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Stock flow modelling and agent based modelling
 

Stock flow modelling and agent based modelling

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Stock flow modeling and agent based modelling

Stock flow modeling and agent based modelling

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    Stock flow modelling and agent based modelling Stock flow modelling and agent based modelling Presentation Transcript

    • Presenta(on  to:  INET@Oxford/CABDYN,  Said  08/10/2012   Project  Title  Goes  Here   School,Oxford  26/02/2013   Business  
    • Agent  based  models  and  stock  flow   consistent  models:  a  coherent   alterna@ve?   Stephen  Kinsella   University  of  Limerick  
    • •  Funded  with  a  series  of  grants  from  the   Ins@tute  for  New  Economic  Thinking,  INET,   Rannis,  Sta@s@cs  Iceland,  and  Irish  Research   Council.    •  Overarching  goal  is  to  build  a  workable  model   comparable  to  models  used  in  CBs/Govt   Departments  for  policy  evalua@on/ counterfactual  scenario  tes@ng.  
    • Today  •  Context.  •  Stock  flow  consistent  methodology.  What  is  it?    •  SFC+ABM:  Why  connect  SFC  models  to  agent   Based  Models?    •  2  Applica@ons   – Irish  INET  model  basics   •  Irish  economic  situa@on  from  a  balance   sheet  perspec@ve   – SFC  ABM  (Kinsella  et  al,  EEJ,  2011)  •  Plan  of  Further  Work  
    • Context:  Irish  Output  and   Unemployment:  Not  good  400.0%300.0%200.0%100.0% 0.0% % % % % % % % % % % % % % % % % % % % % % % 98 98 99 01 05 07 08 08 09 11 00 00 02 02 03 04 04 06 06 10 10 12 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20!100.0%!200.0% Constant%Price%Gross%Domes<c%Product%Index%2005:Q1=100,%Quarterly,%Seasonally%Adjusted% Unemployment%Level:%Survey!Based,%Index%2005:Q1=100,%Quarterly,%Seasonally%Adjusted%
    • Sans  Mul@na@onals:  
    • Part  1/4  SFC  MODELING.  WHAT  IS  IT?  
    • SFC:  Horrible  name,  good  idea.  •  Tobin   (1982)   in   his   Nobel   Lecture   and   Godley   and   Lavoie   (2007),  illustrated  the  generality  of  these  concepts  by  se`ng   out  a  model  of  the  economy  based  on  a  flow-­‐of-­‐funds  matrix.    •  Each   column   shows   a   sector’s   balance   sheet   (for   stocks)   or   sources  and  uses  of  funds  (flows).    •  Meanwhile,  a  row  shows  the  stock  or  flow  of  an  asset  as  it  is   distributed  among  the  supplying  and  demanding  sectors.    •  Approach   now   common   in   simula@ng   models,   but   macro-­‐ econometric   applica@ons   are   scarce   because   of   the   consistency   of   the   data   mainly   from   balance   sheet   with   those   of  the  real  economy  (Na@onal  Accounts).  
    • Stock  flow  consistent  models    •  Morris  A.  Copeland  (1949)  is  the  father  of  the  Flow  of  Funds   accoun@ng.    (Federal  Reserve  Bureau  Z.1  Release).    •  Copeland’s  idea  was  to  enlarge  the  social  accoun@ng   perspec@ve  -­‐  up  to  that  moment  used  mainly  in  the  study  of   na@onal  income  -­‐  to  the  study  of  money  flows.  •  Essen@ally  trying  to  find  an  answer  to  fundamental  economic   ques@on:    ‘when  total  purchases  of  our  na@onal  product  increase,  where  does  the   money  come  from  to  finance  them?  When  purchases  of  our  na@onal   product  decline,  what  becomes  of  the  money  that  is  not   spent?’  (Copeland,  1949,  p.  254)    
    • Tobin  1982,  Nobel  Lecture   These  models  should  have   1.  Precision  regarding  @me.   2.  Tracking  of  stocks.   3.  Several  assets  and  rates  of  return.   4.  Modeling  of  financial  and  monetary  policy   opera@ons.     5.  Walras’s  Law  and  adding  up  constraints.    J.  Tobin.  Money  and  finance  in  the  macroeconomic  process.  Journal  of  Money,  Credit  and  Banking,  14(2):171–204,  1982.  
    • Joan  Robinson  “Before  a  model  can  be  confronted  with  empirical  tests,  it  has  to  be  examined  for  internal  consistency  and  for  the  a  priori  plausibility  of  its  assump@ons”   -­‐-­‐-­‐Joan  Robinson,  What  are  the  quesFons?  JEL   14(4)  1977,  pp.  1319-­‐1320.    
    • Godley  &  Lavoie  •  Sectoral  models  •  Set  up  balance  and  transac@on  matrices  •  Build  a  model’s  equa@ons  from  the  balance  sheet   rela@ons  (Behavioural  and  Iden@ty  rela@ons)  •  Solve  for  steady  state  •  Shock  using  ‘policy  experiments’  through   simula@on.  •  Lem  open  the  ques@on  of  es@ma@ng  these   models.  •  This  is  my  group’s  central  problem.  
    • Evolu@on  of  stock  flow  models:  sectors  Godin  et  al,  2013  Stock  flow  consistent  modeling  through  the  ages,  Levy  Ins@tute  WP  745  
    • Evolu@on  of  Stock  Flow  Models:  Assets  
    • Net Financial Wealth (Assets - Liabilities) Non-Financial Corporations Financial Corporations -80,000 80,000-120,000 40,000-160,000 0-200,000 -40,000-240,000 -80,000 02 03 04 05 06 07 08 09 10 02 03 04 05 06 07 08 09 10 General Gov ernment Households 20,000 140,000 0 120,000 -20,000 100,000 -40,000 80,000 -60,000 60,000 -80,000 40,000 02 03 04 05 06 07 08 09 10 02 03 04 05 06 07 08 09 10 Total Economy100,000 0-100,000-200,000-300,000 02 03 04 05 06 07 08 09 10
    • Net financial Borrow ing/Lending Non-Financial Corporations Financial Corporations15,000 20,00010,000 0 5,000 0 -20,000 -5,000 -40,000-10,000-15,000 -60,000 02 03 04 05 06 07 08 09 10 02 03 04 05 06 07 08 09 10 General Gov ernment Households 4,000 15,000 0 10,000 -4,000 5,000 -8,000 0-12,000 -5,000-16,000 -10,000 02 03 04 05 06 07 08 09 10 02 03 04 05 06 07 08 09 10 Total Economy10,000 5,000 0 -5,000-10,000 02 03 04 05 06 07 08 09 10
    • Issues/Problems  to  solve  •  Consistency/ •  Es@ma@ng  SFC   Frequency/Bubble   models  is  very   issues/Transfer   hard,  especially   pricing   porpolio  balance   equa@ons.   Data   Es@ma@on   Applica@on   Equa@ons  •  Want  this  to  be  as   •  Constantly   policy-­‐relevant  as   balancing   possible   completeness  off   against  complexity  
    • Real  world  balance  sheet.  2011Q1 NFC FC G HH ROWBalance sheet A L A L A L A L A LG & SDRs 841 841Deposits 34,461 358,423 17,907 122,776 183,280 1Bonds 233 451,093 69,945 455 381,371 -1Loans 84,852 602,826 46,207 184,912 286,855 0Equities 150,940 557,115 17,539 46,261 644,255 0ITR 3,511 208,755 125,895 79,349 0Other 10,489 1,045 2,304 5,553 14,783 0Wealth (A-L) -208,542 -70,578 -78,402 104,922 253,441 -841Sum (A-L) 0 0 0 0 0 0
    • Simplified  2011Q1 NFC FC G HH ROWBalance sheet A L A L A L A L A LDeposits 34,461 358,423 17,907 122,776 183,280 1Bonds 233 451,093 69,945 455 381,371 -1Loans 84,852 602,826 46,207 184,912 286,855 0Equities 150,940 557,115 17,539 46,261 644,255 0Wealth (A-L) -201,564 138,381 -80,706 -15,420 159,309 0Sum (A-L) 0 0 0 0 0 0
    • Theore@cal  Balance  Sheet   FINANCIAL IRISHECONOMY ROW BALANCESHEET INSTITUTIONALSECTOR NFCs FCs GG HHs sum A L A L A L A L A L !Physicalcapital ! !! ! ! ! ! ! Deposits !!,! !! !!,! !!,! !!,! 0 ! ! ! ! ! FINANCIAL !!"!"# !!,!,! !!,!,! !! !!,!,! !! 0INSTRUMENT Loans !! !! !,! ! !! !,! !! !,! !! 0 !,! ! ! ! ! ! Equities !! !! !!,!,! !!,!,! !!,!,! 0Wealth(AGL) !! !! !! !! ! ! −! ! Sum(AGL) 0 0 0 0 0 0
    • Simula@on  studies  •  Kinsella  &  Khalil  2011  Debt  Defla@on  Traps   within  Small  Open  Economies  •  Kinsella  &  Khalil  2011  Bad  Banks  Choking   Good  Banks:  Simula@ng  Balance  Sheet   Contagion  •  Kinsella  &  Godin  2012  Leverage,  Liquidity  and   Crisis:  A  Simula@on  Study  
    • Es@ma@on  Studies  •  O’Shea  &  Kinsella  (2010)  Solu@on  and  Simula@on   of  Large  Stock  Flow  Consistent  Monetary   Produc@on  Models  Via  the  Gauss  Seidel   Algorithm  •  Godin  et  al  (2012)  Method  to  Simultaneously   Determine  Stock,  Flow,  and  Parameter  Values  in   Large  Stock  Flow  Consistent  Models  •  Work  in  progress  w/  Rudi  Von  Arim  (UTAH)  on   ‘solving’  and  studying  SFC  matrices  numerically.    
    • Agent  based  Studies  •  Kinsella,  Greiff  &  Nell  Income  Distribu@on  in  a   Stock-­‐Flow  Consistent  Model  with  Educa@on   and  Technological  Change  Eastern  Economic   Journal,  Vol.  37,  Issue  1,  pp.  134-­‐149,  2011  •  New  IRC  &  INET  grants  w/  Mauro  Gallega@  &   Joe  S@glitz  to  bring  ABM  approach  closer  to   SFC  &  Vice  versa.  
    • Pure  Dynamic   Empirical   Es9mated  SFC  Pure  Sta9c   Simula9on   simula9on   Model  Simula9on   •  Calibra@on   •  Empirical   •  No  balance  •  Calibra@on   •  Dynamic   calibra@on   sheets,  par@al  •  Sta@c   parameters  ∆   •  Real  world  data   es@ma@on   parameters   period  by   •  Dynamic   •  No  balance  •  No  empirical   period   parameters   sheets,  full   data   •  No  Empirical   •  Natural  macro   es@ma@on  •  Coherent   data   ra@o  coherent   •  Par@al  Balance   macro  ra@o   criteria,  eg.   •  Coherent   •  More   sheets,  full   Debt/GDP   macro  ra@o   constraints  in   es@ma@on.     criteria,  e.g.   calibra@on   •  Full  balance   Debt/GDP   •  Use  country   sheets,  full   balance  sheets.   es@ma@on.    
    • A  word  on  closures.  Lance  Taylor  (1991:  41):  ‘Formally,  prescribing  closure  boils  down  to  sta@ng  which  variables  are  endogenous  or  exogenous  in  an  equa@on  system  largely  based  upon  macroeconomic  accoun@ng  iden@@es,  and  figuring  out  how  they  influence  one  another  ...  .  A  sense  of  ins@tu@ons  and  history  necessarily  enters  into  any  serious  discussion  of  macro  causality’      
    • Adjustment  Processes.  The  adjustment  processes  within  the  model  towards  the  steady  state  will  be  based  on  simple  reac@on  func@ons  to  disequilibria.      
    • Note  that  the  empirical  values  for  adjusted  GDP,  and  GNP,  are  not  directly  comparable  to  standard  SNA  95  defini@ons.  An  example  will  show  you  why.    
    • Shock  &  Results  
    • Part  3/4.  SFC  +  ABM.  WHY?  
    • SFC   ABM   Much  more  developed,  connec@ons  to   Sectoral   complexity/network  theory/etc   No  black  holes   Individual  rather  than  sectoral   Avoids  lots  of  neoclassical  modeling   Porpolio  es@ma@on/simula@on  v.  easy   problems   Focus  on  closures   Models  agent  interac@ons  more  naturally   Focus  on  empirical  regulari@es  eg  power   Needs  solu@on  methods   laws  
    • Part  4/4  A  PRIMITIVE  SFC/ABM.  
    • SFC  model  with  interac@ng  agents  •  4  Sectors,  households,  firms,  banks,  government.  •  Workers...     –  search  for  work.     –  work  for  a  wage  or  get  dole.     –  spend  money  on  consump@on.     –  spend  money  on  educa@on.    •  Firms...     –  hire  workers.     –  pay  wages.     –  receive  revenue  from  selling  output.    •  Government:  collects  taxes  and  provides  dole.    •  Banks  lend  out,  can  go  broke.  •  Model  allows  for  changes  in  educa@on/employment/income/ wealth  
    • Nice  features  •  no  representa@ve  agent  •  no  u@lity  func@on  •  no  ra@onal  expecta@ons  •  large  number  of  heterogeneous  agents  •  individual  behavior  is  unpredictable  •  individuals  follow  simple  rules    •  indeterminacy  at  the  micro  level  (random   selec@on  from  a  given  distribu@on)    •  SFC  Adding  up  constraints.    
    • Movie.  
    • Cool  stuff:  Measuring  Mobility  •  Via  G.S.  Fields  &  E.A.   Ok,  “Measuring   Movement  of   Income”,  Economica   (1999).    •  Mb=1/N*∑  |log   m_{0}−log  m_{1}|  •  Implies  Higher   savings  →  lower   mobility.      
    • Conclusion  &  Further  Work    •  Promising  connec@ons/crossovers  •  Benchmark  model  to  be  built,  an  INET  group   exists  for  this  now.    •  Lots  of  unexplored  areas,  open  ques@ons,  low   hanging  and  high-­‐hanging  fruit.    •  Fun  @mes  ahead!