An econometric strategy to identify a pre-announced fiscal policy shock is proposed. I show that the reduced form innovations can be recovered by estimating a Vector-moving-average model using the Kalman filter. The structural effects are identified exploiting the shock's pre-announced nature, which leads to potentially different signs of the responses of some endogenous variables during the announcement and after the realization of the shock. I illustrate my strategy by identifying a pre-announced shock to government consumption expenditures. I find that the response of private consumption is significantly negative on impact, rises and becomes significantly positive two quarters after the realization of the policy shock.

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.

We develop an evolutionary algorithm to estimate Threshold Vector Error Correction models (TVECM) with more than two cointegrated variables. Since disregarding a threshold in cointegration models renders standard approaches to the estimation of the cointegration vectors inefficient, TVECM necessitate a simultaneous estimation of the cointegration vector(s) and the threshold. As far as two cointegrated variables are considered, this is commonly achieved by a grid search. However, grid search quickly becomes computationally unfeasible if more than two variables are cointegrated. Therefore, the likelihood function has to be maximized using heuristic approaches. Depending on the precise problem structure the evolutionary approach developed in the present paper for this purpose saves 90 to 99 per cent of the computation time of a grid search.

While the long run relation between money and inflation is well established, empirical evidence on the adjustment to the long run equilibrium is very heterogeneous. In this paper we show, that the development of US consumer price inflation between 1960Q1 and 2005Q4 is strongly driven by money overhang. To this end, we use a multivariate state space framework that substantially expands the traditional vector error correction approach. This approach allows us to estimate the persistent components of velocity and GDP. A sign restriction approach is subsequently used to identify the structural shocks to the signal equations of the state space model, that explain money growth, inflation and GDP growth. We also account for the possibility that measurement error exhibited by simple-sum monetary aggregates causes the consequences of monetary shocks to be improperly identified by using a Divisia monetary aggregate. Our findings suggest that when the money is measured using a reputable index number, the quantity theory holds for the United States.

In this paper we challenge the view that corporate bonds are always arm's length debt. We analyze the effect of bond ratings on the stock price return to acquirers in M&A transactions, which tend to have significant effects on creditor wealth. We find acquirers abnormal returns to be higher if they are unrated, controlling for a wide variety of other effects identified in the literature. Tracing the difference in returns to distinct managerial decisions, we find that, everything else constant, rated firms increase their leverage in takeover transactions by less than their unrated counterparts. Consistent with a significant role for rating agencies, we find monitoring effects to be strongest when acquirer bonds are rated at the borderline between investment grade and junk. Finally, we are able to empirically exclude a large number of alternative explanations for the empirical regularities that we uncover.

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, Durbin, and Evans (1975) and Zeileis (2004), Nyblom (1989) and Hansen (1992), and Andrews, Lee, and Ploberger (1996). Power and size properties are derived using Monte Carlo simulations. Results emphasize that mostly the CUSUM type tests are affected by the presence of heteroscedasticity, whereas the individual parameter Nyblom test and AvgLM test are proved to be highly robust. However, each test is significantly affected by leptokurtosis. Contrarily to other tests, where skewness is far more problematic than kurtosis, it has no additional effect for any of the endogenous break tests we analyze. Concerning overall robustness the Nyblom test performs best, while being almost on par to more recently developed tests in terms of power.

In this paper we develop a small 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 recently published by the International

Monetary Fund (global projection model). Our main contribution is that we model the Euro area countries separately. In this version we consider Germany and France, which represent together about 50 percent of Euro area GDP. The model allows for country specific heterogeneity in the sense that we capture different adjustment patterns to economic shocks. The model is estimated using Bayesian techniques. Out-of-sample and pseudo out-of-sample forecasts are presented.

While the long run relation between money and inflation is well established, empirical evidence on the adjustment to the long run equilibrium is very heterogeneous. In the present paper we use a multivariate state space framework, that substantially expands the traditional vector error correction approach, to analyze the short run impact of money on prices. We contribute to the literature in three ways: First, we distinguish changes in velocity of money that are due to institutional developments and thus do not induce inflationary pressure, and changes that reflect transitory movements in money demand. This is achieved with a newly developed multivariate unobserved components decomposition. Second, we analyze whether the high volatility of the transmission from monetary pressure to inflation follows some structure, i.e., if the parameter regime can assumed to be constant. Finally, we use our model to illustrate the consequences of the monetary policy of the Fed that has been employed to mitigate the impact of the financial crisis, simulating different exit strategy scenarios.

We develop an evolutionary algorithm to estimate Threshold Vector Error Correction models (TVECM) with more than two cointegrated variables. Since

disregarding a threshold in cointegration models renders standard approaches

to the estimation of the cointegration vectors inefficient, TVECM necessitate a

simultaneous estimation of the cointegration vector(s) and the threshold. As far

as two cointegrated variables are considered this is commonly achieved by a grid

search. However, grid search quickly becomes computationally unfeasible if more

than two variables are cointegrated. Therefore, the likelihood function has to be

maximized using heuristic approaches. Depending on the precise problem structure

the evolutionary approach developed in the present paper for this purpose saves 90 to 99 per cent of the computation time of a grid search.

This paper investigates the forces driving output change in a panel of EU manufacturing industries. A flexible modeling strategy is adopted that accounts for: (i) inefficient use of resources and (ii) differences in the production technology across industries. With our model we are able to identify technical, efficiency, and input growth for endogenously determined technology clubs. Technology club membership is modeled as a function of R&D intensity. This framework allows us to explore the components of output growth in each club, technology spillovers and catch-up issues across industries and countries.