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This paper aims to propose a dynamic stochastic general equilibrium (DSGE) model for the State Bank of Vietnam (SBV) to assess the response from the household sector to monetary policy shocks through the consumption function. Moreover, the transmission from monetary policy to household consumption and income distribution is experimented with through the vector autoregression (VAR) model.

In this study, the authors used the maximum likelihood estimation to estimate the DSGE and VAR models with the sample from 1996Q1 to the end of 2021Q4 (104 observations).

The DSGE model’s results show that the response of the household sector is as expected in the theory: a monetary policy shock occurs that increases the policy interest rate by 0.29%, leading to a decrease in consumer spending of about 0.041%, the shock fades after one year. Estimates from the VAR model give similar results: a monetary policy shock narrows income inequality after about 2–3 quarters and this process tends to slow down in the long run.

Based on the research results, the authors propose policy implications for the SBV to achieve the goal of price stability, and stabilizing the macro-economic environment in Vietnam.

The findings of the study have theoretical contributions and empirical scientific evidence showing the effectiveness of the implementation of the SBV’s monetary policy in the context of macro-instability, namely: flexibility, caution and coordination of different measures promptly.

Public debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.

Using quarterly observations over the period 2000–2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.

The results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.

Compared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.

The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.

This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.

The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.

The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

This study aims to construct alternative models to establish the dynamic relationship between exchange rates and housing affordability by estimating both the short- and long-run relationship between exchange rates and housing affordability for 18 OECD countries from 1975Q1 to 2022Q4. After that, this study demonstrates how this nexus behaves during high and low inflation regimes and turbulent times.

This study uses the panel autoregressive distributed lag technique to examine the nexus between housing affordability to capture the distinct characteristics of the sample countries and estimate various short- and long-run dynamics in the relationship between housing affordability and exchange rate.

Exchange rate appreciation improves housing affordability in the short run, whereas this connection tends to dissipate in the long run. Moreover, inflation can worsen housing affordability during turbulent times, such as the global financial crisis, in both the short and long run. Ignoring these changes in the relationship between exchange rates and housing affordability during turbulent times can lead to incorrect conclusions.

To the best of the authors’ knowledge, this study is the first to examine the association between exchange rates and housing affordability by demonstrating how these variables behave in high and low inflation regimes and turbulent times.