Around the world, governments have taken steps to mitigate the impact of the crisis on non-financial firms and households, including increased spending on health care and subsidies and funds raised through the issuance of government securities. Both links are explicitly designed to capture pro-cyclical behavior in the financial sector and how this will affect the dynamics of aggregate demand. This study concluded that a mix of policies is the most effective way to promote economic recovery and improve our understanding of the most effective mix of policies in difficult times, allowing us to better respond to future unprecedented events.
The introduction is the first section, and the overview of the modeling framework is the second section. The analysis of the impulse response function is explained in Section IV for each shock. Aj,t captures house price shock and jj is the amount of weight given to the housing stock.
Explaining the level of real savings held by household savers, Lb,t is a loan given by the bank to the borrower's house, Ψt describes monitoring costs by banks, and ζL,t is the amount of non-performing loan (NPL). The log-linearization of the two equations will give the basic equation of the New Keynesian Phillips Curve (NKPC) πt=βsEt{πt+1}+Ψx̂t. Another parameter that describes the behavior of the central bank is the parameters used in the Taylor rule.
We use the Metropolis Hasting algorithm and harmonic mean estimation to calculate model probabilities.
RESULTS AND ANALYSIS
The letters in the column headed "Previous type" indicate the previous density function. Higher discount factors in the financial industry show that the financial intermediary values future utility more than current utility. We analyze five impulse response function scenarios based on the estimation results and explain the fundamental distinctions in the propagation processes occurring in diverse situations.
We analyze the central bank interest rate cut after the pandemic shock in the second scenario (blue line) and together with the pandemic shock. We analyze a positive fiscal policy shock and combine it with a pandemic shock in the third scenario (red line). As a result of the labor supply shock, household income will decrease, reducing household purchasing power and reducing the demand for housing stock.
Policy rate cuts during the pandemic are intended to support overall liquidity in the system and prevent further spread of the pandemic in the financial sector. Moreover, the reduction in base rates increases the margin of interest rates over loan and deposit rates, increases the financial condition of banks and stimulates greater consumption. In the short run, the IRF shows that fiscal policy has a lower ability to push aggregate demand than monetary policy.
On the other hand, fiscal policy has the potential to increase aggregate demand in the long run to a greater extent than monetary policy. In the following, we examine the impact of the mix of monetary and fiscal policy in dealing with pandemics. As a result, households will require more external financing from the banking sector, leading to an increase in the equilibrium level of credit.
The most serious problem with the DSGE model is misspecification generated by overly stringent constraints. We construct a mapping from the DSGE model to the VAR parameters by using the VAR as an approximation model for the DSGE model. This technique relaxes the constraints of the DSGE model and provides a VAR representation of DSGE models.
To obtain a structural variable we express the forecast error ut as a function of the shocks ϵt appearing in the DSGE model. Starting from the prior distribution of the parameter θ from the DSGE models, obtain the mapping coefficient on the parameter Φ and Σu of the VAR parameters. Construct the joint prior distribution for the VAR and DSGE model parameters using the following structure. 2007) examines the marginal likelihood function of the hyperparameter λ to fit the DSGE model as.
Then use the modified harmonic mean estimator to obtain a numerical approximation of the marginal likelihood function based on the results of the MCMC calculations.
Zeta
CONCLUSIONS AND POLICY IMPLICATIONS
In this study, we develop a DSGE model for Indonesia to measure the effectiveness of the policy mix in mitigating pandemic shocks. Our results, based on the New Keynesian framework, show that the policy mix is the best way to accelerate the economic recovery process. Despite the existence of macroeconomic and financial links, this conclusion is still consistent with Bartsch et al.
However, we should be more concerned about the possibility that the policy mix will generate macroeconomic fluctuations. According to the IRF analysis, the policy combination produces the largest inflation response compared to other scenarios. Our results show that the combination of fiscal and monetary policy has a higher multiplier effect on aggregate demand.
Our results are consistent with Gali (2020), who argues that increased government spending combined with central bank policies aimed at price stability will provide a larger multiplier for growth. However, strong coordination between the fiscal and monetary authorities is required to reduce the impact of inflation risk with the aim of accelerating the economic recovery while maintaining macroeconomic stability. After that, our results are consistent with Liu et al. 2021), who argue that China's fiscal dominance may not result in well-grounded inflation expectations, leading to higher and more volatile inflation.
Our findings suggest a stronger credit balance, as fiscal policy can support household purchasing power through government transfers, leading to increased demand for housing stock from household borrowers. As a result, fiscal policy plays an important role in the policy mix by accelerating the recovery of the pandemic-hit economy. However, we believe that the successful use of its tools to achieve policy objectives requires cooperation between authorities.
Finally, as we have seen, the credit market imperfection has a substantial impact on macroeconomic fluctuations. We suggest that future studies include more frictions to have a better explanation of macroeconomic fluctuations. The policy mix backfires, Vox EU. https://cepr.org/voxeu/columns/stronger-together-policy-mix-strikes-back.
