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- Figure 1: Flow diagram of the model. 12 14 16 18 Logs 200 400 600 800 1000 1200 Time Output Investment Consumption (a) Output, investment and consumption 2.5 3 3.5 4 4.5 5
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- Figure 10: Firm productivity, capital, and size H0: q=0 (serially uncorrelated) H0: q=0 (serially uncorrelated) HA: s.c. present at range specified HA: s.c. present at range specified lags chi2 df p-val lag chi2 df p-val 1-1 525.306 1 0.00 1 525.306 1 0.00 1-2 525.309 2 0.00 2 506.186 1 0.00 1-3 540.39 3 0.00 3 451.117 1 0.00 1-4 541.171 4 0.00 4 295.336 1 0.00 1-5 541.877 5 0.00 5 71.669 1 0.00 Notes. Cumby-Huizinga test for autocorrelation for panel data with large sample size (Baum and Schaffer, 2013) under the null hypothesis of no autocorrelation at any lag order. Test robust to heteroskedasticity. Test also corrected for the possibility that the series may exhibit arbitrary autocorrelation. The left panel report the lag range (between the first and the the last period). The panel on the right reports the correlation at each lag.
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- Figure 4: Cyclcical component of the main macro variables All series exhibit fluctuations that are qualitative similar to those observed in the data (Assenza et al., 2015; Caiani et al., 2016; Dosi et al., 2010, 2015). The volatility of employment and investment is significantly higher than that of consumption and output. Consumption is less volatile than output. Differently from observed time series, in our model investment is more volatile than employment. This is related to the lumpiness of capital stock investment which in our model is constrained by the choice of capital good producers, and their production cue (we do not model entry of new firms in the capital good sector).
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- Figure 5 plots the autocorrelation structure for de-trended real output (5a), investment (5b), consumption (??), and unemployment (5d) for 20 lags. The simulated series are quite similar to real series (Assenza et al., 2015). The first lag autocorrelation of real series estimated by Assenza et al. (2015) for output, investment, consumption, and unemployment are, respectively, 0.8485, 0.7952, 0.8176, 0.6454. For our simulated series, the first lag autocorrelations are 0.8492, 0.8169, 0.9577, and 0.6826.
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- Figure 6 plots the cross-correlation between the cyclical component of real output and the cyclical components of, respectively, real output (6a), investment (6b), consumption (6c), and unemployment (6d) for 10 lags. Investment is pro-cyclical and coincident, consumption follow with a couple of lags, as in Caiani et al. (2016), and short term unemployment is countercyclical and coincident.
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- Figure 6: Crosscorrelation between the cyclical component of output and the main macro variables: output, investment, consumption, and unemploymnet αb 2 v2 tr + ιb + αb 3 vtrιb + εb t , where r is a simulation iteration, ιb is a run fixed effect, and εb the residual. The black series is the regression fit of the data pooled from the different series, and the red bands represent the confidence interval. Overall, the curve is quite close to that found from a number of countries (Nickell et al., 2002).
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- Table 19: Autocorrelation of firm productivity G Extra Tables Ñ 0.06 0.07 0.08 0.09 0.1 0̄.725; 0̄.275 89.30*** 96.36** 103.27*** 110.90*** 119.01*** 0̄.775; 0̄.225 84.86*** 93.58 100.05*** 103.97*** 111.82*** λp,1; 0̄.825; 0̄.175 80.25*** 88.24*** 94.01 98.46*** 105.54*** λq,1 0̄.875; 0̄.125 75.27*** 82.21** 89.96*** 94.97 100.63*** 0̄.925; 0̄.075 70.82*** 78.07*** 84.08*** 89.46*** 95.45 0̄.975; 0̄.025 67.84*** 73.29*** 79.70*** 84.80*** 90.59*** Notes: Mean values over 20 replications for the average inverse herfindahl index over 2000 simulation steps. The index is computes using sales, across all sectors. The significance of the difference between the benchmark configuration (in italics) and each pair of parameters is computed with a t-test: *** p<0.01, ** p<0.05, * p<0.1 Table 20: Inverse Herfindahl Index for different levels of competition
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