Long-term regional economic forecast for Hungary Macro modeling and regional downscaling

Átírás

1 NEW IDEAS AND NEW GENERATIONS OF REGIONAL POLICY IN EASTERN EUROPE Long-term regional economic forecast for Hungary Macro modeling and regional downscaling Sebestyén Tamás Tamás SEBESTYÉN Zsuzsanna MÁRKUSNÉ Márkusné Zsibók Zsuzsanna ZSIBÓK University of Pécs Faculty of Business and Economics HAS Centre for Economic and Regional Studies 7 to 8 April 2016, Pécs, Hungary

2 The NAGiS project National Adaptation Geo-information System (NAGiS) project: aims to develop a multipurpose geo-information system that can facilitate the policy-making, strategybuilding and decision-making process related to the impact assessment of climate change and founding necessary adaptation measures in Hungary The aim of our current CERS project is to forecast the long-term socio-economic development path of Hungary until 2050, and to foster the adaptation to climate change The results (data base) will be integrated into the NAGiS NAGiS will be extended by new data describing the future socio-economic characteristics of Hungary Tamás Sebestyén Zsuzsanna Márkusné 2

3 The NAGiS project (cont.) Our project investigated and quantified demographical, economic and land-use change on various geographical and temporal scales, taking into account the interdependence of socioeconomic spatial processes and climate change Tamás Sebestyén Zsuzsanna Márkusné 3

4 Framework A complex problem modular solution Macroeconomic model Problems with multisectoral models Problems with multiregional models First steps Two model blocs A DSGE model which generates temporary macroeconomic steady state Drivers generating long-term dynamics 4

5 DSGE model block A mainsteam macro model Mid-sized model cc. 50 variables: corresponding all the important aspects of the economy Dynamic: investments are led by intertemporal decision-making additional dynamic elements (capital accumulation, lagged variables) Stochastic: Bayesian estimation vs. calibration Exogeneous shocks incorporating external short- and long-run effects (see: climate, technology etc.) General equilibrium Assures a consistent system of interaction of macro variables 5

6 Main sectors of the DSGE model Households dynamic optimising decision-making labour supply consumption investment holding of domestic government bonds Companies dynamic optimising decision making labour demand capital demand production Investment sector produces capital different productivity from companies productivity 6

7 Main sectors of the DSGE model (cont.) Foreign sector Export demand Import supply (the domestic production is not a perfect substitute of the import goods price structure) Financing Government Levies taxes (after income, consumption, social security) Pays transfers Government spending Investments creating infrastructure as an external effect in production Debt dynamics (stabilised through lump-sum taxes) 7

8 Long-run model block Based on OECD ENV-Growth model Exogenous shocks drive away the model form the steady state generated by the DSGE through which a long-run growth path can be simulated productivity climate demography (labour supply, rate of inactivity) Long-run dynamics are led by productivity A catching-up logic (through foreign productivity) The speed of catching-up depends on the openness of the economy as generated endogeneously by the DSGE model block 8

9 Climate in the long-run model Climate change enters the model as an index number, an exogenous driver affects productivity investments in infrastructure? Problems: elasticities, estimation/calibration of other parameters Model runs: base climate 9

10 Macro forecast for the GDP growth in Hungary (base and climate scenario) 0,023 0,022 0,021 0,02 0,019 0,018 0,017 0,016 0,015 0,014 0,013 10

11 Regionalisation of the macro model County level GDP consumption employment rate Forecast for growth rates Forecast for absolute levels Macro-level aggregation government spending infrastructure 11

12 Regional disaggregation Estimating the co-movement of county-level variables with the national-level variables on the basis of historic data Regression method Projecting the revealed nature of co-movement to the future with the help of the predicted macro variables (GDP) Exogeneous driver: working-age population 12

13 Regional disaggregation National GDP growth rate Forecasted national-level GDP growth rate Factor loading 1 County-level GDP growth rate Forecasted county-level GDP growth rate Factor loading 2 County-level employment growth rate Forecasted county-level growth rate of the working age population Reference period Forecasting period 13

14 Regional disaggregation Estimating the co-movement of county-level variables with the national-level variables with historic data Regression method Projecting the revealed nature of co-movement to the future with the help of the forecast macro variables (GDP) Exogeneous driver: working-age population Problem: lack of reasonable feedback mechanisms Alternatives: extrapolating historic distribution of county-level variables building a regional model which incorporates interregional migration of the factors of production, agglomeration forces etc. 14

15 County-level forecast for the GDP growth rate in Hungary 0,05 Budapest 0,04 0,03 0,02 0,01 0-0,01 Pest Fejér Komárom-Esztergom Veszprém Győr-Moson-Sopron Vas Zala Baranya Somogy Tolna Borsod-Abaúj-Zemplén Heves Nógrád Hajdú-Bihar Szabolcs-Szatmár-Bereg Jász-Nagykun-Szolnok Bács-Kiskun Békés Csongrád -0,02 15

16 County-level forecast for the employment rate in Hungary Budapest Pest Fejér Komárom-Esztergom Veszprém Győr-Moson-Sopron Vas Zala Baranya Somogy Tolna Borsod-Abaúj-Zemplén Heves Nógrád Hajdú-Bihar Szabolcs-Szatmár-Bereg Jász-Nagykun-Szolnok Bács-Kiskun Békés Csongrád 30 16

17 ,08 County-level forecast for the growth rate of consumption in Hungary Budapest 0,06 0,04 0,02 0-0,02-0,04 Pest Fejér Komárom-Esztergom Veszprém Győr-Moson-Sopron Vas Zala Baranya Somogy Tolna Borsod-Abaúj-Zemplén Heves Nógrád Hajdú-Bihar Szabolcs-Szatmár-Bereg Jász-Nagykun-Szolnok Bács-Kiskun Békés Csongrád 17

18 Directions for future research Complex spatial and dynamic interactions are needed Concerning the standard economic forecasting method: sophistication of the frictions a more detailed monetary and fiscal policy econometric estimation of the parameters behind the TFP dynamics Modelling climate change linking with land use and demography with interactions sectoral disaggregation: inclusion of the agricultural sector (e.g. land use as a factor of production) interactions of technology and climate change Regional model: structural interrelations between regions There is a lack of data base and methodology to correctly determine the parameters in past researches. 18

19 Thank You for Your attention!