Development of new version of RIM model (and sector investment estimations) GDP GDP. Hikone Institute of Economic Forecasting IEF RAS
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1 Development of new version of RIM model (and sector investment estimations) GDP GDP Hikone 2010
2 Statistic base Input-output tables in constant and current prices for years National accounts ( ) Institutional accounts ( ) Matrixes of trade and transport margins Tax matrix Rows of sector investment and estimations of fixed capital Consolidated budget data 2
3 Current state of model Redesigned blocks Household consumption Investment and fixed capital Foreign trade Under consideration and construction Budget block Financial market s block 3
4 General scheme of model Estimation of final demand fd = pce+pub+inv+ven+ex-im Estimation of gross output A*out+fd=out or out=f(capinv) Estimation of incomes of population, business and government and deflation to obtain consumption in constant prices Verification of fixed capital restrictions on gross output Solve of price leontivial model p*(a*out)+va=p*out Estimation of value added va=f(out, ) 4
5 Production functions Oil extraction outr[2] = 0.94*outRlag[1][2]+ (0.11*capinv[2]+0.27*capinvlag[1][2])/capintensity2[t]; capintensity2[t] =( *oilstock[t-1]/10^6)*rateusd[t] oilstock[t] = oilstock[t-1] + outr2[t] Ferrous metals outr[16] = 0.97*outRlag[1][16]+ (0.14*capinv[16]+0.29*capinvlag[1][16]); 5
6 Sector investment Sector investment is regressed on replacement needs in that industry, deflated profit and first differences of the output capinv[i] = a 1 * replace[i] + a 2 *profit[i]/(invd) + a 3 * dout; where dout = outr[i] peakout[i][t-1]; peakout[i][t] =max(peakout[i][t-1]*(1-b), outr[i][t]) invd = invt[t]/invrt[t], b fixed capital retirement rate 6
7 Sector investment Replacement is calculated as replace[i] = b1 * capstock[i][t-1]; fixed capital: capstock[i][t] = (1-b1)*capstock[i][t-1] +capinv[i][t] Problem to solve: in real inputs of capacities are distributed in time. We need to obtain and use matrix of distributed investment lags Values of fixed capital with lag and with use of coefficients of production capabilities, estimated at base period, are also used for verification of calculated outputs 7
8 Sector investment 8 A possible solution to problem of different approaches to fixed capital evaluation is to use investment lags when we are verifying production capabilities. We made some estimations of these lags values using Domar s model. Construction lags Coal mining 3,41 3,24 3,30 Food, beverages, tobacco 2,77 2,74 2,74 Wood and wood products; paper and printing 3,08 3,23 3,38 Ferrous metals 1,24 1,56 2,04 Chemicals 3,22 3,10 3,15 Machinery 3,73 3,57 3,46 Stone, Clay, and Glass products 3,07 3,08 3,18 Transport equipment 4,91 4,25 3,42
9 Sector investment As an example of forecast equation I want to show the equation for ferrous metals production SEE = RSQ = RHO = 0.44 Obser = 13 from SEE+1 = RBSQ = DW = 1.12 DoFree = 10 to MAPE = Variable name Reg-Coef Mexval Elas NorRes Mean Beta 0 capinv replace16[1] profit16[1]/invd dif
10 Sector investment inv[i] = j (capinv[j] * InvTS[j][n]*InvEl[i][n]) InvTS matrix of technological structure of investment made by purchaser. Structure includes construction, machinery and equipment and other sectors (for example structure of capital investments in oil extraction is 32% construction, 23% machinery,44% other) InvEl matrix of coefficients, which show sector share in construction, machinery and production of other investment goods. 10
11 InvEl matrix contents (part I) Construction Machinery Other goods Agriculture 0,00 0,00 0,00 Petroleum extraction 0,00 0,00 0,00 Natural gas extraction 0,00 0,00 0,00 Coal mining 0,00 0,00 0,00 Other Fuels, incl. nuclear 0,00 0,00 0,00 Ores and other mining 0,00 0,00 0,00 Food, beverages, tobacco 0,00 0,00 0,00 Textiles, apparel, leather 0,00 0,00 0,00 Wood and wood products 0,00 0,00 0,00 Paper and printing 0,00 0,00 0,02 Petroleum refining 0,00 0,00 0,00 Chemicals 0,00 0,00 0,00 Pharmaceuticals 0,00 0,00 0,00 Plastic products 0,00 0,00 0,00 Stone, Clay, and Glass products 0,00 0,00 0,00 Ferrous metals 0,00 0,00 0,00 Non-ferrous metals 0,00 0,00 0,00 Fabricated metal products 0,00 0,00 0,10 Machinery 0,00 0,52 0,00 Computers, office machinery 0,00 0,03 0,00 Electical apparatus 0,00 0,05 0,00 11 Radio, television, communication equipment 0,00 0,07 0,00
12 InvEl matrix contents (part I) Construction Machinery Other goods Medical, optical, and precision instruments 0,00 0,06 0,00 Automobiles, highway transport equipment 0,00 0,13 0,00 Sea transport equipment and its repair 0,00 0,02 0,00 Airplanes, rockets, and repair 0,00 0,05 0,00 Railroad equipment and its repair 0,00 0,06 0,00 Recycling 0,00 0,00 0,00 Electric, gas, and water utilities 0,00 0,00 0,00 Construction 1,00 0,00 0,00 Wholesale and retail trade 0,00 0,00 0,34 Hotels and restaurants 0,00 0,00 0,00 Transport and storage 0,00 0,00 0,07 Communication 0,00 0,00 0,00 Finance and insurance 0,00 0,00 0,00 Real estate 0,00 0,00 0,03 Equipment rental 0,00 0,00 0,00 Computing service 0,00 0,00 0,00 Research and development 0,00 0,00 0,43 Other business services 0,00 0,00 0,00 Government, defense, social insurance 0,00 0,00 0,00 Education 0,00 0,00 0,00 Health services 0,00 0,00 0,00 12 Other social and personal services 0,00 0,00 0,00
13 Household consumption Household consumption depends on money income, difference in personal consumption compared with previous year and relative sector prices pcepc[i] =a + b*moneyinc[t]/pop[t] + c*dpce + d* relprice[i]; dpce = pcepc[t] pcepc[t-1] pcepc[t] =pcert[t]/pop[t]; relprice = price[i]/(pcet[t]/pcert[t]) moneyinc = 0.8* wagest[t] + 0.3*(profitT[t] invt[t]) 13
14 Imports Sector imports are calculated from flows of intermediate and final consumption by use of elasticity coefficients These import coefficients depend on exchange rate and share of new capital funds ( which are assumed to be competitive with foreign producers) imcoeff[i] = imcoeff[i][t-1]* (1-a 1 *rateusd[t]/rateusd[t-1]) a 2 * capinv[i]/capstock[i] 14
15 Exchange rate Exchange rate of ruble was regressed from difference between consumer price index and import/export ratio rateusd[t] = rateusd[t-1]*(1+a 1 *(CPI[t]- brent[t]/brent[t-1])+a 2 *(imt[t]/ext[t] -1) 15
16 Exports Export is calculated by regression equations, in which a large set of variables is used, such as outputs, internal consumption, exchange rate exr[i]= a 0 + a j *outr[j] + b j *pcer[j] + 16
17 Goverment consumption In current version structure of government consumption remains constant on whole forecasting period. Total government consumption is regressed from tax incomes and reserve fund volume. An exogenous parameter describing minimal amount of government expensive can be set. When deficit appears, it is financed from reserve fund, if possible 17
18 Oil prices forecast A key exogenous parameter of forecast is oil prices. For lookahead calculations has been used the world oil prices, based on forecast made by the Ministry of Economic Development of the Russian Federation 18
19 Forecast scenarios 1. Inertial ( orange ). All tax variables remain at present levels. 2. Modernization ( green ). Government provides subsidies for increasing investment in manufacture, especially in machinery and equipment and high technology industries ( computers, electical apparatus, radio, television, communication equipment, medical, optical, and precision instruments). Mineral extraction tax for oil is increased by 11% 19
20 Forecast scenarios 3. Modernization with corruption parameter( red ). Same as previous scenario, but 40% of subsidies are lost due to corruption. 4. Reduced tax burden for oil sector( blue ). Mineral extraction tax and export duty for oil are reduced by 20%. 20
21 Forecast results: oil extraction 21
22 Forecast results: oil export 22
23 Forecast results: exchange rate Decreasing rates of inflation ( from 8% in 2011 to 2% in 2030) and more quickly Increasing oil prices causes strengthening of ruble. The difference with official MED Forecast lies in scale of year 2017 crisis. 23
24 Forecast results: export 24
25 Forecast results: imports 25
26 Forecast results: households consumption 26
27 Forecast results: government consumption 27
28 Forecast results: investment 28
29 Forecast results: GDP 29
30 Forecast results: output 30
31 Forecast results: average GDP rates Inertial ("orange") Modernization ("green") Modernization ("red") Reduced oil taxes ("blue") The most efficient scenario is green modernization Due to oil price scenario support of oil industry proves to be better then corrupted modernization in short-term forecast, but loses in long-term perspective due to increasing capital intensity of oil extraction and lower dynamic of oil prices 31
32 Forecast results: structure of investment Construction Machinery Other goods Initial(2008) 33,5% 48,2% 18,4% Inercial 33,1% 51,1% 15,8% Modernization 32,2% 54,5% 13,3% Modernization with corruption 32,5% 53,3% 14,1% Reduced tax burden for oil sector 33,1% 50,4% 16,5% 32 In all scenarios share of machinery and equipment in investment increases, share of construction remains sensibly constant, while share of other goods significantly decreases.
33 Conclusion We need to improve verifycation of production capabilities. Investment must have some effect on input coefficients. Blocks of government consumption and financial market require be developed more in detail Using present version of model we can study different scenarios of investment policy on the assumption of conservative budget administration 33
34 THANK YOU FOR YOU ATTENTION 34
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