In this paper, we study main problems and practical issues of modeling and forecasting of macroeconomic variables in the national economy. For that, we employ astructural VAR models and estimate interdependencies among different economic variables. Initial data analysis of macroeconomic...

In this study, we investigate forecasting performance of various univariate and multivariate models in predicting inflation for different horizons. We design our forecast experiment for the post-oil boom years of 2010-2014 and compare forecasting ability of the different models with that of...

This paper estimates a stylized search and matching model on data for Australia covering the period 1978-2008. Using Bayesian methods we find that the model does a fairly good job in replicating the data. Surprisingly, we find a large value for the worker’s bargaining power and low vacancy...

This paper estimates with the Bayesian methods a CES production function for Singapore for 1960-2009. It is found that the elasticity of substitution is 0.6, technical progress is labour augmenting and the steady state growth rate of Singapore is about 1.8%.

In this paper, I present a unified and micro-founded explanation for various types of inflation without assuming ad hoc frictions or irrationality. The explanation is similar to the conventional inflation theory in the sense that an independent central bank can control inflation and also similar...

This paper provides a simple epidemiology model where households, when forming their inflation expectations, rationally adopt the past release of inflation with certain probability rather than the forward-looking newspaper forecast as suggested in Carroll [2003, Macroeconomic Expectations of...

The evolution of inflation and output over the last 50 years is examined through the lens of a micro-founded model that allows for changes in the behavior of the Federal Reserve and in the volatility of structural shocks. Agents are aware of the possibility of regime changes and their beliefs...

In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, which gives posterior predictive densities as a byproduct. We show that the...