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Pesaran and Smith (2011) concluded that Dynamic Stochastic General Equilibrium (DSGE) models were sometimes a straitjacket which hampered the ability to match certain features of the data. In this chapter, the authors look at how one might assess the fit of these models using a variety of measures, rather than what seems to be an increasingly common device – the Marginal Data Density. The authors apply these in the context of models by Christiano, Motto, and Rostagno (2014) and Ireland (2004), finding they fail to make a match by a large margin. Both of these models feature more shocks than observed variables, resulting in the empirical shocks having a singular density, and so making them correlated. When correlated one can neither interpret impulse responses nor perform variance decompositions. Against this, there is a strong argument for having a straitjacket, as it enforces some desirable behavior on models and makes researchers think about how to account for any non-stationarity in the data. The authors illustrate this with examples drawn from the SVAR literature and also more eclectic models such as Holston, Laubach, and Williams (2017) for extracting an estimate of the real natural rate.

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

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.

The chapter considers parameter identification, estimation, and model diagnostics in medium scale DSGE models from a frequency domain perspective using the framework developed in Qu and Tkachenko (2012). The analysis uses Smets and Wouters (2007) as an illustrative example, motivated by the fact that it has become a workhorse model in the DSGE literature. For identification, in addition to checking parameter identifiability, we derive the non-identification curve to depict parameter values that yield observational equivalence, revealing which and how many parameters need to be fixed to achieve local identification. For estimation and inference, we contrast estimates obtained using the full spectrum with those using only the business cycle frequencies to find notably different parameter values and impulse response functions. A further comparison between the nonparametrically estimated and model implied spectra suggests that the business cycle based method delivers better estimates of the features that the model is intended to capture. Overall, the results suggest that the frequency domain based approach, in part due to its ability to handle subsets of frequencies, constitutes a flexible framework for studying medium scale DSGE models.

This paper aims to develops an estimable hybrid model that combines the micro‐founded DSGE model with the flexibility of the atheoretical VAR model.

The model is estimated via the maximum likelihood technique based on quarterly data on real gross national product (GNP), consumption, investment and hours worked, for the South African economy, over the period of 1970:1 to 2000:4. Based on a recursive estimation using the Kalman filter algorithm, the out‐of‐sample forecasts from the hybrid model are then compared with the forecasts generated from the Classical and Bayesian variants of the VAR for the period 2001:1‐2005:4.

The results indicate that, in general, the estimated hybrid‐DSGE model outperforms the classical VAR, but not the Bayesian VARs in terms of out‐of‐sample forecasting performances.

The model lacks nominal shocks and needs to be extended into a small open economy framework.

The paper was able to show that, even though the DSGE model is outperformed by the BVAR, a microfounded theoretical DSGE model has a future in forecasting the South African economy.

To the best of the authors' knowledge, this is the first attempt to use an estimable DSGE model to forecast the South African economy.

The primary purpose of this paper is to investigate the sources of the business cycle fluctuations in Vietnam. To this end, the author develops a small open economy New Keynesian dynamic stochastic general equilibrium (SOE-NK-DSGE) model. Accordingly, this model includes various features, such as habit consumption, staggered price, price indexation, incomplete exchange-rate pass-through (ERPT), the failures of the law of one price (LOOP) and the uncovered interest rate parity. It is then estimated by using the Bayesian technique and Vietnamese data 1999Q1–2017Q1. Based on the estimated model, this paper analyzes the sources of the business cycle fluctuations in this emerging economy. Indeed, this research paper is the first attempt at developing and estimating the SOE-NK-DSGE model with the Bayesian technique for Vietnam.

A SOE-NK-DSGE model—Bayesian estimation.

This paper analyzes the sources of the business cycle fluctuations in Vietnam.

This research paper is the first attempt at developing and estimating the SOE-NK-DSGE model with the Bayesian technique for Vietnam.

This paper surveys the treatment of expectations in estimated Dynamic Stochastic General Equilibrium (DSGE) macroeconomic models.

A recent notable development in the empirical macroeconomics literature has been the rapid growth of papers that build structural models, which include a number of frictions and shocks, and which are confronted with the data using sophisticated full-information econometric approaches, often using Bayesian methods.

A widespread assumption in these estimated models, as in most of the macroeconomic literature in general, is that economic agents' expectations are formed according to the Rational Expectations Hypothesis (REH). Various alternative ways to model the formation of expectations have, however, emerged: some are simple refinements that maintain the REH, but change the information structure along different dimensions, while others imply more significant departures from rational expectations.

I review here the modeling of the expectation formation process and discuss related econometric issues in current structural macroeconomic models. The discussion includes benchmark models assuming rational expectations, extensions based on allowing for sunspots, news, sticky information, as well as models that abandon the REH to use learning, heuristics, or subjective expectations.

Long-term interest rates of small open economies (SOE) correlate strongly with the USA long-term rate. Can central banks in those countries decouple from the United States? An estimated Dynamic Stochastic General Equilibrium (DSGE) model for the UK (vis-á-vis the USA) establishes three structural empirical results: (1) Comovement arises due to nominal fluctuations, not through real rates or term premia; (2) the cause of comovement is the central bank of the SOE accommodating foreign inflation trends, rather than systematically curbing them; and (3) SOE may find themselves much more affected by changes in USA inflation trends than the United States itself. All three results are shown to be intuitive and backed by off-model evidence.

The purpose of this paper is to compare business cycle fluctuations in Ethiopia under interest rate and money growth rules.

In order to achieve this objective, the author constructs a medium-scale open economy dynamic stochastic general equilibrium (DSGE) model. The model features several nominal and real distortions including habit formation in consumption, price rigidity, deviation from purchasing power parity and imperfect capital mobility. The paper also distinguishes between liquidity-constrained and Ricardian households. The model parameters are calibrated for the Ethiopian economy based on data covering the period January 2000–April 2015.

The main result suggests that: the model economy with money growth rule is substantially less powerful or more muted for the amplification and transmission of exogenous shocks originating from government spending programs, monetary policy, technological progress and exchange rate movements. The responses of output to fiscal policy shocks are relatively stronger under autarky which appears to confirm the findings of Ilzetzki et al. (2013) who suggest bigger multipliers in self-sufficient, closed economies. With regard to positive productivity shock, however, the model with interest rate feedback rule generates a decline in output and an increase in inflation, which are at odds with conventional empirical regularities.

The major implication is that a central bank regulating some measure of monetary stocks should not expect (fear) as much expansion (contraction) in output following currency devaluation (liquidity withdrawal) as a sister central bank that relies on an interest rate feedback rule. As emphasized by Mishra et al. (2010) the necessary conditions for stronger transmission of interest-rule-based monetary policy shocks are hardly existent in emerging and developing economies targeting monetary aggregates; hence the relatively weaker responses of output and inflation in the model economy with money growth rule. Monetary policy authorities need to be cautious when using DSGE models to analyze business cycle dynamics. Quite often, DSGE models tend to mimic the proverbial “crooked house” built to every man’s advise. Whenever additional modification is made to an existing baseline model, previously established regularities break down. For instance, this paper documented negative response of output to technology shock. Such contradictions are not uncommon. For example, Furlanetto (2006) and Ramayandi (2008) have also found similarly inconsistent responses to fiscal and productivity shocks, respectively.

Using DSGE models for research and teaching purposes is not common in developing economies. To the best of the author’s knowledge, only one other Ethiopian author did apply DSGE model to study business cycle fluctuation in Ethiopia albeit under the implausible assumption of perfect capital mobility and a central bank following interest rate rule. The contribution of this paper is that it departs from these two unrealistic assumptions by allowing international risk premium as a function of the net foreign asset position of the country and by applying money growth rule which closely mimics the behavior of central banks in low-income economies such as Ethiopia.

F. A. Hayek’s macroeconomic theory and policy ideas have gained renewed attention since the cheap-money boom until 2007, and subsequent bust, followed the basic Hayekian narrative. Only to a very limited extent, however, do we find Hayek’s ideas on the agenda of mainstream macroeconomic researchers since Robert Lucas’s research program gave way to “Neoclassical” and “New Keynesian” DSGE models. We find examples of deeper interest on the periphery of the mainstream. Hayek’s influence on today’s macroeconomic policy discussions remains similarly limited, although he has become an icon to some opponents of loose monetary policy.