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ModelData
Bayesian estimationConclusion
Use of Dynare Software for DSGE modelling
Katerina Gawthorpe
Modern Tools for Financial Analysis and Modeling 2018
5. June 2018
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Outline
1 Model
2 Data
3 Bayesian estimation
4 Conclusion
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Outline
1 Model
2 Data
3 Bayesian estimation
4 Conclusion
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Outline
1 Model
2 Data
3 Bayesian estimation
4 Conclusion
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Outline
1 Model
2 Data
3 Bayesian estimation
4 Conclusion
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Model
Extended version of Aliyev & Bobkova (2014)Characteristics of both models:
External habit formation in the utility functionWage and price rigidityCapital adjustment costs
Main differencesMore detail household heterogeneity
Three HHs groupsIncome effect in the consumption equation
Without non-ricardian HHs specificationImportant for government shock or foreign demand shock
Intermediate-inputsIntroduced into PF, import and exportIntermediate-inputs complement other inputs
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Data transformation
Data on model-entry side:To define parameters for calibrated modelTo define priors for estimated modelTo define size of shocks or their prior value for conditionalforecasts or as input for shock decomposition.
Output values from model:For model evaluation, compared to dataSometimes subject to transformation to obtain absolute orrelative valuesForecast
The link between data and model might be weaker forcalibrated rather than estimated models as no observationequations are necessary for model simulations.
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Data transformation
Seasonally adjusted data with X-13 ARIMA are subject totransformation into growth rates and then demeaned toobtain data compatible with observed variables. The finalvalues are multiplied by 100 for easier interpretation inpercentages. (See Pfeifer, 2017)The transformation of data appears as
X mobst =
(log
Xt
Xt−1− X obs
t)∗ 100
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Bayesian estimation
Bayesian estimation combines prior information about aparameter value with the evolution of data.Mostly used algorithm is the MH-MCMC, this algorithmdecides if a new parameter value given the likelihooddefined by Kalman filter should be accepted.For our model we use two MCMC sequences with 100 000draws. The acceptance ratio is below 0.24.
p(Ω/yt ) =p(yT/Ω)p(Ω)
p(yT )
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Prior & Posterior probability
0.1 0.2 0.3 0.40
5
10
Share of Gov. cons. on output
0 0.2 0.40
5
Share of Gov. cons. on GR
0 0.2 0.4 0.60
5
AR par. in Gov. cons. shock
1 2 3 40
0.5
1
Government cons. shock2 4 6 8
0
0.5
1Income tax shock
0.2 0.4 0.6 0.80
5
Impact of income tax
0.5 1 1.5 20
2
4TR parameter for inflation
0 0.5 10
5
TR parameter for output
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Sensitivity analysis
Output variation with Gov. cons. parameter Output variation with income tax parameter
00.6
0.2
0.4
Out
put
0.4 10
0.6
omega yg Time
0.8
0.2 50 0
-0.040.5
-0.03
0.45 25
Out
put -0.02
20
Income tax parameter
0.4 15
Time
-0.01
100.35 50.3 0
0.2 0.80
5
Impact of income tax
X: 0.3333Y: 6.26
Source: Authors’ creation, data from the Ministry of Finance.
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Conditional forecast
Impact of government consumption shock on output
Output vs government consumption variable in data
Source: Authors’ creation, data from the Ministry of Finance.
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Conditional forecast
Impact of income tax shock
Dynamic of income tax in data
Source: Authors’ creation, data from the Ministry of Finance.
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Shock decomposition
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
-10
-5
0
5
10
15
Government Balance
Monetary policyOther gov. expenditureEU investmentVATIncome taxWagesInvestmentTechnology shockForeign demandLabor demandExchange rateOther
Source: Authors’ creation, data from the Ministry of Finance.
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Dynare - shortcomings
Difficulty to write a Matlab code into a Dynare scriptPossible to write it at the bottom of the script to create a plotOr to write it in the beginning of the script to call onsteady-state values or parameters
Entering exogenous values for a variableCombining deterministic and stochastic shocksIdentification of shock contributions to variables from ashock-decomposition output and subsequenttransformation of quarterly data to annualBlack-box search for the steady-state of non-linear modelsas well as their subsequent estimation and interpretationgEcon package as an alternative?
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
ModelData
Bayesian estimationConclusion
Thank you for your attention...
Katerina GawthorpeMinisterstvo financí
Letenská 15118 10, Praha 1
email: katerina.gawthorpe@mfcr.cz
Katerina Gawthorpe Use of Dynare Software for DSGE modelling
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