Bayesian Estimation

Understanding the dynamics of productivity shocks is instrumental if we are to identify the sources of economic growth. This paper, investigates dynamic effects of positives productivity shocks to agricultural subsectors during the period from 1991-2015, by disaggregating agricultural sector in Iran into four key subsectors (crops, livestock, fishing and forestry) through an estimated DSGE model. Our Bayesian estimation results suggest that positive productivity shocks lead to an increase in output, consumption, capital, employment and real wages and a fall in marginal costs and price indexes in all four subsectors. Comparing the results across the subsectors shows that following the shocks, generally, crops and livestock have the strongest reactions and forestry has the weakest ones. Additionally, among the variables, output indicates the highest responses to the shocks. Variance decomposition analysis reveals that agricultural fluctuations are mainly explained by productivity, monetary, preference and government spending shocks.

The recent housing market experience has led many to concern that the developments in the housing sector are not just a passive reflection of macroeconomic activities but instead might be one of the driving forces of them. In this context, it is crucial to understand the nature of the economy by considering the housing market and build a suitable monetary policy. In this paper, using Bayesian methods, we develop and estimate a DSGE model for Iran from 1988q1 to 2017q4, which explicitly models the housing in the banks’ portfolio to study the macroeconomic effects of monetary authority’s reaction to the housing price inflation. Our findings indicate that this reaction amplifies all the implications of the structural shocks.