Nonlinear Dynamic Stochastic General Equilibrium Research Articles

Desirable banking competition and stability

Every financial crisis raises questions about how the banking market structure affects the real economy. Although low bank concentration may reduce markups and foster riskier behavior, concentrated banking systems appear more resilient to financial shocks. We use a nonlinear dynamic stochastic general equilibrium model with financial frictions to compare the transmissions of shocks under different competition and concentration configurations. The results reveal that oligopolistic competition amplifies the effects of the shocks relative to monopolistic competition. The transmission mechanism works through the markups, which are amplified when banking concentration is increased. The desirable banking market structure is determined according to financial stability and social welfare objectives. Moreover, we find that depending on policymakers’ preferences, a banking concentration of five to eight banks balances social welfare and bank stability objectives in the United States.

The Block-Correlated Pseudo Marginal Sampler for State Space Models

Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly. Our article develops an efficient PMMH method that scales up better to higher dimensional state vectors than previous approaches. The improvement is achieved by the following innovations. First, a novel block version of PMMH that works with multiple particle filters is proposed. Second, the trimmed mean of the unbiased likelihood estimates of the multiple particle filters is used. Third, the article develops an efficient auxiliary disturbance particle filter, which is necessary when the bootstrap disturbance filter is inefficient, but the state transition density cannot be expressed in closed form. Fourth, a novel sorting algorithm, which is as effective as previous approaches but significantly faster than them, is developed to preserve the correlation between the logs of the likelihood estimates at the current and proposed parameter values. The performance of the sampler is investigated empirically by applying it to nonlinear Dynamic Stochastic General Equilibrium models with relatively high state dimensions and with intractable state transition densities and to multivariate GARCH diffusion-driven volatility in the mean models. Although we only apply the method to state space models, the approach will be useful in a wide range of applications such as large panel data models and stochastic differential equation models with mixed effects.

Welfare cost of inflation, when credit card transaction services are included among monetary services

Abstract We investigate the welfare cost of anticipated inflation, when the volume of credit card transactions is included in measured monetary service flows. We use the credit-card-augmented Divisia monetary aggregates in a nonlinear dynamic stochastic general equilibrium (DSGE) New Keynesian model and calculate the welfare costs of inflation. The welfare costs of inflation with credit card services included are greater than without them in the New Keynesian DSGE model. Because of the complexity of the model’s dynamical structure, we are not aware of a simple explanation for the increased welfare sensitivity to inflation.

Sequential Monte Carlo with model tempering

Abstract Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and two nonlinear dynamic stochastic general equilibrium models. The runtime reductions we obtain range from 27 % to 88 %.

Estimating a Nonlinear New Keynesian Model with the Zero Lower Bound for Japan

AbstractWhich type of monetary policy rule best describes the policy conducted by the Bank of Japan (BOJ) during the period when the nominal interest rate is constrained at the zero lower bound (ZLB)? What are the economic fundamentals that explain Japan's prolonged stagnation? How important is incorporating nonlinearities in the analysis? We answer these questions by estimating a small‐scale nonlinear dynamic stochastic general equilibrium (DSGE) model. We find that: the BOJ conducted a threshold‐based forward guidance policy; adverse demand shocks explain Japan's experience; and nonlinear models are very useful in the analysis of the Japanese economy during the ZLB period.

Asymmetric Effects of Monetary Policy

Abstract In this paper, we first use a structural vector autoregression model to examine whether the US economy responds asymmetrically to expansionary and contractionary monetary policies. The empirical results show that monetary policy has significant asymmetric effects on output and investment. To provide an explanation of such asymmetries, we consider a nonlinear dynamic stochastic general equilibrium (DSGE) model in which collateral constraints are occasionally binding over the business cycle. The nonlinear DSGE model is able to match the empirical findings that macroeconomic aggregates react asymmetrically to positive and negative monetary policy shocks.

Switching volatility in a nonlinear open economy

Uncertainty about an economy’s regime can change drastically around a crisis. An imported crisis such as the global financial crisis in the euro area highlights the effect of foreign shocks. Estimating an open-economy nonlinear dynamic stochastic general equilibrium model for the euro area and the United States including Markov-switching volatility shocks, we show that these shocks were significant during the global financial crisis compared with periods of calm. We describe how US shocks from both the real economy and financial markets affected the euro area economy and how bond reallocation occurred between short- and long-term maturities during the global financial crisis. Importantly, the estimated nonlinearities when domestic and foreign financial markets influence the economy, should not be neglected. The nonlinear behavior of market-related variables highlights the importance of higher-order estimation for providing additional interpretations to policymakers.

Switching Volatility in a Nonlinear Open Economy

Uncertainty about an economy’s regime can change drastically around a crisis. An imported crisis such as the global financial crisis in the euro area highlights the effect of foreign shocks. Estimating an open-economy nonlinear dynamic stochastic general equilibrium model for the euro area and the United States including Markov-switching volatility shocks, we show that these shocks were significant during the global financial crisis compared with periods of calm. We describe how U.S. shocks from both the real economy and financial markets affected the euro area economy and how bond reallocation occurred between short- and long-term maturities during the global financial crisis. Importantly, the estimated nonlinearities when domestic and foreign financial markets influence the economy should not be neglected. The nonlinear behavior of market-related variables highlights the importance of higher-order estimation for providing additional interpretations to policymakers.

Review of Solution and Estimation Methods for Nonlinear Dynamic Stochastic General Equilibrium Models with the Zero Lower Bound

We review solution and estimation methods for nonlinear dynamic stochastic general equilibrium models and their application, with a special focus on the zero lower bound on the nominal interest rate. In a fully nonlinear setting, both the solution and estimation methods involve iterative procedures, and their computational expense grows rapidly with an increase in the dimensionality of state variables and parameters. We describe how the procedures deal with the dimensionality problem.

Forecasting using a Nonlinear DSGE Model

Abstract A medium-scale nonlinear dynamic stochastic general equilibrium (DSGE) model was estimated (54 variables, 29 state variables, 7 observed variables). The model includes an observed variable for stock market returns. The root-mean square error (RMSE) of the in-sample and out-of-sample forecasts was calculated. The nonlinear DSGE model with measurement errors outperforms AR (1), VAR (1) and the linearised DSGE in terms of the quality of the out-of-sample forecasts. The nonlinear DSGE model without measurement errors is of a quality equal to that of the linearised DSGE model.

The Changing Transmission of Uncertainty Shocks in the U.S.

This article investigates if the impact of uncertainty shocks on the U.S. economy has changed over time. To this end, we develop an extended factor augmented vector autoregression (VAR) model that simultaneously allows the estimation of a measure of uncertainty and its time-varying impact on a range of variables. We find that the impact of uncertainty shocks on real activity and financial variables has declined systematically over time. In contrast, the response of inflation and the short-term interest rate to this shock has remained fairly stable. Simulations from a nonlinear dynamic stochastic general equilibrium (DSGE) model suggest that these empirical results are consistent with an increase in the monetary authorities’ antiinflation stance and a “flattening” of the Phillips curve. Supplementary materials for this article are available online.

An auxiliary particle filter for nonlinear dynamic equilibrium models

We develop a particle filter algorithm to approximate the likelihood function of nonlinear dynamic stochastic general equilibrium models. The new algorithm reduces the Monte Carlo variance of likelihood approximation and accelerates the convergence of posterior sampler. It requires much fewer particles to achieve comparable results as currently available particle filters. We illustrate our algorithm in Bayesian estimation of a new Keynesian macroeconomic model.

Forecasting macroeconomic data for an emerging market with a nonlinear DSGE model

This paper considers the forecasting performance of a nonlinear dynamic stochastic general equilibrium (DSGE) model. The results are compared with those of a wide selection of competing models, which include a linear DSGE model and a variety of vector autoregressive (VAR) models. The parameters in the VAR models are estimated with classical and Bayesian techniques, where some of the Bayesian models are augmented with stochastic variable selection, time-varying parameters, endogenous structural breaks and various forms of prior shrinkage (where the Minnesota prior is included as a special case). The structure of the DSGE models follow that of New Keynesian varieties, which allow for nominal and real rigidities. The nonlinear DSGE model makes use of the second-order solution method of Schmitt-Grohé and Uribe (2004), and a particle filter is used to generate values for the unobserved variables. Most of the parameters in these models are estimated using maximum likelihood techniques. The models are applied to the macroeconomic data of South Africa, which is classified as an emerging market economy. The initial in-sample period of 1960Q1 to 1999Q4 is used to generate an eight-step ahead forecast. The models are then estimated recursively, by extending the in-sample period by a quarter, to generate successive forecasts over the out-of-sample period 2000Q1 to 2011Q4. We find that the forecasting performance of the nonlinear DSGE model is almost always superior to that of its linear counterpart, particularly over longer forecasting horizons. The nonlinear DSGE model also outperforms the selection of VAR models in most cases.

Estimating nonlinear DSGE models by the simulated method of moments: With an application to business cycles

This paper studies the application of the simulated method of moments (SMM) to the estimation of nonlinear dynamic stochastic general equilibrium (DSGE) models. Monte-Carlo analysis is employed to examine the small-sample properties of SMM in specifications with different curvatures and departures from certainty equivalence. Results show that SMM is computationally efficient and delivers accurate estimates, even when the simulated series are relatively short. However, the small-sample distribution of the estimates is not always well approximated by the asymptotic Normal distribution. An empirical application to the macroeconomic effects of skewed disturbances shows that negatively skewed productivity shocks induce agents to accumulate additional capital and can generate asymmetric business cycles.

Estimating Nonlinear DSGE Models by the Simulated Method of Moments

This paper studies the application of the simulated method of moments (SMM) for the estimation of nonlinear dynamic stochastic general equilibrium (DSGE) models. Monte Carlo analysis is employed to examine the small-sample properties of SMM in specifications with different curvature. Results show that SMM is computationally efficient and delivers accurate estimates, even when the simulated series are relatively short. However, asymptotic standard errors tend to overstate the actual variability of the estimates and, consequently, statistical inference is conservative. A simple strategy to incorporate priors in a method of moments context is proposed. An empirical application to the macroeconomic effects of rare events indicates that negatively skewed productivity shocks induce agents to accumulate additional capital and can endogenously generate asymmetric business cycles.