Chapter 3. Introduction to Quantitative Macroeconomics. Measuring Business Cycles. Yann Algan Master EPP M1, 2010

Transcription

1 Chapter 3 Introduction to Quantitative Macroeconomics Measuring Business Cycles Yann Algan Master EPP M1, 2010

2 1. Traditional framework for Fluctuations Undergraduate textbook model Divide markets into good market, financial market and labor market Write equilibrium condition : IS, LM, and Ph. Curve (AD-AS) IS: LM PC C = cy and I = I(Y,r) Y = Y(Y,r,T) +G M/P = L(i,Y) + g(y,z)

3 Implications for understanding fluctuations SR: Prices are given, IS-LM determines Demand and Output MR : AS is only determined by real factors and Output at its natural level Money increases output in the SR but not in the LR Fiscal expansion increases output in the SR. may decrease it in the LR

4 What are the shortcuts of this model? Lack of microfoundations Hard to do welfare analysis without explicit utility and motives of agents Key role of uncertainty and expectations Leave unexplained a lot of puzzles in SR analysis Example 1: Boom in the 90 s in the US - Public deficit Contraction ( - 0.2% in 1997, + 2.3% in 2000) - International crisis and drop in household income (g=7% in 1997 and 4% in 2000) - But consumption remains stable (g=4%): C=cY? - Increase in investment (g=14%): I=aY?

5 Example 2: Microfoundations of price rigidities -Keynesian/Disequilibrium theories rely on nominal rigidities - But why won t entrepreneurs adjust their price if they could increase their profits? Other argument than irrationality? What would be the reaction of price-setting and wage-setting to modification in public policies? Lucas critique Bring back theories to systematic confrontation with the data

6 Major reconstruction Real business cycle revolution in the 80s - Theoretical refoundations Fluctuations as the results of optimal answers of agents to modifications of the environment + Technological and real shocks - Methodological revolution: measuring fluctuations and confrontation to the data

7 Introducing money but with micro foundations - Positive analysis of the effect of monetary policy by taking into account of optimal reaction of households and firms - Normative analysis of the welfare costs of inflation Real imperfections: information, transaction costs Wage bargaining and real rigidities Equilibrium unemployment and Job creation-destructions

8 2. Measuring Fluctuations Want to know general characteristics of fluctuations How long typical recessions or booms last? Are fluctuations in output and employment transitory or permanent? How do C, I, Unemp vary with output? How do we explain job creation-job destruction process? How do nominal variables, financial assets move with output?

9 2.1 Business Cycles: regularities in fluctuations Traditional approach: Historical approach: ex. of Kondratieff process every 50 years Burns and Mitchell (1946): first systematic time series study of peak and through in history and characterization of the mean lengths and the amplitude of fluctuations Ex. : Friedman and Schwartz (1963): Monetary History Modern approach: - Integration of macro-economy and econometrics - Quantification of the statistical properties of the series

10 In search for regularities: covariance stationarity «Business cycles» : room for characterizing typical facts only if things repeat themselves to a certain extent, with regularities Concept of covariance stationary Possible to estimate the moments, the process of a random variable Y iff it displays covariance stationary, that is

11 Reasonable assumptions? Sometimes not: Crisis 2008, Great Depression, Transition Economy, or European Unemployment, Inflation Sometime yes: Typical example: post war GDP (not the original time-serie since it trends up, but a transformation of it)

12 Unemployment fluctuations

13 US Unemployment rate

14 Inflation

15 Unemployment fluctuations

16 GDP

17 Wages are a-cyclical or slightly pro-cyclical What kind of theories do we need to account for such fluctuations?

18 Question: Why do economists take the log of the series? Assume constant growth rate g The log-gdp reports directly the growth rate as the slope of the series

19 2.2 Trend versus Cycles Wold Decomposition and ARMA representation If a series Yt is co-variance stationary, then it can be represented by a Wold decomposition (MA representation)

20 Very convenient!! -The Wold representation may be not the true process but even highly non-linear process have an infinite MA Wold decomposition - Infinite MA cannot be estimated but can be approximated by ARMA(n,m) process or AR(n) process, thus allowing to estimate the process of the series: correlations, cross-correlations Ex.: AR(1)

21 Identifying the cycle part of the series

22 Cycle : Output Gap

23 Cycle : Growth cycle But rough filter!! (Jumpy series since all variations of the data longer than one quarter are filtered out)

24 Trend Cycle (1)

25 Trend Cycle (2). Some key flaws!! Trend stationarity versus Difference Stationarity Nelson-Plosser (1982): Spurious identification if the series is stationary in first difference rather than in level In this case: no clean separation between trend and cycle Ex. DS : Random Walk with drift With

26 Thus Stochastic trend Implications Trend Stationary: a one-period shock has only a transitory effect Difference stationary: a one-period shock has a permanent effect Sources of long-term growth (technological progress) and short-term fluctuations are indistinguishable We need a filter that eliminates long-run components!!

27 HP Filter

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32 2.3 Fluctuations in GDP and components Representation of the output fluctuation process y: log deviation from a trend Cyclical part well fitted by an AR(2)

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34 Volatility of output components - Lower volatility of consumption: smoothing effect? - Investment five times more volatile than output: key dimension of fluctuations - Hours less volatile (hiring or destructions costs?) - Wages much less volatile (wage rigidity?)

35 2.4 Comovements between output and components Comovements between cyclical behavior of output and its component Contemporaneous correlation and leads and lags

36 Strong correlation between consumption, investment and output Output and Consumption Output and Investment What implications for a theory of fluctuations?

37 Little correlation with exports Little correlation with governement spending

38 Comovement with employment -Correlation high and positive Puzzling? Think about the leisure consumption trade-off - Highly positive lags: movement in output, then employment - Adjustment on the intensive margin first (correlation is the same as output) and extensive margins (hiring) second

39 High correlation with factor productivity (TFP and labor productivity) Wages a-cyclical Puzzling? Keynes/Tarshis discussion. Inconsistent with movement only along a labor curve or a supply curve Do we need a mix of the two? Or a new theories: foundations for real wages rigidities and large movement in employment (job creation-destruction margins)

40 High correlation with inflation Phillips curve or Output-inflation gap (which explanation?)

41 What is the impact of money on output? Hotly debated question (Great Depression, Competetive desinflation ) -Correlation really high (nominal and real) But what is the causality at work?

42 2.5 Are all business cycles alike? -Volatility and comovement qualitatively similar - But lower output fluctuations and higher persistency of shocks (unemployment)

43 3. Identifying the sources of Fluctuations SVAR approach Motives Identifying and quantifying promising classes of business cycle models using a simple time series procedures Run vector autoregressions in the data and impose identifying assumptions based on theory to back out the role of structural shocks Ex.: Test the AD-AS model : Blanchard and Quah (AER, 1989) and Gali (QJE, 1992)

44 3.1 Identification and Economic Interpretation Blanchard and Quah type analysis to evaluate the relative importance of supply and demand shocks Decompose any movement in the economy as the consequence of two orthogonal demand and supply shocks

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47 3.2 The Structural VAR procedure

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49 How can we back out the structural parameters from the time-series?

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52 3.3 Some summary statistics and tests From the VMA representation, output is given by: Impulse response (IRF) -Demand shock : - Supply shock : Forecast error in predicting output one period ahead

53 Share of the variance of FE due to demand shock is - One-period ahead - At horizon k Historical decomposition Counter-factual: what would have happen if only demand or supply shock had occured?

54 3.4 Applications -Data: US Output (private sector) and Price series for GNP deflator

55 Impulse response function

56 Estimated shocks

57 Variance decomposition

58 Variance decomposition

59 Historical decomposition

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