arXiv subjects

Maurizio Mazzocco

Publications and source records attributed to Maurizio Mazzocco.

3 recordsLinked to original sources

arXiv2022

Central Limit Theory for Combined Cross-Section and Time Series with an Application to Aggregate Productivity Shocks

Combining cross-section and time series data is a long and well established practice in empirical economics. We develop a central limit theory that explicitly accounts for possible dependence between the two data sets. We focus on common factors as the mechanism behind this dependence. Using our central limit theorem (CLT) we establish the asymptotic properties of parameter estimates of a general class of models based on a combination of cross-sectional and time series data, recognizing the interdependence between the two data sources in the presence of aggregate shocks. Despite the complicated nature of the analysis required to formulate the joint CLT, it is straightforward to implement the resulting parameter limiting distributions due to a formal similarity of our approximations with the standard Murphy and Topel's (1985) formula.

stat.ME↗

arXiv2020

Joint Time Series and Cross-Section Limit Theory under Mixingale Assumptions

In this paper we complement joint time series and cross-section convergence results of Hahn, Kuersteiner and Mazzocco (2016) by allowing for serial correlation in the time series sample. The implications of our analysis are limiting distributions that have a well known form of long run variances for the time series limit. We obtain these results at the cost of imposing strict stationarity for the time series model and conditional independence between the time series and cross-section samples. Our results can be applied to estimators that combine time series and cross-section data in the presence of aggregate uncertainty in models with rationally forward looking agents.

math.PR↗

arXiv2017

Estimation with Aggregate Shocks

Aggregate shocks affect most households' and firms' decisions. Using three stylized models we show that inference based on cross-sectional data alone generally fails to correctly account for decision making of rational agents facing aggregate uncertainty. We propose an econometric framework that overcomes these problems by explicitly parameterizing the agents' inference problem relative to aggregate shocks. Our framework and examples illustrate that the cross-sectional and time-series aspects of the model are often interdependent. Therefore, estimation of model parameters in the presence of aggregate shocks requires the combined use of cross-sectional and time series data. We provide easy-to-use formulas for test statistics and confidence intervals that account for the interaction between the cross-sectional and time-series variation. Lastly, we perform Monte Carlo simulations that highlight the properties of the proposed method and the risks of not properly accounting for the presence of aggregate shocks.

stat.ME↗