Tests of Policy Interventions in DSGE Models

This paper considers tests of the effectiveness of a policy intervention, defined as a change in the parameters of a policy rule, in the context of a macroeconometric dynamic stochastic general equilibrium (DSGE) model. We consider two types of intervention, first the standard case of a parameter change that does not alter the steady state, and second one that does alter the steady state, e.g. the target rate of inflation. We consider two types of test, one a multi‐horizon test, where the postintervention policy horizon, H, is small and fixed, and a mean policy effect test where H is allowed to increase without bounds. The multi‐horizon test requires Gaussian errors, but the mean policy effect test does not. It is shown that neither of these two tests are consistent, in the sense that the power of the tests does not tend to unity as H→∞, unless the intervention alters the steady state. This follows directly from the fact that DSGE variables are measured as deviations from the steady state, and the effects of policy change on target variables decay exponentially fast. We investigate the size and power of the proposed mean effect test by simulating a standard three equation New Keynesian DSGE model. The simulation results are in line with our theoretical findings and show that in all applications the tests have the correct size; but unless the intervention alters the steady state, their power does not go to unity with H.     

Optimal Control of Heteroscedastic Macroeconomic Models

This paper analyses the implications of heteroscedasticity for optimal macroeconomic policy and welfare. We find that changes in the variance structure driven by exogenous processes like generalized autoregressive conditional heteroscedasticity (GARCH) affect welfare but not the optimal feedback rule. However, changes in the variance structure driven by state‐dependent processes affect both. We also derive certainty‐equivalent transformations of state‐dependent volatility models that allow standard quadratic dynamic programming algorithms to be employed to study optimal policy. These results are illustrated numerically using a reduced‐form model of the US economy in which changes in volatility are driven by a GARCH process and the rate of inflation. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting (aggregate) demand for US commercial air travel

We analyze whether it is better to forecast air travel demand using aggregate data at (say) a national level, or to aggregate the forecasts derived for individual airports using airport-specific data. We compare the US Federal Aviation Administration’s (FAA) practice of predicting the total number of passengers using macroeconomic variables with an equivalently specified AIM (aggregating individual markets) approach. The AIM approach outperforms the aggregate forecasting approach in terms of its out-of-sample air travel demand predictions for different forecast horizons. Variants of AIM, where we restrict the coefficient estimates of some explanatory variables to be the same across individual airports, generally dominate both the aggregate and AIM approaches. The superior out-of-sample performances of these so-called quasi-AIM approaches depend on the trade-off between heterogeneity and estimation uncertainty. We argue that the quasi-AIM approaches exploit the heterogeneity across individual airports efficiently, without suffering from as much estimation uncertainty as the AIM approach.     

Leaning into the Wind: A Structural VAR Investigation of UK Monetary Policy*

This paper adapts Uhlig's [Journal of Monetary Economics (2005) forthcoming] sign restriction identification methodology to investigate the effects of UK monetary policy using a structural vector autoregression (VAR). It shows that shocks which can reasonably be described as monetary policy shocks have played only a small role in the total variation of UK monetary and macroeconomic variables. Most of the variation in UK monetary variables has been due to their systematic reaction to other macroeconomic shocks, namely non‐monetary aggregate demand, aggregate supply, and oil price shocks. We also find, without imposing any long run identifying restrictions, that aggregate supply shocks have permanent effects on output.     

Aggregate vs. disaggregate data analysis—a paradox in the estimation of a money demand function of Japan under the low interest rate policy

We use Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of a liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglected heterogeneity among micro units. We provide necessary and sufficient conditions for the existence of a cointegrating relation among aggregate variables when heterogeneous cointegration relations among micro units exist. We also conduct simulation analysis to show that when such conditions are violated, it is possible to observe stable micro relations, but unit root phenomena among macro variables. Moreover, the prediction of aggregate outcomes, using aggregate data, is less accurate than the prediction based on micro equations, and policy evaluation based on aggregate data ignoring heterogeneity in micro units can be grossly misleading. Copyright © 2005 John Wiley & Sons, Ltd.     

Comparisons of aggregate variable forecasts using aggregate and disaggregate models

When forecasting aggregate variables, a choice must often be made to either add up individual forecasts made at a disaggregate level or to simply forecast at the aggregate level. The presence of heterogeneity introduces aggregation bias and makes the disaggregates approach more preferable, while the presence of data and specification errors introduces relatively large variances in the disaggregate forecasts, making the aggregate approach more preferable. It is suggested that the mean square error is useful in evaluating the combined effects of heterogeneity and specification and data errors, and in facilitating comparisons between aggregate and disaggregate approaches to aggregate variable forecasting.     

Predictive financial models of the euro area: A new evaluation test

This paper investigates the predictive ability of financial variables for euro area growth. Our forecasts are built from univariate autoregressive and single equation models. Euro area aggregate forecasts are constructed both by employing aggregate variables and by aggregating country-specific forecasts. The forecast evaluation is based on a recently developed test for equal predictive ability between nested models. Employing a monthly dataset from the period between January 1988 and May 2005 and setting the out-of-sample period to be from 2001 onwards, we find that the single most powerful predictor on a country basis is the stock market returns, followed by money supply growth. However, for the euro area aggregate, the set of most powerful predictors includes interest rate variables as well. The forecasts from pooling individual country models outperform those from the aggregate itself for short run forecasts, while for longer horizons this pattern is reversed. Additional benefits are obtained when combining information from a range of variables or combining model forecasts.     

Monetary policy rules for convergence to the Euro

This paper investigates the applicability of open-economy convergence-consistent instrument rules for monetary policies in the economies undergoing monetary convergence to a common currency area. The proposed policy rule is forward-looking, consistent with a monetary framework based on inflation-targeting containing input variables that are relative to the corresponding variables in the common currency area. Robust forms of the policy rule are tested empirically for three inflation-targeting countries converging to the euro, i.e. the Czech Republic, Poland and Hungary. Empirical tests imply systemic differences in monetary policies among these euro-candidates. The Czech monetary policy seemingly follows the rule prescribed by our model. Both the Czech and the Polish central bank interest rate policies respond predominantly to changes in the inflation gap, while the Hungarian responds mainly to the exchange rate gap. In all three cases, changes in the eurozone short-term interest rates strongly drive adjustments in the central banks’ reference interest rates.     

Three types of robust Ramsey problems in a linear-quadratic framework

This paper studies robust Ramsey policy problems in a general discrete-time linear-quadratic framework when the Ramsey planner faces three types of ambiguity. This framework includes both exogenous and endogenous state variables. In addition, the equilibrium system from the private sector contains both backward-looking and forward-looking dynamics. We provide recursive characterizations and algorithms to solve for robust policy. We apply our method to a basic New Keynesian model of optimal monetary policy with persistent cost-push shocks. We find that (i) all three types of ambiguity make optimal monetary policy more history-dependent but with different reasons for each type; and (ii) they deliver qualitatively different initial responses of inflation and the output gap following a cost-push shock.     

What drives dynamic connectedness of the U.S equity sectors during different business cycles?

The study investigates (i) the time-varying and directional connectedness of nine equity sectors through intra- and inter-sector volatility spillover periods and (ii) assesses the impact of state variables on aggregate volatility spillovers. The study finds about 76% of volatility linkage is associated with cross-sector volatility transmissions. Aggressive sectors, which are sensitive to macroeconomic risk, play the net volatility transmission role. Defensive sectors that are largely immune to macroeconomic risk play the net volatility receiving role. The intensity and direction of volatility transmissions among the sectors vary with economic expansion and recession periods. Over time, some sectors switching from net transmitting to net receiving role and vice versa. Macro and financial market uncertainty variables significantly impact volatility spillover at lower volatility spillover (economic expansion period) and higher volatility (economic recession periods) volatility spillover quantiles. Political signals are seemingly more imprecise and uninformative during economic expansion or low quantiles, intensifying volatility spillover. Overall, the causal effects of macro, financial, and policy uncertainty variables on aggregate volatility spillover are asymmetric, nonlinear, and time-varying. The study's result supports the cross-hedging and financial contagion views of volatility transmission across nine US equity sectors.     

Forecasting and decomposition of portfolio credit risk using macroeconomic and frailty factors

This paper presents a dynamic portfolio credit model following the regulatory framework, using macroeconomic and latent risk factors to predict the aggregate loan portfolio loss in a banking system. The latent risk factors have three levels: global across the entire banking system, parent-sectoral for the intermediate loan sectors and sector-specific for the individual loan sectors. The aggregate credit loss distribution of the banking system over a risk horizon is generated by Monte Carlo simulation, and a quantile estimator is used to produce the aggregate risk measure and economic capital. The risk contributions of the individual sectors and risk factors are measured by combining the Hoeffding decomposition with the Euler capital allocation rule. For the U.S. banking system, we find that the real GDP growth rate, the global and sector-wide frailty risk factors and their spillovers significantly affect loan defaults, and the impacts of the frailty factors are not only economy-wide but also sector-specific. We also find that the frailty risk factors make more significant risk contributions to the aggregate portfolio risk than the macroeconomic factors, while the macroeconomic factors help to improve the accuracy and efficiency of the credit risk forecasts.     

Solving DSGE models with a nonlinear moving average

We propose a nonlinear infinite moving average as an alternative to the standard state space policy function for solving nonlinear DSGE models. Perturbation of the nonlinear moving average policy function provides a direct mapping from a history of innovations to endogenous variables, decomposes the contributions from individual orders of uncertainty and nonlinearity, and enables familiar impulse response analysis in nonlinear settings. When the linear approximation is saddle stable and free of unit roots, higher order terms are likewise saddle stable and first order corrections for uncertainty are zero. We derive the third order approximation explicitly, examine the accuracy of the method using Euler equation tests, and compare with state space approximations.     

Which one should I imitate?

An individual repeatedly faces the same decision. Payoffs generated by his available actions are noisy. This individual forgets earlier experience and is limited to learn by observing current success of two other individuals. We select among behavioral rules that lead an infinite population of identical individuals in the long run to the expected payoff maximizing action. Optimal behavior requires learning by imitation. The second most successful in a sample must sometimes be imitated although the most successful will always be imitated with a higher probability. Inertia is optimal. When each individual follows an optimal rule, then the population evolves according to an aggregate monotone dynamic.     

A contextual analysis of power and foreign policy behavior

This article applies contextual analysis to state directed dyads and monads successfully achieving a more complete and empirically successful model of foreign policy behavior. While contextual analysis is a multilevel analysis integrating individual and group level variables, its logic can be applied to any unit of analysis and groups of such units. Benefits of contextual analysis include considering new explanatory models, in this case alternative uses of attribute magnitude in the directed dyad, and more comprehensive explanation by integrating variables often isolated at separate levels of analysis. A general proposition explaining volume of foreign policy behavior is advanced and then two more specific contextual models are delineated. Empirical testing with events data reveals a mixed additive interaction model to be far superior to previous population wide dyadic models.     

Liquidity traps and expectation dynamics: Fiscal stimulus or fiscal austerity?

We examine global dynamics under infinite-horizon learning in New Keynesian models where the interest-rate rule is subject to the zero lower bound. The intended steady state is locally but not globally stable. Unstable deflationary paths emerge after large pessimistic shocks to expectations. For large expectation shocks that push interest rates to the zero bound, a temporary fiscal stimulus, or in some cases a policy of fiscal austerity, will insulate the economy from deflation traps if the policy is appropriately tailored in magnitude and duration. A fiscal stimulus “switching rule,” which automatically kicks in without discretionary fine-tuning, can be equally effective.     

Solving the incomplete markets model with aggregate uncertainty using parameterized cross-sectional distributions

This note describes how the incomplete markets model with aggregate uncertainty in Den Haan et al. [Comparison of solutions to the incomplete markets model with aggregate uncertainty. Journal of Economic Dynamics and Control, this issue] is solved using standard quadrature and projection methods. This is made possible by linking the aggregate state variables to a parameterized density that describes the cross-sectional distribution. A simulation procedure is used to find the best shape of the density within the class of approximating densities considered. This note compares several simulation procedures in which there is—as in the model—no cross-sectional sampling variation.     

Comparison of solutions to the incomplete markets model with aggregate uncertainty

This paper compares numerical solutions to the model of Krusell and Smith [1998. Income and wealth heterogeneity in the macroeconomy. Journal of Political Economy 106, 867–896] generated by different algorithms. The algorithms have very similar implications for the correlations between different variables. Larger differences are observed for (i) the unconditional means and standard deviations of individual variables, (ii) the behavior of individual agents during particularly bad times, (iii) the volatility of the per capita capital stock, and (iv) the behavior of the higher-order moments of the cross-sectional distribution. For example, the two algorithms that differ the most from each other generate individual consumption series that have an average (maximum) difference of 1.63% (11.4%).     

CRASS KEYNESIANISM

Keynesianism supports make-work schemes without regard for consumers' preferences. The impact of fiscal and monetary policy in corrupting the flow of inter-temporal production is entirely discounted. Where Keynes argued that long-term considerations should not obstruct the implementation of short-term palliatives for immediate problems, Keynesianism fosters belief in the judgement of the state across a wide range of expenditures. In effect, however, Keynesian concepts of aggregate demand and the income multiplier set no basis for sound economic policy.     

A note on interactions-driven business cycles

Within the standard Keynesian multiplier framework, extended by a micro-model of interactive formation of individual consumption propensities, we demonstrate that socioeconomic interactions can lead to cyclical fluctuations in aggregate economic activity. The underlying micro-model of direct interactions is a version of Alan Kirman’s generic opinion formation model, with an additional feedback effect from macroscopic variables on the transition probabilities. Our model engenders cyclical fluctuations of economic variables, despite the fact that neither the Keynesian multiplier model nor Kirman’s model does so on its own.