Fiscal consolidation as proposed is a workable policy, which will keep the rising debt-to-GDP ratio at sustainable level, but it is still not sufficient to reverse debt trend in 2017. Perhaps the other structural measures will do this job, but they are beyond the scope of this paper.
SažetakProgram fiskalnog prilagođavanja uspešno je sproveden u 2015. Na osnovu postignutih rezultata sada je trenutak da se razmišlja o sledećoj fazi programa koja bi se odnosila na optimizaciju fiskalne politike. Pod pojmom "optimalne fiskalne politike" podrazumevamo takvo dizajniranje fiskalnih instrumenata koje bi omogućilo postizanje maksimalne stope rasta BDP. MMF je predložio okvire jedne takve politike u svojoj poslednjoj studiji o zemljama Centralne, Istočne i Jugoistočne Evrope.Mi ćemo u ovom radu testirati navedene preporuke, ali u izmenjenom analitičkom okviru koji nam daje QUEST_Serbia Dynamic Stochastic General Equilibrium (DSGE) model. Model je tako modifikovan da su sve fiskalne varijable tretirane kao endogene veličine podložne stohastičkim šokovima i oceni parametara na osnovu Bajesove procedure za period Y2003Q1 do Y2015Q4. Dodatno, primenili smo novi analitički alat dekompozicije impulsnih funkcija koji nam omogućava da kompleksne dinamičke nelinearne odnose svedemo na jednostavniju formu linearnih veza između stejt varijabli i ostalih endogenih varijabli.Naši rezultati podržavaju opšti stav MMF na primeru srpske ekonomije da smanjenje fiskalnog opterećenja na rad i kapital ima pozitivno dejstvo na rast, a da smanjenje fiskalnih transfera i državne potrošnje ima negativan, ali privremeni efekat na rast. Na drugoj strani, naši rezultati ne podržavaju podizanje stope PDV jer to negativno utiče na rast, a podržavaju povećanje javnih investicija samo pod određenim uslovima. Ključne reči: fiskalno prilagođavanje, DSGE modeli, optimalno oporezivanje, dekompozicija funkcije impulsnih odziva JEL CLASSIFICATION: C680, E620 AbstractThe fiscal consolidation program in 2015 was a success. Despite this success, it is time to consider a switch away from the fiscal consolidation policy towards a fiscal optimization policy. By "fiscal optimization policy" we mean a proper design of fiscal instruments that might lead towards the maximum potential rate of GDP growth. Relying on a panel regression model for 76 countries, the IMF recommended some guidelines for such an optimal fiscal policy in its latest regional report on Central, Eastern, and Southeastern European (CESEE) countries.In this paper we test the IMF's recommendations in a different analytical framework based on the QUEST_Serbia Dynamic Stochastic General Equilibrium (DSGE) model. We endogenize all fiscal revenue instruments, update macroeconomic data, and estimate the model's coefficients using Bayesian technique. We also develop a new analytical tool for the decomposition of Impulse Response Functions (IRF), which helps us to reduce complex dynamic non-linear general equilibrium relations to simpler linearized relations between endogenous variables and key state variables.Our findings support a general IMF suggestion in the particular case of the Serbian economy for reducing fiscal duties on labor and capital inputs, as well as public consumption and transfer payments. We, however, do not support increasing VAT rates or expanding public investments...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.