08/10/2012   Project  Title  Goes  Here  Presenta(on  to:  INET@Oxford/CABDYN,  Said  Business  School,Oxford  26/02/2013  

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  

!200.0%

!100.0%

0.0%

100.0%

200.0%

300.0%

400.0%

1998%

1998%

1999%

2000%

2000%

2001%

2002%

2002%

2003%

2004%

2004%

2005%

2006%

2006%

2007%

2008%

2008%

2009%

2010%

2010%

2011%

2012%

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:  

SFC  MODELING.  WHAT  IS  IT?  Part  1/4  

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  

-240,000

-200,000

-160,000

-120,000

-80,000

02 03 04 05 06 07 08 09 10

Non-Financial Corporations

Net Financial Wealth (Assets - Liabilities)

-80,000

-40,000

0

40,000

80,000

02 03 04 05 06 07 08 09 10

Financial Corporations

-80,000

-60,000

-40,000

-20,000

0

20,000

02 03 04 05 06 07 08 09 10

General Gov ernment

40,000

60,000

80,000

100,000

120,000

140,000

02 03 04 05 06 07 08 09 10

Households

-300,000

-200,000

-100,000

0

100,000

02 03 04 05 06 07 08 09 10

Total Economy

-15,000

-10,000

-5,000

0

5,000

10,000

15,000

02 03 04 05 06 07 08 09 10

Non-Financial Corporations

Net financial Borrow ing/Lending

-60,000

-40,000

-20,000

0

20,000

02 03 04 05 06 07 08 09 10

Financial Corporations

-16,000

-12,000

-8,000

-4,000

0

4,000

02 03 04 05 06 07 08 09 10

General Gov ernment

-10,000

-5,000

0

5,000

10,000

15,000

02 03 04 05 06 07 08 09 10

Households

-10,000

-5,000

0

5,000

10,000

02 03 04 05 06 07 08 09 10

Total Economy

•  Constantly  balancing  completeness  off  against  complexity  

• Want  this  to  be  as  policy-­‐relevant  as  possible  

•  Es@ma@ng  SFC  models  is  very  hard,  especially  porpolio  balance  equa@ons.  

•  Consistency/Frequency/Bubble  issues/Transfer  pricing  

Data   Es@ma@on  

Equa@ons  Applica@on  

Issues/Problems  to  solve  

Real  world  balance  sheet.  

2011Q1Balance 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) 00 0 0 0 0

NFC FC G HH ROW

Simplified  

2011Q1Balance 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) 00 0 0 0 0

NFC FC G HH ROW

FINANCIAL'BALANCE'SHEET

IRISH'ECONOMY' ROW' ''

sum'INSTITUTIONAL'SECTOR'

NFCs' FCs' ' GG' ' HHs' 'A' L' A' L' A' L' A' L' A' L' '

Physical'capital' !!' ' ' ' ' ' ' ' ' ' !!''

FINANCIAL'INSTRUMENT'

Deposits' !!,!! ' ' ' !!

!' !!,!! ' ' !!,!

! ' ' !!,!! ' ' 0'

!!"!"# ' !!,!,!! ' ' !!,!,!! ' ' ' !!! ' !!,!,!! ' ' ' !!!' 0'Loans' ' !!,!! ' !!!' ' ' !!,!! ' ' !!,!! ' ' !!,!! ' 0'Equities' ' !!!' ' !!!' !!,!,!! ' ' !!,!,!! ' ' !!,!,!! ' ' 0'

Wealth'(AGL)'' ' !!' ' !!' ' !! ' ' !!' ' !!' −!!'Sum'(AGL)' 0' 0' 0' 0' 0' 0'

Theore@cal  Balance  Sheet  

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  Sta9c  Simula9on  • Calibra@on  • Sta@c  parameters  

• No  empirical  data  

• Coherent  macro  ra@o  criteria,  eg.  Debt/GDP  

Pure  Dynamic  Simula9on  • Calibra@on  • Dynamic  parameters  ∆  period  by  period  

• No  Empirical  data  

• Coherent  macro  ra@o  criteria,  e.g.  Debt/GDP  

Empirical  simula9on  • Empirical  calibra@on  

• Real  world  data  • Dynamic  parameters  

• Natural  macro  ra@o  coherent  

• More  constraints  in  calibra@on  

• Use  country  balance  sheets.  

Es9mated  SFC  Model  • No  balance  sheets,  par@al  es@ma@on  

• No  balance  sheets,  full  es@ma@on  

• Par@al  Balance  sheets,  full  es@ma@on.    

• Full  balance  sheets,  full  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  

SFC  +  ABM.  WHY?  Part  3/4.  

SFC  

Sectoral  

No  black  holes  

Avoids  lots  of  neoclassical  modeling  problems  

Focus  on  closures  

Needs  solu@on  methods  

ABM  

Much  more  developed,  connec@ons  to  complexity/network  theory/etc  

Individual  rather  than  sectoral  

Porpolio  es@ma@on/simula@on  v.  easy  

Models  agent  interac@ons  more  naturally  

Focus  on  empirical  regulari@es  eg  power  laws  

A  PRIMITIVE  SFC/ABM.  Part  4/4  

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!