Working papers results

We analyze the effect of judicial errors on the innovative activity of firms.
If successful, the innovative effort allows to take new actions that may be ex-post wel-
fare enhancing (legal) or decreasing (illegal). Deterrence in this setting works by affecting
the incentives to invest in innovation (average deterrence). Type-I errors, through over-
enforcement, discourage innovative effort while type-II errors (under-enforcement) spur it.
The ex-ante expected welfare effect of innovations shapes the optimal policy design. When
innovations are ex-ante welfare improving, laissez-faire is chosen. When innovations are
instead welfare decreasing, law enforcement should limit them through average deterrence.
We consider several policy environments differing in the instruments available. Enforcement
effort is always positive and fines are (weakly) increasing in the social loss of innovations. In
some cases accuracy is not implemented, contrary to the traditional model where it always
enhances (marginal) deterrence, while in others it is improved selectively only on type-II
errors (asymmetric protocols of investigation).

In 2003 the Brazilian central government (CG) launched an anti-corruption program. Since then municipalities have been randomly selected to be audited on a monthly basis. Evidence in the literature suggests that the probability of re-election of an incumbent mayor decreases as the number of reported corruption violations rises before the municipal elections. By exploiting the exogenous variation in the timing of the release of the audit reports and the Brazilian institutional scheme, this paper sheds light on the mechanisms through which the Brazilian anti-corruption program functions. After the release of the audit reports, municipalities where more than two corruption violations were reported receive 26% fewer transfers from the CG. Total expenditure on infrastructure is also reduced. While the CG increases the amount of transfers to municipalities where the mayor is both affiliated with the partys president and found to be honest, it helps politically aligned municipalities with high levels of released corruption to move through the punishment process more quickly. The effects of the dissemination of corruption information on the probability of re-election of incumbent mayors seem to gradually disappear with time. Yet, when these effects have completely faded and voters have time to feel the consequences of receiving fewer transfers, the probability of re-election of corrupt politicians decreases.

This paper brings together several important strands of the econometrics literature: errorcorrection,
cointegration and dynamic factor models. It introduces the Factor-augmented Error
Correction Model (FECM), where the factors estimated from a large set of variables in levels
are jointly modelled with a few key economic variables of interest. With respect to the standard
ECM, the FECM protects, at least in part, from omitted variable bias and the dependence of
cointegration analysis on the specific limited set of variables under analysis. It may also be in
some cases a refinement of the standard Dynamic Factor Model (DFM), since it allows us to
include the error correction terms into the equations, and by allowing for cointegration prevent
the errors from being non-invertible moving average processes. In addition, the FECM is a
natural generalization of factor augmented VARs (FAVAR) considered by Bernanke, Boivin and
Eliasz (2005) inter alia, which are specified in first differences and are therefore misspecified in
the presence of cointegration. The FECM has a vast range of applicability. A set of Monte Carlo
experiments and two detailed empirical examples highlight its merits in finite samples relative to
standard ECM and FAVAR models. The analysis is conducted primarily within an in-sample
framework, although the out-of-sample implications are also explored.

We conduct a detailed simulation study of the forecasting performance of
diffusion index-based methods in short samples with structural change. We
consider several data generation processes, to mimic different types of
structural change, and compare the relative forecasting performance of factor
models and more traditional time series methods. We find that changes in the
loading structure of the factors into the variables of interest are extremely
important in determining the performance of factor models. We complement
the analysis with an empirical evaluation of forecasts for the key
macroeconomic variables of the Euro area and Slovenia, for which relatively
short samples are officially available and structural changes are likely. The
results are coherent with the findings of the simulation exercise, and confirm
the relatively good performance of factor-based forecasts also in short samples
with structural change.

This paper compares different ways to estimate the current state of the economy using factor
models that can handle unbalanced datasets. Due to the different release lags of business cycle
indicators, data unbalancedness often emerges at the end of multivariate samples, which is some-
times referred to as the 'ragged edge' of the data. Using a large monthly dataset of the German
economy, we compare the performance of different factor models in the presence of the ragged edge:
static and dynamic principal components based on realigned data, the Expectation-Maximisation
(EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors
are used to estimate current quarter GDP, called the 'nowcast', using different versions of what
we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible
combinations of factor estimation methods and Factor-MIDAS projections with respect to now-
cast performance. Additionally, we compare the performance of the nowcast factor models with
the performance of quarterly factor models based on time-aggregated and thus balanced data,
which neglect the most timely observations of business cycle indicators at the end of the sample.
Our empirical findings show that the factor estimation methods don't differ much with respect
to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection
performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor
models that can exploit ragged-edge data.

A reduction in income tax rates generates substantial dynamic responses within the frame-
work of the standard neoclassical growth model. The short-run revenue loss after an in-
come tax cut is partly -- or, depending on parameter values, even completely -- offset
by growth in the long-run, due to the resulting incentives to further accumulate capital.
We study how the dynamic response of government revenue to a tax cut changes if we
allow a Ramsey economy to engage in international trade: the open economy's ability to
reallocate resources between labor-intensive and capital-intensive industries reduces the
negative effect of factor accumulation on factor returns, thus encouraging the economy to
accumulate more than it would do under autarky. We explore the quantitative implica-
tions of this intuition for the US in terms of two issues recently treated in the literature:
dynamic scoring and the Laffer curve. Our results demonstrate that international trade
enhances the response of government revenue to tax cuts by a relevant amount. In our
benchmark calibration, a reduction in the capital-income tax rate has virtually no effect
on government revenue in steady state.

This paper studies monetary policy in the Euro area looking at the
variable most directly related to current and expected monetary policy,
the yield on long term government bonds. We find that the level of longterm
rates in Europe is almost entirely explained by U.S. shocks and by
the systematic response of U.S. and European variables (inflation, short
term rates and the output gap) to these shocks. Our results suggest in
particular that U.S. variables are more important than local variables
in the policy rule followed by European monetary authorities: this was
true for the Bundesbank before EMU and has remained true for the
ECB, at least so far. Using closed economy models to analyze monetary
policy in the Euro is thus inconsistent with the empirical evidence on the
determinants of Euro area long-term rates. It is also inconsistent with
the way the Governing Council of the ECB appears to make actual policy
decisions.