Lecture 15More on Real Business CyclesStart: MoneyNoah WilliamsUniversity of Wisconsin - MadisonEconomics 312Spring 2010Williams Economics 312Simulations from a Quantitative VersionWe have seen the qualitative behavior of the model,showing that the real business cycle model is consistentwith the data.Apart from the special case we studied last time, to fullysolve the model we need to use numerical methods.Calibrate the model: choose parameters to match some keyeconomic data.Example: set β so that steady state real interest ratematches US data.Program up on computer and simulate: use randomnumber generator to draw technology shocks, feed themthrough the model.Compute correlations and volatilities and compare to USdata.Williams Economics 312Figure 10.03 Small shocks and large cyclesAbel/Bernanke, Macroeconomics, © 2001 Addison Wesley Longman, Inc. All rights reservedWilliams Economics 3120 20 40 60 80 100 120 140 160 180 2000.3040.3060.3080.310.3120.3140.3160.3180.32LaborWilliams Economics 3120 20 40 60 80 100 120 140 160 180 2003.123.133.143.153.163.173.183.19CapitalWilliams Economics 3120 20 40 60 80 100 120 140 160 180 2000.50.520.540.560.580.60.620.64OutputWilliams Economics 3120 10 20 30 40 50 60 70 80 90 1000.99811.0021.0041.0061.0081.011.012Impulse ResponsesyklWilliams Economics 312Abel/Bernanke, Macroeconomics, © 2001 Addison Wesley Longman, Inc. All rights reservedFigure 10.01 Actual versus simulated volatilities of key macroeconomic variablesWilliams Economics 312Abel/Bernanke, Macroeconomics, © 2001 Addison Wesley Longman, Inc. All rights reservedFigure 10.02 Actual versus simulated correlations of key macroeconomic variables with GNPWilliams Economics 312Assessment of the Basic Real Business ModelIt accounts for a substantial amount of the observedfluctuations. Accounts for the covariances among a numberof variables. Has some problems accounting for hoursworked.Are fluctuations in TFP really productivity fluctuations?Factor utilization rates vary over the business cycle.During recessions, firms reduce the number of shifts.Similarly, firms are reluctant to fire trained workers.Neither is well-measured. They show up in the Solowresidual.There is no direct evidence of technology fluctuations.Is intertemporal labor supply really so elastic?All employment variation in the model is voluntary, drivenby intertemporal substitution.Deliberate monetary policy changes appear to have realeffects.Williams Economics 312Putting our Theory to Work I: the Great DepressionGreat Depression is a unique event in US history.Timing 1929-1933.Major changes in the US Economic policy: New Deal.Can we use the theory to think about it?Williams Economics 312Data on the Great DepressionYear u Y C I G i π1929 3.2 203.6 139.6 40.4 22.0 5.9 −1930 8.9 183.5 130.4 27.4 24.3 3.6 −2.61931 16.3 169.5 126.1 16.8 25.4 2.6 −10.11932 24.1 144.2 114.8 4.7 24.2 2.7 −9.31933 25.2 141.5 112.8 5.3 23.3 1.7 −2.21934 22.0 154.3 118.1 9.4 26.6 1.0 7.41935 20.3 169.5 125.5 18.0 27.0 0.8 0.91936 17.0 193.2 138.4 24.0 31.8 0.8 0.21937 14.3 203.2 143.1 29.9 30.8 0.9 4.21938 19.1 192.9 140.2 17.0 33.9 0.8 −1.31939 17.2 209.4 148.2 24.7 35.2 0.6 −1.6Williams Economics 312Output, Inputs and TFP During the Great DepressionTheory.AA=.YY− α.KK− (1 − α).LLData (1929=100)Year Y L K A1930 89.6 92.7 102.5 94.21931 80.7 83.7 103.2 91.21932 66.9 73.3 101.4 83.41933 65.3 73.5 98.4 81.9Why did TFP fall so much?Williams Economics 312Figure 1: Real Output, Consumption and Private Hours(Per Adult, Index 1929 =100)60657075808590951001051929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939YearsIndicesConsumptionOutputHoursWilliams Economics 312Williams Economics 312Potential ReasonsChanges in Capacity Utilization.Changes in Quality of Factor Inputs.Changes in Composition of Production.Labor Hoarding.Increasing Returns to Scale.Williams Economics 312Other Reasons for Great DepressionBased on Cole and Ohanian (1999)Monetary Shocks: Monetary contraction, change in reserverequirements too lateBanking Shocks: Banks that failed too smallFiscal Shocks: Government spending did rise (moderately)Sticky Nominal Wages: Probably more important forrecoveryWilliams Economics 312Output and Productivity after the Great DepressionCole and Ohanian (2001). Data (1929=100); data aredetrendedYear Y A1934 64.4 92.61935 67.9 96.61936 74.4 99.91937 75.7 100.51938 70.2 100.31939 73.2 103.1Fast Recovery of A, slow recovery of output. Why?Williams Economics 312Williams Economics 312Figure 2: Comparing Output in the Models to the Data50607080901001101933 1934 1935 1936 1937 1938 1939 1940YearIndicesDataCartelModelCompetitiveModelWilliams Economics 312Putting our Theory to Work II: ArgentinaHow to think about Argentina in 1998.According to the Wall Street Journal, 4/2/98,the IMFdispatched representatives to Argentina, to convince thegovernment to cool the economy.Reasons stated:(i) High growth rates (6.5 to 7% annually)following upon strong growth which started in 1990, onlyinterrupted briefly in 1995; (ii) Export prices falling; (iii)Trade deficit returning.Argentina even more interesting in 1990s boom. Grew fastfrom 1990 to 1998.Surprise: In light of high rate of TFP growth, standardmodel says investment should have been much larger in the1990s, and capital stock therefore much larger by the endof the decade.Williams Economics 312ARGENTINAGDP per working age person (Index)0.80.911.11.21.31.41.51.61.7195019601970198019902000Ln (GDP p. c.)1998Lost Decade Depression1990s boomWilliams Economics 312ARGENTINAGDP-0.4-0.3-0.2-0.100.10.20.30.4198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003Ln (GDP)DataModelWilliams Economics 312ARGENTINAGDP-0.4-0.3-0.2-0.100.10.20.30.4198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003Ln (GDP)DataModelModel restarted with 1999 capitalWilliams Economics 312ARGENTINACapital Input0.30.40.50.60.70.80.91198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003Ln (K)ModelDataModelWilliams Economics 312ARGENTINACapital Input0.30.40.50.60.70.80.91198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003Ln (K)DataModelModel restartedwith 1999 capital Williams Economics 312Explanations and ImplicationsPossible explanations:Measurement problems? Unlikely, gets similar results withalternative way of constructing capital-stock series.Time-inconsistency