动态随机一般均衡模型： 文献研究与未来展望

动态随机一般均衡 （DSGE） 模型是近10多年来宏观经济学发展的主要标志物。它为研究经济增长和经济波动提供了统一的分析框架。DSGE模型的产生和发展大体上经历了实际的经济周期（RBC）模型和新凯恩斯主义DSGE模型两个阶段。DSGE模型是一种动态模型,又考虑了随机冲击,还具有所需数据信息较少的简约化特征,因而受到政策制定者尤其是中央银行的青睐。DSGE模型现在在理论基础、理论模型与实际数据的匹配性、模型的估计方法等方面还存在这样那样的问题,但是这个模型体现着现代宏观经济学的许多共识和发展趋势,随着贝叶斯方法与DSGE模型的有机融合,随着计算机性能和计算能力的不断提高,DSGE模型将会得到进一步发展和改进。     

CONSISTENT VARIANCE OF THE LAPLACE‐TYPE ESTIMATORS: APPLICATION TO DSGE MODELS

The Laplace‐type estimator has become popular in applied macroeconomics, in particular for estimation of dynamic stochastic general equilibrium (DSGE) models. It is often obtained as the mean and variance of a parameter's quasi‐posterior distribution, which is defined using a classical estimation objective. We demonstrate that the objective must be properly scaled; otherwise, arbitrarily small confidence intervals can be obtained if calculated directly from the quasi‐posterior distribution. We estimate a standard DSGE model and find that scaling up the objective may be useful in estimation with problematic parameter identification. It this case, however, it is important to adjust the quasi‐posterior variance to obtain valid confidence intervals.     

Teaching courses in macroeconomics and monetary policy with Bloomberg analytics

In this article, the authors illustrate the use of Bloomberg for analyzing topics in macroeconomics and monetary policy in economics and finance courses. The hands-on experience that students gain from such a course has many benefits, including deeper learning and clearer understanding of data. The authors describe goals and learning objectives, then compare Bloomberg with Federal Reserve Economic Data (FRED). In addition, they provide examples of how to use Bloomberg in the classroom, describe how to have students perform sector analysis, show how Bloomberg tools are useful for analyzing monetary policy, discuss how to use Bloomberg to analyze the financial sector, and illustrate the platform’s use in a case study.     

Hayek, Keynes, and modern macroeconomics

The Great Recession seems to be creating a change in the trend of macroeconomic thinking. Prior to the financial crisis of 2008, dynamic stochastic general equilibrium (DSGE) models dominated the macroeconomics literature without any apparent challengers on the horizon. Since then, however, we have seen an increasing interest in macroeconomic models that address the state of confidence (??animal spirits??), complexity, cognition, and radical uncertainty. Most of the renewed interest in animal spirits, complexity, cognition, and radical uncertainty has come from a more or less ??Keynesian?? perspective. We discuss the potential to emphasize these elements from a more ??Hayekian?? perspective and argue that Austrian approaches to macroeconomics along these lines are more likely to resonate with mainstream economists than in years past.     

On the predictability of time-varying VAR and DSGE models

Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic fluctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspecifications as they are able to solve the trade-off between theoretical coherence and empirical fit. However, these models are still linear and they do not consider time variation for parameters. The time-varying properties in VAR or DSGE models capture the inherent nonlinearities and the adaptive underlying structure of the economy in a robust manner. In this article, we present a state-space time-varying parameter VAR model. Moreover, we focus on the DSGE–VAR that combines a microfounded DSGE model with the flexibility of a VAR framework. All the aforementioned models as well simple DSGEs and Bayesian VARs are used in a comparative investigation of their out-of-sample predictive performance regarding the US economy. The results indicate that while in general the classical VAR and BVARs provide with good forecasting results, in many cases the TVP–VAR and the DSGE–VAR outperform the other models.     

Teaching Inflation Targeting: An Analysis for Intermediate Macro

Over the last decade, many central banks have adopted policies known as inflation targeting. If intermediate-level macroeconomics students are to be prepared to think about current policy issues, it is important to provide them with an introduction to the macroeconomic implications of inflation targeting. Unfortunately, the standard aggregate demand-aggregate supply frameworks commonly used to teach intermediate macroeconomics are not well suited for this task because they are expressed in terms of output and the price level and because they fail to make explicit the policy objectives of the central bank. The author provides a simple graphical device involving the output gap and the inflation rate that overcomes these problems and that can be used to teach intermediate macroeconomics students about inflation targeting.     

Indeterminate forecast accuracy under indeterminacy

This paper studies whether the observed time variation in the forecast accuracy of macro-econometric models can be reconciled with the monetary policy stance that induces (in)determinacy in stylized DSGE models. Using a small-scale New Keynesian monetary framework as laboratory and structural parameters calibrated to the estimates obtained on U.S. data from different macroeconomics regimes, we exploit reduced-form econometric models – such as Vector Autoregressions – to assess their regime-specific forecastability. We show that conducting (pseudo) out-of-sample forecast comparisons in the presence of indeterminacy is a non-trivial exercise, even when sunspot shocks play no role in generating the data. Overall, our simulation experiment suggests that equilibrium indeterminacy need not lead to superior (absolute or relative) forecast accuracy. This finding challenges the view that the deteriorating performance of forecast models over the Great Moderation relative to the Great Inflation was entirely due to changes in the U.S. monetary policy.     

On the effects of rare disasters and uncertainty shocks for risk premia in non-linear DSGE models

This paper studies how rare disasters and uncertainty shocks affect risk premia in DSGE models approximated to second and third order. Based on an extension of the results in Schmitt-Grohé and Uribe (2004) to third order, we derive propositions for how rare disasters, stochastic volatility, and GARCH affect any type of risk premia in a wide class of DSGE models. To quantify the effects, we set up a standard New Keynesian DSGE model where total factor productivity includes rare disasters, stochastic volatility, and GARCH. We find that rare disasters increase the level of the 10-year nominal term premium, whereas a key effect of uncertainty shocks, i.e. stochastic volatility and GARCH, is an increase in the variability of this premium.     

Programmed Learning-Policy Analysis: An Experiment in Teaching Principles of Economics

Darnton reports on an experiment using programmed instruction in macroeconomics. He also explains how assignments to write “position papers” on economic policies are used. An analysis of the experiment deals with the time devoted to the course by students and instructor, student understanding of economics and student attitude toward this approach. The TUCE was used as the testing instrument, and Darnton asserts that the experimental group did better than the control group on complex application questions. The experimental approach proved to be popular with the students, and the author claims that it provided for a more efficient use of faculty resources.     

The Optimal Level and Disposition of Saving and the Mix of Monetary and Fiscal Policy in Intermediate Macroeconomics

The optimal level of saving and its optimal disposition between investment in the domestic capital stock and the accumulation of overseas assets should be an important topic in intermediate macroeconomics courses. This article shows how the analysis of this topic can be presented to students.