Estimated Dynamic Stochastic General Equilibrium Model Research Articles

Can China's energy-consumption permit trading scheme achieve the “Porter” effect? Evidence from an estimated DSGE model

The implementation of China's energy-consumption permit trading scheme (ECPTS) is a recent initiative to accelerate energy transition towards carbon neutrality; however, its impacts on the economy and environment have not been well studied. A dynamic stochastic general equilibrium (DSGE) model is employed to estimate the macroeconomic effect of the ECPTS. The results show that in the short term, the ECPTS could obtain environmental dividends but fails to achieve the “Porter” effect, and its energy-saving effect is only significant over the long run. When combined with the implementation of carbon tax, the negative impact of the ECPTS on final output could be narrowed but a long-term loss of social welfare is inevitable due to labor transfer costs between energy firms. Moreover, a low-carbon energy structure could weaken the macroeconomic effect of the ECPTS, which is in line with expectations as China's energy transition aims to achieve large-scale development of renewable energy. The findings support the Chinese government to expand the regional coverage of the ECPTS and explore a coordination mechanism with carbon pricing policy. The government is also suggested to improve the legislation and supporting measures of the ECPTS to weaken its negative impact on the macroeconomy.

The driving forces of China's business cycles: Evidence from an estimated DSGE model with housing and banking

We study the approximate sources of China's business cycles in an estimated dynamic stochastic general equilibrium (DSGE) model with housing and banking. The model replicates well the volatility and cyclicality of key macroeconomic variables observed in the past two decades in China. A host of shock decomposition exercises demonstrate that, among the shocks being considered, both financial and housing shocks are driving China's business cycles, accounting for a particularly large fraction of the variance in most macroeconomic and financial variables at the business cycle frequencies. In particular, the capital quality, housing demand, and loan-to-value shocks display prominent contributions to the business cycle fluctuations. Moreover, there exists substantial interactions between the banking and housing sectors in China, where the collateral constraint and the financial constraint amplify with each other. The results shed new light in the understanding of China's business cycles, and may serve as a useful benchmark for future quantitative analyses of China's macroeconomic fluctuations using DSGE frameworks.

Turkish Housing Market Dynamics: An Estimated DSGE Model

In this article, we investigate the underlying driving dynamics behind house price variations in Turkey by estimating a dynamic stochastic general equilibrium (DSGE) model in which the housing market and collateral constraints are included. The model also analyses the interaction between macroeconomic variables and the housing market by making policy simulations under different loan-to-value (LTV) ratios, which are used as a housing market-specific economic policy tool. The model is extended by including the traditional Taylor rule with house prices for representing monetary policy. Our findings show that house prices in Turkey are largely explained by housing preference shocks. Besides, we find that monetary policy shock plays a small role in determining the variables of the housing market in the short-term period. However, the magnitude of the impact of housing market shocks on the rest of the economy depends on the LTV ratios. The higher the LTV ratio, the higher are the effects of the government’s housing policy instrument for stabilising the housing market on real macroeconomic variables such as consumption and output in Turkey. Finally, our findings show that the fluctuations in house prices have not played a substantial role in the monetary policy reaction function of Turkey. JEL Codes: E32, E52, E44, E51, R31

Monetary Policy and Financial Exclusion in an Estimated DSGE Model of Sub-Saharan African Economies

ABSTRACTThis paper examines the effectiveness of monetary policy and its implications for financially included and excluded households in Sub-Saharan African (SSA) economies, using an estimated New-Keynesian dynamic stochastic general equilibrium (DSGE) model. The model has financially included households coexisting with financially excluded households. We exploit time series data on four SSA economies, spanning 1985–2016, to estimate the model’s parameters through Bayesian inference methods. Our estimation results show that the share of financially excluded households in these economies is relatively small, usually between 35% and 42%. Further, our Bayesian impulse response analysis shows that a positive monetary policy shock significantly reduced inflation and output, despite a sizeable fraction of the population is financially excluded. Additionally, we find that contractionary monetary policy tends to have differentiated impacts; it decreases the consumption of financially excluded households more than that of financially included ones. The results reveal that financially included households are able to absorb shocks, and thus can smooth consumption more effectively than financially excluded households. Generally, although an increase in the number of financially excluded households reduces the effects of interest rate policies, we find an opposite result: the effectiveness of monetary policy improves as the fraction of financially included households falls.

Too Many Shocks Spoil the Interpretation

We show that when a model has more shocks than observed variables the estimated filtered and smoothed shocks will be correlated. This is despite no correlation being present in the data generating process. Additionally the estimated shock innovations may be autocorrelated. These correlations limit the relevance of impulse responses, which assume uncorrelated shocks, for interpreting the data. Excess shocks occur frequently, e.g. in Unobserved-Component (UC) models, filters, including Hodrick- Prescott (1997), and some Dynamic Stochastic General Equilibrium (DSGE) models. Using several UC models and an estimated DSGE model, Ireland (2011), we demonstrate that sizable correlations among the estimated shocks can result.

Too Many Shocks Spoil the Interpretation

We show that when a model has more shocks than observed variables the estimated filtered and smoothed shocks will be correlated. This is despite no correlation being present in the data generating process. Additionally the estimated shock innovations may be autocorrelated. These correlations limit the relevance of impulse responses, which assume uncorrelated shocks, for interpreting the data. Excess shocks occur frequently, e.g. in Unobserved-Component (UC) models, filters, including Hodrick-Prescott (1997), and some Dynamic Stochastic General Equilibrium (DSGE) models. Using several UC models and an estimated DSGE model, Ireland (2011), we demonstrate that sizable correlations among the estimated shocks can result.

The transmission of financial shocks in an estimated DSGE model with housing and banking

We develop a dynamic stochastic general equilibrium (DSGE) model with housing and banking to study the transmission of financial shocks between the financial and real sectors. A deterioration in the bank's balance sheet induced by financial shocks could have amplified and persistent impacts on real activities. The amplification of the shocks are originated from financial frictions tied to households and banks. We find that a disruption in bank net worth initiated by capital quality shocks generates a decline in household loans, house prices and output. Bank liquidity shocks also have negative effects on these variables. Housing preference shocks could generate a positive comovement between house prices and output. All these findings are qualitatively consistent with empirical evidence, suggesting that these financial shocks are critical to the dynamics of house prices and other macroeconomic variables.

The macroeconomic effects of quantitative easing in the euro area: Evidence from an estimated DSGE model

This paper estimates an open-economy dynamic stochastic general equilibrium (DSGE) model with Bayesian techniques to provide a structural empirical evaluation of the macroeconomic effects of the European Central Bank's (ECB's) quantitative easing (QE) programme. Allowing for cross-border holding of government debt and using data on government debt stocks and yields across maturities, we identify the parameters governing portfolio rebalancing in the private sector. We rely on a methodological extension that measures the non-linear contribution of QE in shock decompositions under an occasionally binding constraint (zero lower bound). Our results suggest an average contribution of ECB QE to annual Euro Area GDP growth and CPI inflation in 2015–18 of 0.3 and 0.5 percentage points, respectively, with a maximum impact in 2016.

Measuring the Impact of Population Aging on Tax Revenue: Evidence from Japan and Korea

This paper assesses empirically the tax implications of population aging in Japan and Korea. We achieve the research objectives by taking two approaches: (1) a vector autoregression (VAR) analysis; and (2) an estimated dynamic stochastic general equilibrium (DSGE) framework. Based on data from 1973 to 2015, the VAR results for both countries reveal that an aging shock decreases GDP, hours, consumption, and investment. As the tax bases fall, so do tax revenues. Conditioned on estimated DSGE models, the second approach is to characterize Laffer curves at two steady states: the current (as of 2015) and aged (as of 2060) states. We find that the adverse effect of population aging on tax revenues is more serious for Korea than for Japan. Even in the aged state, Japan has room to generate tax revenues comparable to the current state, whereas combinations of tax rates satisfying this property do not exist for Korea.

Bayesian Estimation of Macro-Finance DSGE Models with Stochastic Volatility

We develop a Bayesian Markov chain Monte Carlo algorithm for estimating risk premia in dynamic stochastic general equilibrium (DSGE) models with stochastic volatility. Our approach is fully Bayesian and employs an affine solution strategy that makes estimation of large-scale DSGE models computationally feasible. We use our algorithm to estimate the US equity risk premium in a DSGE model that includes time-preference, technology, investment, and volatility shocks. Time-preference and technology shocks are primarily responsible for the sizable equity risk premium in the estimated DSGE model. The estimated historical stochastic volatility and equity risk premium series display pronounced countercyclical fluctuations.

Observed Inflation-Target Adjustments in an Estimated DSGE Model for Indonesia: Do They Matter for Aggregate Fluctuations?

This paper investigates the role of observed official inflation-target adjustments in aggregate macroeconomic fluctuations in Indonesia, using an estimated Dynamic Stochastic General Equilibrium (DSGE) model. The paper finds that these adjustments or shocks play a non-trivial role in the fluctuations of inflation and nominal interest rate in Indonesia. Output fluctuations, however, are virtually unaffected. A counterfactual exercise shows that a gradual reduction in Bank Indonesia's inflation target may have not been optimal. The paper also provides additional insights on the contribution of various shocks in driving aggregate fluctuations in Indonesia. Technology and monetary-policy shocks are found to be the main driving factor for both output and inflation fluctuations. Movements in the nominal interest rate are mostly driven by preference and risk-premium shocks, with inflation-target shocks playing a larger role in the longer run. The inclusion of inflation-target shocks in the model is also shown to improve the model's fit and out-of-sample predictive performance.

Environmental tax shocks and carbon emissions: An estimated DSGE model

We develop an energy-environment-economy dynamic stochastic general equilibrium (DSGE) model with five sectors, including households, energy, government, final goods, and the environment. The model is used to assess the response of China's carbon emissions to environmental tax shocks. The core of the model establishes an exponential function of environmental taxes that are mapped against the share of fossil energy in the total energy use. The model parameters are obtained through a combination of calibration and Bayesian estimation. Using the impulse response analysis and variance decomposition, we find that environmental tax shocks can drive the reduction of carbon emissions in China. However, the mitigating effect of the environmental tax shocks can be weakened by some other exogenous shocks, and that China's carbon emissions are likely to grow over a long period of time. Moreover, environmental tax shocks can improve energy structure by promoting the introduction of clean energy which can reduce carbon emissions.

An Estimated DSGE Model to Analyze Housing Market Policies in Hong Kong SAR

During the last decade, Hong Kong SAR has experienced a large increase in house prices and credit, prompting the authorities to respond with several rounds of tightening macroprudential rules and increasing stamp duty taxes. This paper provides a Dynamic Stochastic General Equilibrium (DSGE) model for Hong Kong SAR and analyzes the effectiveness of these measures, and finds that they have helped reduce house price appreciation and household leverage. A baseline small open economy real business cycle model is extended by including a housing sector, financial frictions, foreign demand for the domestic housing stock, and is estimated using Bayesian methods and data for Hong Kong SAR between 1996 and 2017. The paper finds that, without these policies, house prices would have been 10.5 percent higher, and the household credit-GDP ratio 14 percent higher.

House prices and macroprudential policy in an estimated DSGE model of New Zealand

We analyse macroprudential and monetary policies and their interactions using an estimated dynamic stochastic general equilibrium (DSGE) model tailored to New Zealand. Our estimates show that monetary policy has large spillover effects on house prices, however it has not been a major driver of house prices in New Zealand which have instead been driven by sector-specific shocks. We consider macroprudential policies, including the loan-to-value restrictions that have been implemented in New Zealand. We find that loan-to-value restrictions reduce house prices with negligible effects on consumer prices, suggesting they can be used without derailing monetary policy. Efficient policy frontier analysis suggests macroprudential policy is a useful addition to monetary policy, and the two do not need to be coordinated. We estimate that the loan-to-value restrictions imposed in New Zealand in 2013 reduced house prices by 3.8% and that greater forward guidance on their duration would have made them more effective.

An Estimated DSGE Model to Analyze Housing Market Policies in Hong Kong SAR

During the last decade, Hong Kong SAR has experienced a large increase in house prices and credit, prompting the authorities to respond with several rounds of tightening macroprudential rules and increasing stamp duty taxes. This paper provides a Dynamic Stochastic General Equilibrium (DSGE) model for Hong Kong SAR and analyzes the effectiveness of these measures, and finds that they have helped reduce house price appreciation and household leverage. A baseline small open economy real business cycle model is extended by including a housing sector, financial frictions, foreign demand for the domestic housing stock, and is estimated using Bayesian methods and data for Hong Kong SAR between 1996 and 2017. The paper finds that, without these policies, house prices would have been 10.5 percent higher, and the household credit-GDP ratio 14 percent higher.

What Drives the Owner‐Occupied and Rental Housing Markets? Evidence from an Estimated DSGE Model

Most dynamic stochastic general equilibrium (DSGE) models with a housing market do not explicitly include a rental market and assume a tight mapping between house prices and rents over the business cycle. However, rents are much smoother than house prices in the data. We match this feature of the data by adding both an owner‐occupied housing market and a rental market in a standard DSGE model. The intertemporal preference shock accounts for more than half of the variation in house prices and contributes to residential investment fluctuations through the liquidity constraint, and nominal rigidity in rental contracts captures the variation in the price‐rent ratio.

House Prices and Macroprudential Policy in an Estimated DSGE Model of New Zealand

We analyse the effects of macroprudential and monetary policies and their interactions using an estimated dynamic stochastic general equilibrium (DSGE) model tailored to New Zealand. We find that the main historical drivers of house prices are shocks specific to the housing sector. While our estimates show that monetary policy has large spillover effects on house prices, it does not appear to have been a major driver of house prices in New Zealand. We consider macroprudential policies, including the loan-to-value restrictions that have been implemented in New Zealand. We find that loan-to-value restrictions reduce house prices with negligible effects on consumer prices, suggesting that they can be used without derailing monetary policy. We estimate that the loan-to-value restrictions imposed in New Zealand in 2013 reduced house prices by 3.8 per cent and that greater forward guidance on their duration would have made them more effective.

Dealing with financial instability under a DSGE modeling approach with banking intermediation: A predictability analysis versus TVP-VARs

In the dynamic stochastic general equilibrium (DSGE) literature there has been an increasing awareness on the role that the banking sector can play in macroeconomic activity. We present a DSGE model with financial intermediation as in Gertler and Karadi (2011). The estimation of shocks and of the structural parameters shows that time-variation should be crucial in any attempted empirical analysis. Since DSGE modelling usually fails to take into account inherent nonlinearities of the economy, we propose a novel time-varying parameter (TVP) state-space estimation method for VAR processes both for homoskedastic and heteroskedastic error structures. We conduct an exhaustive empirical exercise to compare the out-of-sample predictive performance of the estimated DSGE model with that of standard ARs, VARs, Bayesian VARs and TVP-VARs. We find that the TVP-VAR provides the best forecasting performance for the series of GDP and net worth of financial intermediaries for all steps-ahead, while the DSGE model outperforms the other specifications in forecasting inflation and the federal funds rate at shorter horizons.

Why has economic growth slowed down in Indonesia? An investigation into the Indonesian business cycle using an estimated DSGE model

Economic growth in Indonesia has been trending down from about 6.5% in 2010 to less than 5% recently. Calibrating and estimating a dynamic stochastic general equilibrium (DSGE) model of Indonesia, we show that most of Indonesia’s growth over the last decade has been driven by supply factors, especially rising multi-factor productivity (MFP) as Indonesia reaped the benefits of post-Asian-crisis structural reforms. The pace of multi-factor productivity growth has slowed since 2010, however, a decelerating trend reinforced by slower world growth. A series of interest rate cuts has successfully managed to offset some of those headwinds. However, absent further structural reforms to revive productivity growth, supportive monetary policy will not be sufficient to sustain long-term growth and poses inflation risks.

Zero Lower Bound and Parameter Bias in an Estimated DSGE Model

This paper examines how and to what extent parameter estimates can be biased in a dynamic stochastic general equilibrium (DSGE) model that omits the zero lower bound constraint on the nominal interest rate. Our experiments show that most of the parameter estimates in a standard sticky-price DSGE model are not biased although some biases are detected in the estimates of the monetary policy parameters and the steady-state real interest rate. Nevertheless, in our baseline experiment, these biases are so small that the estimated impulse response functions are quite similar to the true impulse response functions. However, as the probability of hitting the zero lower bound increases, the biases in the parameter estimates become larger and can therefore lead to substantial differences between the estimated and true impulse responses.