This paper documents spillover effects using participation in an elite international football tournament as a laboratory. Using a novel dataset from top 5 European football leagues, we find that participation in highly selective UEFA Champions League (UCL) generates large performance gains to participating teams in their domestic leagues. More precisely, UCL participation improves goal differences (goals scored minus goals conceded) by approximately 0.3 goals per game and probability margin (probability of winning minus probability of losing) by approximately 10 percentage points. By investigating causal channels through which participation in the UCL might affect performance, we argue that our results suggest the importance of spillover effects in sports.
Are Small-Scale SVARs Useful for Business Cycle Analysis? Revisiting Non-Fundamentalness, Joint with Fabio Canova (Journal of European Economic Association)
Non-fundamentalness arises when observables do not contain enough information to recover the vector of structural shocks. Using Granger causality tests, the literature suggested that many small scale VAR models are non-fundamental and thus not useful for business cycle analysis. We show that causality tests are problematic when VAR variables are cross sectionally aggregated or proxy for non-observables. We provide an alternative testing procedure, illustrate its properties with a Monte Carlo exercise, and reexamine the properties of two prototypical VAR models.
I develop a procedure to decompose stock prices into a value and a noise component, and derive the restrictions that market efficiency imposes on these components. This provides a novel methodology to measure the Rate of Market Efficiency (RME) across countries and over time. Further, the formal statistical tests can be supplemented by a graphical diagnostic to date-stamp and quantify market inefficiency (i.e., bubble). I find that the RME varies through time in a cyclical fashion, reaching its minimum in the 1990s. My estimates suggest a positive bubble during the dot-com period and a negative bubble during the Great Recession.
This paper provides new conditions under which the shocks recovered from the estimates of structural vector autoregressions are fundamental. I prove that the Wold innovations are unpredictable if and only if the model is fundamental. I propose a test based on a generalized spectral density to check the unpredictability of the Wold innovations. The test is applied to study the dynamic effects of government spending on economic activity. I find that standard SVAR models commonly employed in the literature are non-fundamental. Moreover, I formally show that introduction of a narrative variable that measures anticipation restores fundamentalness.
Non-fundamentalness arises when observed variables do not contain enough information to recover structural shocks. This paper propose a new test to empirically detect non-fundamentalness, which is robust to the conditional heteroskedasticity of unknown form, does not need information outside of the specified model and could be accomplished with a standard F-test. A Monte Carlo study based on a DSGE model is conducted to examine the finite sample performance of the test. I apply the proposed test to the U.S. quarterly data to identify the dynamic effects of supply and demand disturbances on real GNP and unemployment.
Testing for Noncausal Vector Autoregressive Representation
We propose a test for noncausal vector autoregressive representation generated by non-Gaussian shocks. We prove that in these models the Wold innovations are martingale difference if and only if the model is correctly specified. We propose a test based on a generalized spectral density to check for martingale difference property of the Wold innovations. Our approach does not require to identify and estimate the noncausal models. No specific estimation method is required, and the test has the appealing nuisance parameter free property. The test statistic uses all lags in the sample and it has a convenient asymptotic standard normal distribution under the null hypothesis. A Monte Carlo study is conducted to examine the finite-sample performance of our test.
Other Publications (pre-PhD)
The Effects of Entry Regulation on Bank Competition: The Case of the Iranian Banking Industry,Journal of Applied Economics,Volume 15, I, 2012.
We focus on a modified version of the markup test to investigate the impact of entry regulation on competitive conditions in the Iranian banking industry for the period 1996-2006. The time interval under examination corresponds to an era characterized by substantial relaxation of entry barriers and private bank penetration. To estimate Lerner indexes as a measure of bank competition, we set up a simultaneous equation model for unbalanced panel data by utilizing the stepwise maximum likelihood method. We find that concomitantly with the new bank entries a pro-competitive change in the banking industry took place.