We use the Italian Survey of Household Income and Wealth, a rather unique dataset with a long time dimension of panel information on consumption, income and wealth, to structurally estimate a buffer-stock saving model. We exploit the information contained in the joint dynamics of income,...

We use the Italian Survey of Household Income and Wealth, a rather unique dataset with a long time dimension of panel information on consumption, income and wealth, to structurally estimate a buffer-stock saving model. We exploit the information contained in the joint dynamics of income,...

Durables like cars or houses are a substantial component in the balance sheets of households. These durables are exposed to risk and can be insured in the market. We build a dynamic model in which agents have three possibilities to cope with the risk exposure of the durable stock: (i) purchase...

We characterize how public insurance schemes are constrained by hidden financial transactions. When non-exclusive private insurance entails increasing unit transaction costs, public transfers are only partly offset by hidden private transactions, and can influence consumption allocation. We show...

We use the Italian Survey of Household Income and Wealth, a rather unique dataset with a long time dimension of panel information on consumption, income and wealth, to structurally estimate a buffer-stock saving model. We exploit the information contained in the joint dynamics of income,...

We consider an economy where individuals privately choose effort and trade competitively priced securities that pay off with effort-determined probability. We show that if insurance against a negative shock is sufficiently incomplete, then standard functional form restrictions ensure that...

We estimate the transmission of the pandemic shock in 2020 to prices in the residential and commercial real estate market by causal machine learning, using new granular data at the municipal level for Germany. We exploit differences in the incidence of Covid infections or short-time work at the...

We estimate the transmission of the pandemic shock in 2020 to prices in the residential and commercial real estate market by causal machine learning, using new granular data at the municipal level for Germany. We exploit differences in the incidence of Covid infections or short-time work at the...

We estimate the transmission of the pandemic shock in 2020 to prices in the residential and commercial real estate market by causal machine learning, using new granular data at the municipal level for Germany. We exploit differences in the incidence of Covid infections or short-time work at the...