1. Building and Utilizing Macroeconomic Modeling for Policy Purposes: An Overview by Atchana Waiquamdee Deputy Governor Bank of Thailand Presented at the SEACEN-CCBS/BOE-BSP Workshop on Dynamic Stochastic General Equilibrium Modeling and Econometric Techniques November 23–27, 2009 Manila, Philippines

2. 22222  Introduction  Macro models and DSGE model  Building DSGE model  Challenges in macro modeling  Conclusion 2 Outline of Presentation

3. 33333 1. Introduction 3

4. 4 The Long History of Economic Models  Analogies – clear your thinking –Adam Smith’s pin factory  Early analytical tools – simplify the issues –Alfred Marshall’s demand/supply, Edgeworth’s box, Cournot’s model of competition, Hicks’ IS-LM analysis  Formal use of mathematics – retain the coherence –Micro: Game theory, General equilibrium –Macro: Dynamic models with optimizing and rational agents –Empirical: Econometrics, Time-series

5. 5 Two macro revolutions (1) Lucas critique  Role of monetary policy: anchoring inflation expectations  Expectations in previous macro models –Purely adaptive: based on what happened in the past  Rational expectations Agents do not make systematic errors when predicting the future –Model parameters in fact depend on agents’ expectations of policy –Would be naive to evaluate policies based on past aggregate data –To predict effects of policy experiment, we must model deep parameters that govern individual behavior

6. 6  Nevertheless, even in model with rational agents, role of monetary policy is limited –In reality, we do observe impacts of monetary policy in short run  Hence, emerging role of market imperfections –Imperfect competition –Nominal frictions: price and wage rigidities  Rational expectations and New Keynesian framework –Give rise to crucial role of macroeconomic stabilization policies –Form the basis for modern macroeconomic models Two macro revolutions (2) New Keynesian macroeconomics

7. 77777 2. An overview of macro models 7

8. 8 Generic uses of macro models  Examination of dynamics of macro variables –Output and its components, employment, prices  Macro models: –Articulate and expound theoretical ideas –Test different theories –Construct “what if” scenarios –Produce forecasts

9. 9 Using macro models in policymaking process  Policymaker must base decisions on limited understanding of the economy, a large and complex system  Models aim to simplify complex system but keep all salient features –Deep parameters capturing preferences, technology, and institution factors  Good models, for policy making, should… –Be backed by a coherent and logical theory –Replicate the statistical properties of the actual time series  Both criteria are necessary –Lack of a coherent theory leads to ‘Lucas critique’, spurious relationships, data mining, over-fitting, and model misspecification for example –Lack of statistical congruenceleads to poor forecasting

10. 10 Driven by theory Driven by data Portfolio of macro models in most central banks DSGE model Macroecono- metric model VAR Adapted from Pagan (2003), “Report on Modeling and Forecasting at the Bank of England,” Bank of England Quarterly Bulletin, (Spring), 60-88 ■ ■ ■ Small New-Keynesian model ■ 10 Different models have different comparative advantage in what is required by policymakers Those mainly driven by data: forecasting Those mainly driven by theory: policy analysis

11. 11 VAR  Use econometric procedures to obtain model parameters  Emphasize fitting historical data  Rely very little on theory

12. 12 Macroeconometric Model  Fit data to equations that are loosely based on theory  Typically use error correction mechanism, specifying –Long-run relationship: based on theory –Short-run relationship: guided by factors driving short-run dynamics  While linked to theory, long-run values –Do not depend on supply side –May not satisfy stock-flow consistency  Furthermore, model does not emphasize microeconomic agents and their expectations –Susceptible to Lucas critique

13. 13 Small New-Keynesian Model  This model borrows key results from New Keynesian economics that emphasizes market imperfections, especially, price rigidities  Model usually specified in terms of gaps—how much each variable is deviated from its long-run, equilibrium value  While model focuses on key aggregate relationships –IS relation, Phillips curve, Taylor rule, UIP condition they lack structural features that underline aggregate dynamics

14. 14 DSGE Model Neoclassical RBC models New Keynesian imperfections  Optimizing agents  Perfect competition  Monopolistic competition  No rigidities  Real and nominal rigidities New Neoclassical Synthesis DSGE Model = Sticky-Price RBC Model Long run Driving forces = real factors (e.g., technology shocks) Short run Monetary policy does have real effects because of price rigidities 14

15. 15 DSGE models characterize the workings of the economy  Rational expected utility- maximizing agents and forward-looking central bank  Both demand and supply sides of the economy  Stock-flows consistency between various variables in order to explain  Short-run business cycle fluctuations  Long-run growth path of the economy 15 Why DSGE Models?

16. 16 Why DSGE Models? DSGE models provide coherent framework for policy analysis and forecasting  Provide counterfactual experiments/simulations  Answer questions relating to structural changes  Identify sources of shocks in the economy  Forecast and predict the effects of policy changes  Evaluate alternative policies 16

17. 17 What can DSGE offer the policymaker?  It aims at ‘understanding’ rather than just ‘forecasting’ –A rigorous structural model is required to uncover the underlying driving forces of economic development  It imposes logical discipline on the policymaker –Effects of monetary policy works in complex ways especially for small open economies; Interdependence of economic variables must be acknowledged  It is conceptually a natural tool for policy experiments –The framework is free from ‘Lucas critique’, and is therefore more stable for alternative policy experiments

18. 18 Traditional process Policymaking process Assumptions & risk assessment Standard Macro- econometric model Inputs Balance of risk & policy decision Tunings Forecasts Judgments With DSGE Assumptions & risk assessment DSGE model Inputs Balance of risk & optimal policy Tunings Forecasts Judgments Priors Scenarios evaluation Policy experiments Transmission linkages

19. 19 DSGE models at policy institutions 19 Institution/Mode l Key applications Federal Reserve Board SIGMA Increased government spending, rising home consumption demand, falling currency risk premiums, changes in foreign demand, permanent productivity growth, reductions in labor and capital tax rates IMF GEM & GIMF Multi-country open economy; Fiscal analysis (GIMF) Central Bank of Chile MAS Contribution of shocks to business cycle, Effects of copper price shocks under different fiscal rules Riksbank RAMSES Scenario analysis given alternative interest rate paths Bank of Thailand BOTDSGE Macro-financial linkage: financial accelerators

20. 20 Applications of DSGE Models at policy institutions Examples of policy analysis A.Effectiveness of alternative monetary policy rules (Laxton and Pesenti, 2003) B.Effects of structural reform policies (Bayoumi, Laxton, and Pesenti, 2004) C.Natural rates of output and interest (Edge, Kiley, and Laforte, 2008) 20

21. 21 Two-country DSGE model used to assess effectiveness of Taylor rules in Czech Republic Key findings  Rules that perform well in closed economy also perform well in another closed economy  Yet, maybe inefficient when applied to small open economies  Such rules respond too weakly to forecasts of inflation and too strongly to movements in output gap  In small, open, emerging economies, a modified rule that responds strongly to inflation produces better macroeconomic performance 21 A. Effectiveness of Alternative Monetary Policy Rules

22. 22 Two-country DSGE model where price and wage markup parameters of euro area are lowered to U.S. levels to simulate impact of increasing product & labor market competition Key findings  Euro area output and welfare rises significantly  There are positive spillovers to the rest of the world  Structural reforms ease the task of monetary policymakers in euro area  Increased competition reduces nominal rigidities in euro area  Costs of stabilizing output reduced, thereby making it easier to use monetary policy in a counter- cyclical manner 22 B. Effects of structural reform policies

23. 23 A disaggregated DSGE model for U.S. economy studying historical evolution of natural rates of output and interest Potential output  Output that would prevail absent wage and price rigidities and abstracting from shocks to markups –Different from a more traditional view of potential output as a smoothly evolving series 23 C. Natural rates of output and interest

24. 24 In short, DSGE models facilitate assessing how structural features of the economy affect its responses to shocks  Reduced-form models If someone disagrees with predicted dynamics, it may be difficult to discuss why  VARs impose very little restrictions (just regressing on past data), making it difficult to identify what is behind model dynamics  Structural models are superior as they offer precise and coherent framework of where restrictions come from  If policymakers disagree with the results, it is possible for staff to identify what is behind them and fix them  Two-way interaction between policymakers and staff 24

25. 25 3. Building models for policy purposes 25

26. 26 General Approach and Objective  Identify key features/stylized facts of economy  Balanced growth path, steady-state ratios  Slow adjustments in prices and wages  Slow adjustments in GDP components, e.g. consumption, investment  Mimic those features by incorporating in models:  Calibrate key stylized facts, e.g. C/Y = 0.60  Nominal rigidities: contractual price/wage setting processes  Real rigidities: habit persistence, investment adjustment costs 26

27. 27 Househ olds  Consume and invest  Hire inputs and produce Model environment of a baseline DSGE model Agents What they do Features Firms Governme nt Central Bank  Supply labor to firms and set wage  Consumption habit persistence;  Monopolistic competitive labor market; wage rigidities  Monopolistic competitive market  Spend according to fiscal rule  Set interest rate according to monetary policy rule  Fixed proportion of nominal GDP  Inflation targeting 27  Invest  Investment adjustment costs  price rigidity

28. 28 Model consists of a)First-order conditions and policy rules b)Exogenous processes c)Market clearing conditions d)Steady-state conditions Transition dynamics N equations in N unknowns Transiti on dynamic s & terminal conditio ns Fully/par tially micro- founded behavior Dynamic path of model variables can be solved (Think of solving a system of differential equations) 3.1. Model structure 28

29. 29 1. Firm’s input demand problem 29 Optimal inputs demanded a) First-order conditions micro- founded behavior 3.1. Model structure

30. 30 2. Firm’s pricing problem Optimal price setting micro-founded behavior Price rigidity parameter Markup parameter character ize underlyin g structure of economy  Price in long run = markup over marginal cost  Price in short run depends on how difficult it is for firms to change prices 3.1. Model structure

31. 31 Productivity Intuition Productivity growth is a random walk with drift b) Exogenous processes Capital at any t Intuition Capital supplied by households = capital demanded by firms c) Market clearing conditions Capital in steady state Intuition Along balanced growth path capital-output share is constant d) Steady-state conditions 3.1. Model structure

32. 32 3.2. Model parameterization Two broad methods  Calibration Select parameters based on empirical findings that result in model that best characterizes Thai economy  Estimation Potential difficulties with likelihood –limited data –structural breaks –multiple local maxima 32

33. 33 4. Challenges and New Directions 33

34. 34  Reliance on individual rationality  Near-absence of heterogeneity  Simplifying assumptions: –Assuming perfect financial market: missing macro-financial linkage –Assuming Ricardian equivalence: no role of fiscal policy  Approximate model solution –Missing nonlinearities originally built in model Potential weaknesses in DSGE models

35. 35  Not enough emphasis on financial market imperfections –Standard models focus on real economy exclusively –Probably missing real-financial linkage in the economy  While some models study liquidity problems in financial markets –They do not explain why some assets that previously were very liquid suddenly stop being traded Possible improvement  Integrate financial imperfections in addition to standard nominal and real rigidities in macro models—recent study by Bank of Thailand  More on last day of Workshop 4.1. Models need to strike balance between simplicity and complexity

36. 36 4.2. Model solutions need to incorporate risks and uncertainty 36  Although DSGE models themselves admit nonlinearities, model solution relies on linearizing the model –We focus on small shocks in the neigbourhood of steady state –Buiter (2009): We took these models “into the basement and beat them with a rubber hose until they behaved”  In such linear quadratic framework equations describing economy are linear and objective function specifying policy goals is quadratic –Minimizing inflation and output volatility subject to linear model

37. 37  Under these assumptions, monetary policy is certainty equivalent –Policy responses do not depend on variances or any other aspect of probability distribution of shocks –Mishkin (2008): In such an environment, monetary policy “does not focus on risk management” Possible improvement  Solving model using higher-order approximation methods that retain information discarded by linearization (first- order approximation) 4.2. Model solutions need to incorporate risks and uncertainty

38. 38 4.3. DSGE models need to have good forecast performance 38 It was only until recently that DSGE models can  Track/forecast time series as well as VAR (Smets and Wouters, 2003)  Incorporate extraneous predictions and judgments consistent with underlying structure of the model (Benes et al., 2009)  Predict future path of nonmodeled variables (Schorfheide and Sills, 2009) These improvements will help  Utilize information on high-frequency (monthly) indicators in (quarterly) DSGE models  Gain more accuracy of initial condition of forecast  More on last day of Workshop

39. 39 We need macro models for policymaking process  Success of central banks rests in appropriate and timely response of monetary policy to disturbances to anchor inflation expectation  Role of modelers and forecasters is to provide clear framework of the working of economy and monetary policy transmission  Models help policymakers take appropriate action and communicate their decisions to public –Provide consistent framework to think about forecast and to evaluate alternative monetary policy