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Modeling market and nonmarket Intangible investments in a macro-econometric framework

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Modeling market and nonmarket Intangible investments in a macro-econometric framework. Sociedty for Economic Measurement Annual Conference. Thessaloniki July 2016

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Modeling market and nonmarket Intangible investments in a macro-econometric framework

  1. 1. Modeling market and nonmarket Intangible investments in a macro-econometric framework F. Bacchini (Istat), Rome E. Bontempi (University of Bologna) R. Golinelli (University of Bologna) C. Jona-Lasinio, (LUISS Lab and Istat), Rome Society for Economic Measurement Annual Conference Thessaloniki (Greece), July 6th- 8th, 2016 This project has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement No. 612774 SPINTAN 1 / 17
  2. 2. Outline • New data on intangibles − > new opportunities for macroeconometric models • Modeling intangible capital in a macro-econometric framework • MeMo-It: the macro-econometric model for the Italian economy • Empirical results and future challenges SPINTAN 2 / 17
  3. 3. Motivation • Existing macro econometric models rarely include investment by asset and hardly explicitly incorporate intangible investments • Some work has been done to evaluate the best framework to model R&D transmission mechanisms for policy purposes (IPTS WP (2015)): • Dynamic Stochastic General Equilibrium (DSGE) model-QUEST • Spatial Computable General Equilibrium (SCGE) model- RHOMOLO • Computable General Equilibrium (CGE) model-GEM-E3 • Macro-econometric model-NEMESIS SPINTAN 3 / 17
  4. 4. New data on public intangibles Introduction of SNA 2008 ESA 2010, R&D is now classied as investment SPINTAN 4 / 17
  5. 5. Aim We investigate: • the determinants of market and nonmarket investment in intellectual property products, looking at software and RD capital accumulation • the mechanisms trough which their interaction aects the growth performance of the Italian economy We do that including market and nonmarket investment in intellectual property products in the macro-econometric model, MeMo-It developed by the Italian Statistical Institute for the medium term forecasts. SPINTAN 5 / 17
  6. 6. MeMo-It: theoretical background Frontier that reects dierent optimal composition between economic theory and data (Pagan, 2003) at the bottom we have models (such as VARs) that aim to summarize the data. The position on the curve can be related to the institutional framework in which modelers operate. The total effort to be spent in the modeling activity leads to the “budget constraint” line. Its slope reflects the relative “price” based on theoretical vs. data management expertise. Figure 2 reports two alternative lines (cases). Figure 2 - Two alternative models (points) along the “best practice” frontier SPINTAN 6 / 17
  7. 7. MeMo-it: theoretical background • MeMo-It modeling is a mixture of both LSE and Fair-updated Cowles Commission approaches and techniques: in order to merge theory and data at point B, MeMo-It uses cointegration methods on dynamic sub-systems to estimate theory-interpretable and identied steady state relationships, imposed in the form of equilibrium-correction models. • MeMo-it is a New Keynesian model where in the short run the activity is mainly driven by the demand side, while in the long run the economic system converges to the potential output determined by the supply side of the economy. SPINTAN 7 / 17
  8. 8. MeMo-it: main transmission channels MeMo is structured into 5 main interacting blocks including 60 equations and 82 identities. The diagram in figure 3 outlines MeMo-It main relationships. In particular, the five rectangles represent the model’s basic blocks which are progressively numbered from 1 to 5 to refer to the subsections where their details are given: supply side (5.1), labor market (5.2), demand side (5.3), prices (5.4), and Government (5.5). In addition, three rhombuses denote the main sources of external information for the age- and gender-structure of the population, the ECB policy interest rate (in the financial sector) and global variables, such as world demand, exchange rates, oil price and other import prices. Arrows identify the causal structure of the MeMo-It relationships across blocks. Figure 3 - Outline of MeMo-It block relationships MeMo-It is substantially based on the New-Keynesian approach where the supply sideSPINTAN 8 / 17
  9. 9. Data on intangibles Investment (excluding residential structures) SPINTAN 9 / 17
  10. 10. Modeling investment by asset in a macro econometric framework We model business and public investment accounting for asset specic characteristics potentially aecting the reactivity of capital accumulation over the business cycle. • Framework consistent with both traditional and micro investment models (Clark, (1944); Hall and Jorgenson, (1967); Bloom et al 2007). • Investigate short and long run investment determinants. • Aggregate vs investment by asset (intangible vs physical assets) SPINTAN 10 / 17
  11. 11. Modeling investment by asset in a macro econometric framework To explore all these options in a comprehensive framework we adopt a Vector Error Correction Model (VECM) (Johansen, 1995). • The vector of variables for the aggregate representation is Zagg = (kagg , y, ucagg , liq, unc) • while that for the representation by asset becomes Zj = (kj , y, ucj , liq, unc) with j = iprmkt, iprnmkt; SPINTAN 11 / 17
  12. 12. RD and Software market nonmarket - Italy (2000-2015): compare asset dynamics SPINTAN 12 / 17
  13. 13. RD and Sotware market and nonmarket - Italy (2000-2015): compare dynamics between institutional sectors SPINTAN 13 / 17
  14. 14. RD market sector ∆log I_RD GDP = 0.1−0.4(log I_RD(−1) GDP(−1) −log(I_RDNM ))−0.06∆log Bloom SPINTAN 14 / 17
  15. 15. Software, market sector ∆log I_SW = 0.6 − 0.2(log I_SW (−1) − 1.03log(I_RD) − 1.82log( GOS(−1) GDPN (−1) ) + 0.7∆log(I_RS) + 0.2∆log(I_SW (−1) − 0.06∆log Bloom SPINTAN 15 / 17
  16. 16. Software, nonmarket sector ∆log I_SWNM = −0.4 − 0.5(log I_SWNM (−1) − log I_RDNM ) + 2.7 deficit(−1) GDPN (−1) SPINTAN 16 / 17
  17. 17. Summing up and policy challenges • Previous work showed that individual investment characteristics matter since, assets behave dierently over the business cycle • Tangible assets: In the short run, uncertainty are key determinants of capital accumulation, In the long run, instead, uncertainty and output are the main drivers coherently with the exible neoclassical model • Positive correlation between MKT and NMKT intangibles (more on RD) • NMKT intangibles can be a key policy instrument to foster MKT investments SPINTAN 17 / 17

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