This paper presents a simple model of state-dependent pricing that allows identification of the relative importance of the degree of price rigidity that is inherent to the price setting mechanism (intrinsic) and that which is due to the price's driving variables (extrinsic). Using two data sets...

This paper presents a simple model of state-dependent pricing that allows identifying the relative importance of both nominal and real factors in price rigidity. Using two rich datasets consisting of a large fraction of the price quotes used to compute the Belgian and French Consumer Price...

This paper presents a simple model of state-dependent pricing that allows identification of the relative importance of the degree of price rigidity that is inherent to the price setting mechanism (intrinsic) and that which is due to the price's driving variables (extrinsic). Using two data sets...

This paper presents a simple model of state-dependent pricing that allows identifying the relative importance of both nominal and real factors in price rigidity. Using two rich datasets consisting of a large fraction of the price quotes used to compute the Belgian and French Consumer Price...

This paper presents a simple model of state-dependent pricing that allows identification of the relative importance of the degree of price rigidity that is inherent to the price setting mechanism (intrinsic) and that which is due to the price’s driving variables (extrinsic). Using two data...

This paper builds on the work of Acemoglu et al. (2012) and considers a production network with unobserved common technological factor and establishes general conditions under which the network structure contributes to aggregate fluctuations. It introduces the notions of strongly and weakly...

Estimation and inference in the spatial econometrics literature are carried out assuming that the matrix of spatial or network connections has uniformly bounded absolute column sums in the number of cross-section units, n. In this paper, we consider spatial models where this restriction is...

This paper proposes a regularisation method for the estimation of large covariance matrices that uses insights from the multiple testing (MT) literature. The approach tests the statistical significance of individual pair-wise correlations and sets to zero those elements that are not...

This paper develops an individual-based stochastic network SIR model for the empirical analysis of the Covid-19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate...

This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of...