Conference Agenda

When panel data exhibit cross-sectional dependence, particular care is required, as cross-dependence may be induced by omitting relevant variables. If these variables correlate with the regressors, rendering them endogenous, sophisticated approaches such as the CCE approach or the PC estimator are recommended. These approaches may however be difficult to implement or build on strong assumptions. Therefore, if regressor endogeneity can reasonably be excluded, it is common to resort to simpler estimators in conjunction with panel-robust standard errors.
In practice, it is furthermore not uncommon for cross-dependence to be of various strength, ranging from weak (e.g. in spatial data models) to strong (when unobserved factors load onto all units).
We quantify the impact the strength of cross-dependence has on the within estimator and on the associated (panel-robust) tests when unobserved factors in errors are not confounding. Using theoretical justifications and Monte Carlo simulation we find that cross-dependence alone in the errors is not sufficient to impact the fixed-effects estimator or its standard errors. Rather, it is the interaction of error and regressor cross-dependence that causes problems such as reduced convergence rates and distorted significance tests in finite samples.

We consider a structural matrix-autregressive (SMAR) model to conduct impulse response analysis for structural shocks to matrix-valued time series. The MAR model of order $p$ offers a parsimonious and interpretable framework for these time series, thus addressing issues of high-dimensionality in corresponding vector-autoregressive (VAR) models. To interpret the dynamics, we resort to impulse response analysis as a popular tool from the SVAR context. Its conclusions rely on the valid identification of structural shocks that are mutually contemporaneously uncorrelated and interpretable. In contrast to the existing literature, the proposed SMAR model enables the identification of multiple structural shocks. To address the restrictive nature of the single-term MAR($p$) model, we discuss the extension to a multi-term SMAR($p$) model as a compromise between the single-term SMAR and the (unrestricted) SVAR model, trading off parsimony against flexibility. We discuss its identification, focusing in particular on issues that arise due to the typical Kronecker-product structure of the coefficient matrices in the MAR framework. Further, we discuss estimation and inference in the general multi-term SMAR($p$) model, including a bootstrap method to compute confidence bands for the impulse response curves. In this context, a key point concerns model misspecification and the use of MAR models to approximate more general SVAR data generating processes. Finally, we demonstrate the performance and practical use of our approach by Monte Carlo simulations and a real data application.

Vector Multiplicative Error Models (vMEMs) provide a flexible framework for modeling multivariate non-negative time series. Within this framework, each variable is expressed as the product of its conditional mean—modeled as a function of past observations—and a positive innovation with unit expectation. Consequently, the model can capture dynamic cross-dependencies and have proven useful in applications such as modeling durations, volatilities, and trading volumes. This contribution focuses on goodness-of-fit (GOF) tests for vMEMs, aiming to assess whether the model structure and the assumed innovation distribution adequately reflect the properties of the observed data. We propose a GOF test statistic and derive its asymptotic distribution under the null hypothesis. The performance of a bootstrap version of the test is illustrated through Monte Carlo simulations.