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1 A Multiple Break Panel Approach to Estimating United States Phillips Curves Bill Russell, Anindya Banerjee, Issam Malki and Natalia Ponomareva Royal Economic Society 2011 Conference Royal Holloway University London, April 2011

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2 Graph 1: United States Quarterly Inflation Seasonally adjusted, March 1960 – June 2007

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3 What is the true statistical process of inflation? 1.Shocks mean zero and no change to MP then inflation varies around the long-run rate of inflation 2.An increase in long-run rate requires a loosening in MP inflation converges on new long-run rate Implies inflation is stationary around shifting means Can estimate shifts in mean using Bai-Perron technique for multiple breaks and shown in the inflation slide

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4 Graph 1: United States Quarterly Inflation Seasonally adjusted, March 1960 – June 2007

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5 Hybrid Phillips curve

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6 Remainder of Presentation 1.Demonstrate standard results in the literature are biased using Monte Carlo simulations when we assume inflation is stationary 2.Estimate United States short and long-run Phillips curves assuming inflation is stationary around a shifting mean

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7 1. Monte Carlo simulations Assuming Inflation is I(0) Generate Phillips curve data with no significant dynamic terms Forcing variable Inflation series Mean shift inflation series

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8 Monte Carlo Simulations – generate 190 observations – replicate the model 10,000 times – estimate Phillips curves with GMM using – report average estimates (inference the same with median) – true model

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10 What do we conclude from the Monte Carlo analysis? (i)Unaccounted mean shifts bias upwards the dynamic inflation coefficients and downward the forcing variable (ii)Bias is so large that mean shifts alone will generate the standard empirical Phillips curve results of the past 35 years

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11 2. Estimate United States Phillips Curves Assuming inflation is I(0) around shifting means 1.Apply Bai-Perron technique to identify multiple breaks in mean and identify n inflation regimes – in our case 9 2.Partition the data into n cross sections of data where each is an individual inflation regime with a constant mean inflation 3.Estimate the 9 short run Phillips curves using 2SLS fixed effects panel estimator 4.Estimate with standard time series panel estimator (2 lags of independent variables as instruments)

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12 Estimate United States Phillips Curves Data Quarterly March 1960 – June 2007 Inflation is ln GDP implicit price deflator at factor cost Markup is ln of GDP deflator at factor cost on unit labour costs (national accounts measures)

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14 Calculating the Long-run Markup -Define long-run inflation as the mean rate of inflation in each regime

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15 Calculating the Long-run Markup -Provides a locus of nine combinations of long-run rates of inflation and the markup -Can use this locus to look at the shape of the long-run Phillips curve

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17 Graph 3: United States Inflation and the Markup

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18 Conclusions i.If inflation is stationary around shifting means then no support for any of the modern theories ii.No evidence that the lead in inflation plays a significant role in inflation dynamics iii.Marginal evidence that any lags in inflation are significant in the inflation-markup Phillips curve

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19 Conclusions iv.Given ii & iii then no support for F-P, NK & hybrid models and markup should be thought of as ECM and a proxy for the firms profit margin v.Friedman/Phelps remarkable empirical insight appears true to a first approximation

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20 Spare slides from here

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21 Monte Carlo on the panel methodology

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24 Reassessing Cogley and Sbordone slides from here

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28 Inflation Persistence

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29 Alternative Hypothesis: The estimated low persistence is due to the over-breaking of highly persistent data Generate 190 observations assuming and Estimate fixed effects OLS using the cross-section panel methodology imposing breaks 0 to 15

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30 Graph 2: The Impact of Over-breaking on Estimates of the AR(1) Coefficients

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31 Graph 2: The Impact of Over-breaking on Estimates of the AR(1) Coefficients

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33 A2. Monte Carlo simulations assuming inflation is I(1) ADF univariate unit root test statistic = , CV 5% = Difference the data and the model True model remains

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35 From Russell (2007). Non-stationary Inflation and Panel Estimates of United States Short and Long-run Phillips Curves. Price index is all urban CPI. Assumes inflation is stationary around shifting means. Same data as Russell and Banerjee (2008).

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36 From Russell and Banerjee (2008). The Long-run Phillips curve and Non- stationary Inflation, Journal of Macroeconomics, vol. 29, pp Price index is all urban CPI. Assumes inflation and markup are integrated. Graph 9: United States Long-run Phillips Curve

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37 Issues: long-run Phillips curve has a positive slope Ross and Wachter (1973) Friedmans (1977) Nobel Lecture Akerlof, Dickens and Perry (2000) Markup and inflation are negatively related in the long-run

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38 Banerjee, A. and B. Russell (2001). Inflation and the Markup in the G7 Economies and Australia, Review of Economics and Statistics, vol. 83, no. 2, May, pp

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39 Three Issues 1.Are the coefficients constant across regimes 2.Estimating dynamic panels when n is large relative to t - Arellano & Bond (1991), Arellano & Bover (1995), Blundell and Bond (1998), Bond (2002) - rule-of-thumb says it is ok if t is large enough to estimate each cross section separately

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40 Three Issues 3. Endogeneity of u/e rate and expected inflation term - 2SLS - instruments are two lags of inflation and unemployment rate

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41 1 (i) Inflation is not stationary Inflation stationary with constant mean implies that (i) The question what is the long-run rate of inflation? is valid. Average March 1952 – September % March 1952 – September % Last 10 years 2.4% (ii)Institutional arrangements have no impact on the long-run rate of inflation

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42 1 (i) Inflation is not stationary (iii) All monetary economics and macroeconomics literature on the dynamics of inflation with changes in money growth is at best misplaced (iv) Long-run Phillips curve in an applied sense is a single point - If you do not accept (i) to (iv) then you have to conclude that inflation is not stationary with a constant mean

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43 Consider now the last 50 years of US Inflation Data What is the true statistical process of ? - not integrated - not trend stationary - not stationary with constant mean therefore stationary with shifting means

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