In this article, we examine the effect of estimation biases – introduced by model misspecification – on the impulse responses analysis for dynamic stochastic general equilibrium (DSGE) models. Thereby, we use full and limited information estimators to estimate a misspecified DSGE model and calculate impulse response functions (IRFs) based on the estimated structural parameters. It turns out that IRFs based on full information techniques can be unreliable under misspecification.

In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameter estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.

This paper analyzes the role of common data problems when identifying structural breaks in small samples. Most notably, we survey small sample properties of the most commonly applied endogenous break tests developed by Brown et al. (J R Stat Soc B 37:149–163, 1975) and Zeileis (Stat Pap 45(1):123–131, 2004), Nyblom (J Am Stat Assoc 84(405):223–230, 1989) and Hansen (J Policy Model 14(4):517–533, 1992), and Andrews et al. (J Econ 70(1):9–38, 1996). Power and size properties are derived using Monte Carlo simulations. We find that the Nyblom test is on par with the commonly used F type tests in a small sample in terms of power. While the Nyblom test’s power decreases if the structural break occurs close to the margin of the sample, it proves far more robust to nonnormal distributions of the error term that are found to matter strongly in small samples although being irrelevant asymptotically for all tests that are analyzed in this paper.

Fiscal spending multiplier calculations have attracted considerable attention in the aftermath of the global financial crisis. Much of the current literature is based on VAR estimation methods and DSGE models. In line with the Keynesian literature we argue that many of these models probably underestimate the fiscal spending multiplier in recessions. The income-expenditure model of the fiscal spending multiplier can be seen as a good approximation under these circumstances. In its conventional form this model suffers from an underestimation of the multiplier due to an overestimation of the import intake of domestic absorption. In this article we apply input-output calculus to solve this problem. Multipliers thus derived are comparably high, ranging between 1.4 and 1.8 for many member states of the European Union. GDP drops due to budget consolidation might therefore be substantial in times of crisis.

In this paper we develop an open economy model explaining the joint determination of output, inflation, interest rates, unemployment and the exchange rate in a multi-country framework. Our model -- the Halle Economic Projection Model (HEPM) -- is closely related to studies published by Carabenciov et al. Our main contribution is that we model the Euro area countries separately. In doing so, we consider Germany, France, and Italy which represent together about 70 percent of Euro area GDP. The model combines core equations of the New-Keynesian standard DSGE model with empirically useful ad-hoc equations. We estimate this model using Bayesian techniques and evaluate the forecasting properties. Additionally, we provide an impulse response analysis and a historical shock decomposition.

<p>We study the aggregate, distributional, and welfare effects of fiscal policy responses to Germany’s energy crisis using a novel Ten-Agents New-Keynesian (TENK) model. The energy crisis, compounded by the COVID-19 pandemic, led to sharp increases in energy prices, inflation, and significant consumption disparities across households. Our model, calibrated to Germany’s income and consumption distribution, evaluates key policy interventions, including untargeted and targeted transfers, a value-added tax cut, energy tax reductions, and an energy cost brake. We find that untargeted transfers had the largest short-term aggregate impact, while targeted transfers were most cost-effective in supporting lower-income households. Other instruments, as the prominent energy cost brake, yielded comparably limited welfare gains. These results highlight the importance of targeted fiscal measures in addressing distributional effects and stabilizing consumption during economic crises.</p>

I estimate a dynamic stochastic general equilibrium (DSGE) model for the United States that incorporates oil market shocks and risk shocks working through credit market frictions. The findings of this analysis indicate that risk shocks play a crucial role during the Great Recession and the Dot-Com bubble but not during other economic downturns. Credit market frictions do not amplify persistent oil market shocks. This result holds as long as entry and exit rates of entrepreneurs are independent of the business cycle.

This paper addresses a general problem with the use of surveys as source of information about the state of an economy: Answers to surveys are highly dependent on information that is publicly available, while only additional information that is not already publicly known has the potential to improve a professional forecast. We propose a simple procedure to disentangle the private information of agents from knowledge that is already publicly known for surveys that ask for general as well as for private prospects. Our results reveal the potential of our proposed technique for the usage of European Commissions‘ consumer surveys for economic forecasting for Germany.

Using data from the European Central Bank‘s Survey of Professional Forecasters, we analyse the role of ex-ante conditioning variables for macroeconomic forecasts. In particular, we test to which extent the heterogeneity, updating and ex-post performance of predictions for inflation, real GDP growth and the unemployment rate are related to assumptions about future oil prices, exchange rates, interest rates and wage growth. Our findings indicate that inflation forecasts are closely associated with oil price expectations, whereas expected interest rates are used primarily to predict output growth and unemployment. Expectations about exchange rates and wage growth also matter for macroeconomic forecasts, albeit less so than oil prices and interest rates. We show that survey participants can considerably improve forecast accuracy for macroeconomic outcomes by reducing prediction errors for external conditions. Our results contribute to a better understanding of the expectation formation process of experts.

We study the dynamic impact of Covid-19, economic mobility, and containment policy shocks. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through sign and zero restrictions. Incidence and mobility shocks raise cases and deaths significantly for two months. Restrictive policy shocks lower mobility immediately, cases after one week, and deaths after three weeks. Non-pharmaceutical interventions explain half of the variation in mobility, cases, and deaths worldwide. These flattened the pandemic curve, while deepening the global mobility recession. The policy tradeoff is 1 p.p. less mobility per day for 9% fewer deaths after two months.