This paper investigates the relationship between heterogeneity in sectoral price stickiness and the response of the economy to aggregate real shocks. We show that sectoral heterogeneity reduces inflation persistence for a constant average duration of price spells, and that inflation persistence can fall despite duration increases associated with increases in heterogeneity. We also find that sectoral heterogeneity reduces the persistence and volatility of interest rate and output gap for a constant price spells duration, while the qualitative impact on inflation volatility tends to be positive. A relevant policy implication is that neglecting price stickiness heterogeneity can impair the economic dynamics assessment.

In turbulenten Zeiten, die von anhaltenden geopolitischen Krisen, dem holprigen Regierungswechsel in Deutschland und volatilen Aktienmärkten geprägt sind, mögen die eine oder der andere Investor auf Betongold setzen. Ob dies eine gute Idee ist, zeigt ein Blick auf die Dynamik im europäischen Häusermarkt.

Research using historical data from Northern Ireland indicates that house prices are higher in more diverse neighbourhoods. Housing policy should focus on regenerating neighbourhoods, improving public services and fostering social integration.

Is there a multicultural neighborhood price premium? We exploit plausibly exogenous variation in British colonization patterns in Northern Ireland during the early 1600s which created neighborhoods of varying religious composition that persists until today. These religious groups are culturally distinct, but are observationally equivalent ethnically and socioeconomically. A standard deviation increase neighborhood-level multiculturalism raises house prices by 9.6%. Multiculturalism raises property prices by increasing asset liquidity and housing demand as a wider spectrum of society demand houses in these areas. The findings and mechanism contrast sharply with prior evidence showing negative relationships due to homophily, social networks, and identification challenges.

Accurate macroeconomic forecasts are essential for effective policy decisions, yet their precision depends on the accuracy of the underlying assumptions. This paper examines the extent to which assumption errors affect forecast accuracy, introducing the average squared assumption error (ASAE) as a valid instrument to address endogeneity. Using double/debiased machine learning (DML) techniques and partial linear instrumental variable (PLIV) models, we analyze GDP growth forecasts for Germany, conditioning on key exogenous variables such as oil price, exchange rate, and world trade. We find that traditional ordinary least squares (OLS) techniques systematically underestimate the influence of assumption errors, particularly with respect to world trade, while DML effectively mitigates endogeneity, reduces multicollinearity, and captures nonlinearities in the data. However, the effect of oil price assumption errors on GDP forecast errors remains ambiguous. These results underscore the importance of advanced econometric tools to improve the evaluation of macroeconomic forecasts.

Does information leakage in a target's social networks increase its stock price prior to a merger announcement? Evidence reveals that a target with more social connections indeed experiences a higher pre-announcement price run-up. This effect does not exist during or after the merger announcement, or in windows ending two months before the announcement. It is more pronounced among targets with severe asymmetric information, and weaker when the information about the upcoming merger is publicly available prior to the announcement. It is also weaker in expedited deals such as tender offers.