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Most existing studies on the impact of tourism on economic growth adopt an econometric approach that is insufficient to confirm that tourism actually leads to economic growth. Moreover, it cannot explain the causalities of different variables. Taking Mauritius as an example, this study aims to use the dynamic stochastic general equilibrium approach to investigate the contribution of tourism to economic growth when there is a productivity shock in the tourism sector.

A two-sector, small, open economy is modelled under the dynamic stochastic general equilibrium framework. The model is estimated using the Bayesian method based on real tourism and macroeconomic data from Mauritius for the period from 1999 to 2014. The impulse response functions are used to simulate the contribution of tourism to economic growth when there is a productivity shock in the tourism sector.

The simulation results show that the Mauritian gross domestic product (GDP) would increase by 0.09 per cent if the productivity of tourism is improved by 1 per cent, indicating that tourism could lead to economic growth. Considering the average annual growth rate of the Mauritian GDP, the contribution of tourism to its economic growth is significant. Furthermore, the effects of tourism on economic growth are moderated by price elasticities in international tourism demand.

This is the first study that estimates the dynamic stochastic general equilibrium model using the Bayesian method in tourism economic field. By correcting the prior information with real tourism and macroeconomic data, the estimation and simulation results are more robust compared with the calibration method, which has been used frequently in tourism studies.

The Laplace-type estimator (LTE) is a simulation-based alternative to the classical extremum estimator that has gained popularity in applied research. We show that even though the estimator has desirable asymptotic properties, in small samples the point estimate provided by LTE may not necessarily converge to the extremum of the sample objective function. Furthermore, we suggest a simple test to verify if the estimator converges. We illustrate these results by estimating a prototype dynamic stochastic general equilibrium model widely used in macroeconomics research.

In this chapter we approach the estimation of dynamic stochastic general equilibrium models through a moments-based estimator, the empirical likelihood. We attempt to show that this inference process can be a valid alternative to maximum likelihood, which has been one of the preferred choices of the related literature to estimate these models. The empirical likelihood estimator is characterized by a simple setup and only requires knowledge about the moments of the data generating process of the model. In this context, we exploit the fact that these economies can be formulated as a set of moment conditions to infer on their parameters through this technique. For illustrational purposes, we consider a standard real business cycle model with a constant relative risk averse utility function and indivisible labor, driven by a normal technology shock.

We design experimental economies based on a New Keynesian Dynamic Stochastic General Equilibrium (DSGE) model. We apply shocks to tastes, productivity, and interest rate policy, and measure the persistence of these shocks. We find that, in a setting where goods are perfect substitutes, there is little persistence of output shocks compared to treatments with monopolistic competition, which perform similarly irrespective of whether or not menu costs are present. Discretionary central banking is associated with greater persistence than automated instrumental rules.

The authors use a reduced form state-dependent labor participation decision model to illustrate that parameter stability is achieved only if a model properly takes account the observed sample heterogeneity and unobserved sample heterogeneity provided (external) conditions of a model stay constant. Our analysis of the dynamic response path to a health shock using Australian HILDA panel data from 2002 to 2009 shows that experiencing an event by itself can only have a temporary effects. The long-run equilibrium condition is independent of initial conditions or shocks that do not last. In other words, if experiencing an event does not lead to changes in the response parameters such as the real business cycle (Kydland & Prescott, 1977, 1982) or dynamic stochastic general equilibrium model (DSGE, e.g., Sbordone et al., 2010) assumed, policy change may only change the short-run response path. There is no long-term impact for a policy change. On the other hand, if a policy change leads to changes in the decision rules (e.g., the recent US–China trade friction) as the Lucas critique (1976) implies, then there is no other way to evaluate the impact of a policy except to explicitly model how agents respond to the policy change.

China's economic transition is essentially the process of China's institutional changes. During the changes, the appearance of institutional innovation is not regular; instead, it is intermittent and random. The purpose of this paper is to show that the fitful appearance of institutional innovation is the root of China's economic growth and fluctuations.

This paper constructs a real business cycle (RBC) model introducing the institutional factor expressed in the quantitative form under the dynamic stochastic general equilibrium (DSGE) framework by measuring China's institutional changes quantitatively.

By comparing the characteristics of the actual economic data with those of the simulated economic data, we find that this RBC model can explain 94.44%, 66.07%, 23.46%, 21.03% and 15.45% of the cyclical fluctuations in output, investment, labor, consumption and capital, respectively.

The impulse response analysis finds that the institutional shocks have a relatively long duration, lasting about 30 years, and decline slowly over time, while technological shocks decline relatively fast, lasting approximately ten years.

The purpose of this paper is to investigate the effects of news shocks on monetary policies using the dynamic stochastic general equilibrium (DSGE) model. To this end, two kinds of news shocks (known as technology and consumer preferences) are defined according to Khan and Tsoukalas’ (2012) approach.

In order to construct and simulate the DSGE model to approaching the real conditions in a case study, consumption habits in the utility function were concerned based on the assumption of the zero-value obtained from multiplying the inflation by the real interest rate in the Fisher’s equation, whereas the real interest rates in the long run were appointed as negative remark in simulating the monetary policy models. The estimation and simulation results for the research models indicated that monetary policies using the interest rate instrument identified the news shocks less frequently than monetary policies using the monetary base instrument.

The approximate value of the social loss function in the optimal commitment and discretionary monetary policies suggests that the optimal commitment policy is estimated to be lower in both cases. Due to value of the social loss function in optimal monetary policies with nominal interest rate instrument in the presence of news shocks, this could be claimed that monetary policy with interest rate instrument is more appropriate than the monetary policy with a monetary base instrument.

The approximate value of the social loss function in the optimal commitment and discretionary monetary policies suggests that the optimal commitment policy is estimated to be lower in both cases.

This article reviews the literature on the econometric relationship between DSGE and VAR models from the point of view of estimation and model validation. The mapping between DSGE and VAR models is broken down into three stages: (1) from DSGE to state-space model; (2) from state-space model to VAR( ∞ ); (3) from VAR( ∞ ) to finite-order VAR. The focus is on discussing what can go wrong at each step of this mapping and on critically highlighting the hidden assumptions. I also point out some open research questions and interesting new research directions in the literature on the econometrics of DSGE models. These include, in no particular order: understanding the effects of log-linearization on estimation and identification; dealing with multiplicity of equilibria; estimating nonlinear DSGE models; incorporating into DSGE models information from atheoretical models and from survey data; adopting flexible modeling approaches that combine the theoretical rigor of DSGE models and the econometric model’s ability to fit the data.