Comparing different data descriptors in Indirect Inference tests on DSGE models

Indirect inference testing can be carried out with a variety of auxiliary models. Asymptotically these different models make no difference. However, in small samples power can differ. We explore small sample power with three different auxiliary models: a VAR, average Impulse Response Functions and Moments. The latter corresponds to the Simulated Moments Method. We find that in a small macro model there is no difference in power. But in a large complex macro model the power with Moments rises more slowly with increasing misspecification than with the other two which remain similar

Classical or gravity? which trade model best matches the UK facts?

We examine the empirical evidence bearing on whether UK trade is governed by a Classical model or by a Gravity model, using annual data from 1965 to 2015 and the method of Indirect Inference which has very large power in this application. The Gravity model here differs from the Classical model in assuming imperfect competition and a positive effect of total trade on productivity. We found that the Classical model passed the test comfortably, and that the Gravity model also passed it but at a rather lower level of probability, though as the test power was raised it was rejected. The two models’ policy implications are similar

Indirect Inference- a methodological essay on its role and applications

In this short paper we review the intellectual history of indirect inference as a methodology in its progress from an informal method for evaluating early models of representative agents to formally testing DSGE models of the economy; and we have considered the issues that can arise in carrying out these tests. We have noted that it is asymptotically equivalent to using FIML-i.e. in large samples; and that in small samples it is superior to FIML both in lowering bias and achieving good power. In application its power needs to be evaluated by Monte Carlo experiment for the particular context. Structural models need to be defined in terms of their scope of application and auxiliary models chosen suitably to test their applicability within this scope. Power can be set too high by using too many auxiliary model features to match; and it can be pushed too low by using too few. Excessively high shocks, such as wars and crises, may also limit a model's applicability by causing unusual behaviour that cannot be captured by the model. If so, these need to be excluded so that the model is evaluated for the 'normal times' in which it is applicable

Indirect inference and small sample bias — Some recent results

Macroeconomic researchers use a variety of estimators to parameterise their models empirically. One such is FIML; another is indirect inference (II). One form of indirect inference is ‘informal’ whereby data features are ‘targeted’ by the model — i.e. parameters are chosen so that model-simulated features replicate the data features closely. Monte Carlo experiments show that in the small samples prevalent in macro data, both FIML informal II produce high bias, while formal II, in which the joint probability of the data- generated auxiliary model is maximised under the model simulated distribution, produces low bias. They also show that FII gets this low bias from its high power in rejecting misspecified models, which comes in turn from the fact that this distribution is restricted by the model-specified parameters, so sharply distinguishing it from rival misspecified models

Comparing behavioural and rational expectations for the US post-war economy

The banking crisis has caused a resurgence of interest in behavioural models of expectations in macroeconomics. Here we evaluate behavioural and rational expectations econometrically in a New Keynesian framework, using US post-war data and the method of indirect inference. We find that after full reestimation the model with behavioural expectations is strongly rejected by the data, whereas the standard rational expectation version passes the tests by a substantial margin

Can a small New Keynesian model of the world economy with risk-pooling match the facts?

We ask whether a model of the US and Europe trading with the rest of the world can match the facts of world behaviour in a powerful indirect inference test. One version has uncovered interest parity (UIP), the other risk‐pooling. Both pass the test but the most probable is risk‐pooling. This is consistent with risk‐pooling failing a number of single‐equation tests, as has been found in past work; we show that these tests will typically reject risk‐pooling when it in fact prevails. World economic behaviour under risk‐pooling shows much stronger spillovers than under UIP with opposite monetary responses to the exchange rate. We argue that the risk‐pooling model therefore demands more attention from policy‐makers

Stabilisation policy, rational expectations and price-level versus inflation targeting: a survey

We survey literature comparing inflation targeting (IT) and price-level targeting (PT) as macroeconomic stabilisation policies. Our focus is on New Keynesian models and areas that have seen significant developments since Ambler's (2009, Price-level targeting and stabilisation policy: a survey. Journal of Economic Surveys 23(5): 974–997) survey: optimal monetary policy; the zero lower bound; financial frictions and transition costs of adopting a PT regime. Ambler's conclusion that PT improves social welfare in New Keynesian models is fairly robust, but we note an interesting split in the literature: PT consistently outperforms IT in models where policymakers commit to simple Taylor-type rules, but results in favour of PT when policymakers minimise loss functions are overturned with small deviations from the baseline model. Since the beneficial effects of PT appear to hang on the joint assumption that agents are rational and the economy New Keynesian, we discuss survey and experimental evidence on rational expectations and the applied macro literature on the empirical performance of New Keynesian models. Overall, the evidence is not clear-cut, but we note that New Keynesian models can pass formal statistical tests against macro data and that models with rational expectations outperform those with behavioural expectations (i.e. heuristics) in direct statistical tests. We therefore argue that policymakers should continue to pay attention to PT

"Does inattentiveness matter for DSGE modelling? An empirical investigation",

The purpose of this paper is to investigate the empirical performance of the standard New Keynesian dynamic stochastic general equilibrium (DSGE) model in its usual form with full-information rational expectations and compare it with versions assuming inattentiveness—namely sticky information and imperfect information data revision. Using a Bayesian estimation approach on US quarterly data (both real-time and survey) from 1969 to 2015, we find that the model with sticky information fits best and is the only one that can generate the delayed responses observed in the data. The imperfect information data revision model is improved fits better when survey data is used in place of real-time data, suggesting that it contains extra information

A DSGE model of China

We use available methods for testing macro models to evaluate a model of China over the period from Deng Xiaoping’s reforms up until the crisis period. Bayesian ranking methods are heavily influenced by controversial priors on the degree of price/wage rigidity. When the overall models are tested by Likelihood or Indirect Inference methods, the New Keynesian model is rejected in favour of one with a fair-sized competitive product market sector. This model behaves quite a lot more ‘flexibly’ than the New Keynesian