIDAS for Belgium, Italy and Germany.  Updating, extending and considerable problem solving by Geert Bryon (FPB)  PROGRESS-MiDaL project (Grant VS/2009/0569)  FPP (Be): development, application and testing  Gaëtan de Menten: development  Geert Bryon: application and testing  Gijs Dekkers: management, data and a bit application  CEPS/INSTEAD (Lux): testing  IGSS (Lux): investment, testing  Cathal O’Donoghue (Teacasc, Ire), Howard Redway (minstry of work and pensions, uk): comments and conceptual assistance

3. Federal Planning Bureau Economic analyses and forecasts Overview of this sneak preview  Current features  Current performance  Demonstration  TODO(?)

4. Federal Planning Bureau Economic analyses and forecasts Current features  Input  Simulation: a text file  Alignment: CSV files  Initial field data: an hdf5 file  Output  hdf5 file  Converters  Old data format (Tab-separated text files) hdf5

5. Federal Planning Bureau Economic analyses and forecasts constants per_period: PARAMETER-NAME: float entities entity-name1 (e.g. Household): fields links processes entity-name2 (e.g. Person): fields links processes simulation: init: processes: entity: [list of processes, separated by commas] input: path: “path name” file: “file name.h5” output: path: “path name” file: “file name.h5” start_period: periods: The setup of a mode l

6. Federal Planning Bureau Economic analyses and forecasts Current features  Language  Python  High level, concise, readable, easy interface with C  Lots of 3 rd party libraries (especially scientific tools)  But uses some efficient (open source) libraries written mostly in C  Numpy  Numexpr  PyTables

7. Federal Planning Bureau Economic analyses and forecasts Current features  Can declare “fields” with a type  float, int, bool  Evaluate simple expressions  Arithmetic operators: +, -, *, /, **, %  0.51 * age + 0.023 * age ** 2 – 0.0012 * age ** 3  Comparison operators: =, >  age < 20  Boolean operators: and, or, not  not male and (age >= 15) and (age = 15) and (age <= 50)  Conditional expressions: if(condition, iftrue, iffalse)  if(age < 65, earnings, pension)

8. Federal Planning Bureau Economic analyses and forecasts Current features  Store fields  for each period (if the field is declared)  age: “age + 1”  as temporaries (the value is lost after each period)  ischild: “age < 18”  Macros (re-evaluated wherever they appears)  ISCHILD: “age < 18”  difference with temporaries:  ischild: "age < 18"  before1: “if(ischild, 1, 2)"  before2: “if(ISCHILD, 1, 2)“ # before1 == before2  age: "age + 1"  after1: "if(ischild, 1, 2)"  after2: "if(ISCHILD, 1, 2)“ # after1 != after2 !! # after1 == before 1

9. Federal Planning Bureau Economic analyses and forecasts Current features  Functions  Per individual  abs, log, exp  clip  0.25 * clip(age ** 3, 0, 100000)  round  round(age / 10.0, 2)  min/max  min(age, 99)  max(pension, benefit)

10. Federal Planning Bureau Economic analyses and forecasts Current features  Functions  Aggregate functions  grpcount, grpsum, grpavg, grpstd, grpmax, grpmin  abs(age - grpavg(age))  Normal: random numbers with a normal distribution  normal(loc=0.0, scale=grpstd(errsal))  Some functions accept a filter argument  abs(age - grpavg(age, filter=male), filter=not male) filter=not male)

11. Federal Planning Bureau Economic analyses and forecasts Current features  lag/value_for_period  Only simple expressions and explicitly saved aggregates for now  value_for_period(inwork and not male, 2002)  lag(sum_twr)  matching: match two sets of individuals (aka Marriage market)  matches individuals from set 1 with individuals from set 2  follow a particular order (given by an expression)  for each individual in set 1, computes the score of all (unmatched) individuals in set 2 and take the best scoring one  matching(set1filter=to_marry and not male, set2filter=to_marry and male, set2filter=to_marry and male, orderby=difficult_match) orderby=difficult_match)

12. Federal Planning Bureau Economic analyses and forecasts Current features  Many-to-one links  partner.age  grpavg(partner.age – age)  partner.father.age  partner.get(earnings + benefits)

13. Federal Planning Bureau Economic analyses and forecasts Current features  One-to-many links  countlink(link, filter)  countlink(persons)  countlink(children, age < 18)  sumlink(link, expr, filter)  sumlink(persons, earnings, age >= 18)  avglink(link, expr, filter)  avglink(children, age, not male)  minlink/maxlink(link, expr, filter)  minlink(children, age, not male)

14. Federal Planning Bureau Economic analyses and forecasts Current features  Regressions  Logit  logit_regr(expr, filter, align)  Continuous (expr + normal(0, 1) * mult + error)  cont_regr(expr, filter, align, mult, error_var)  Clipped continuous (always positive)  clip_regr(expr, filter, align, mult, error_var)  Log continuous (exponential of continuous)  log_regr(expr, filter, align, mult, error_var)  Alignment  Fixed percentage or 2 dimensional table in a csv file

15. Federal Planning Bureau Economic analyses and forecasts Current features  Lifecycle functions  new: create new individuals  new('person', filter=to_give_birth)  remove: remove individuals from the dataset  remove(dead)  remove(nb_persons == 0)  Miscellaneous functions  show: print anything to the console  show(grpcount(age >= 18))  show(grpcount(not dead), grpavg(age, filter=not dead))

16. Federal Planning Bureau Economic analyses and forecasts Current features (9/10)  Miscellaneous functions  dump: produce a table with the expressions given as argument  show(dump(age, age / 10, filter=id < 20))  groupby (aka “pivot table”): group individuals by their value for the given expressions, and optionally compute an expression for each group  show(groupby((age / 10, gender)))  show(groupby((agegroup, gender, inwork), grpcount()))  show(groupby(agegroup, grpavg(income)))  show(groupby((inwork, gender), id, filter=age < 10)  csv: write a table to a csv file  csv(dump(age, age / 10, gender), suffix=‘age’)  Show: interactive assessment of results: command line

17. Federal Planning Bureau Economic analyses and forecasts Current Performance  For a simple model:  birth (using alignment data from MIDAS)  chronic ill (using a fixed percentage alignment)  marriage market  earnings (using macro alignment)  Or at least what I think macro alignment is...  death (using alignment data from MIDAS)

18. Federal Planning Bureau Economic analyses and forecasts Current Performance  10,000 persons, 20 periods  2,65s (on a Dell latitude laptop computer)  100,000 persons, 20 periods  29s  1,000,000 persons, 20 periods  16 minutes 31s, of which approx. 83% is spent in the marriage market  ~180Mb RAM  897Mb output file  could be compressed if needed  For a complete model with 100,000 persons  probably under 10min

19. Federal Planning Bureau Economic analyses and forecasts Demonstration

20. Federal Planning Bureau Economic analyses and forecasts TODO  Automated tests (aka “unit tests”)  Documentation  User manual  Code  Speed optimizations  Clean-up the code

21. Federal Planning Bureau Economic analyses and forecasts Questions or comments?