A dynamic modelling for heavy-tailed cylindrical time series is developed by combining score-driven models with a generalised Pareto-type cylindrical distribution. The proposed specification extends existing cylindrical models by allowing location, scale, concentration, and crucially, the tail...

The yield curve is widely regarded as a powerful descriptor of the economy and market expectations. A common approach to its statistical representation relies on a small number of factors summarizing the curve, which can then be used to forecast real economic activity. We argue that optimal...

We develop a new method for deriving high-frequency synthetic distributions of consumption, income, and wealth. Modern theories of macroeconomic dynamics identify the joint distribution of consumption, income, and wealth as a key determinant of aggregate dynamics. Our novel method allows us to...

This paper studies a dynamic ordered logit model for panel data with fixed effects. The main contribution of the paper is to construct a set of valid moment conditions that are free of the fixed effects. The moment functions can be computed using four or more periods of data, and the paper...

We study a discrete-time optimal growth model with endogenous discounting. The discount factor may depend on both consumption and the capital stock, and intertemporal utility is modeled as a discounted sum of instantaneous utilities, with the sum of discount factors equal to one. We show that...

In this paper, we propose a component-based dynamic factor model for nowcasting GDP growth. We combine ideas from "bottom-up" approaches, which utilize the national income accounting identity through modelling and predicting sub-components of GDP, with a dynamic factor (DF) model, which is...

We study a dynamic principal-agent setting in which both sides learn about the importance of effort. The quality of the agent's output is not observed directly. Instead, the principal jointly designs an evaluation technology and a wage schedule. More precise performance evaluation reduces...

In the past two decades, the design and implementation of living donor kidney exchange clearinghouses have been a major success story in market design. Instead of batching and optimizing exchanges over a fixed pool of incompatible patient-donor pairs, the busiest programs now operate...

In this study, a nonlinear dynamical system is developed to analyze the dynamics of Indian exports to USA, the US tariff rate on Indian goods, and the competitiveness of India's export sector. The model is formulated as a three-dimensional system of differential equations that incorporates...

This paper addresses a significant gap in the existing literature on financial inclusion - namely, the dynamic instability of the impacts generated by its determinants in four major emerging market economies: Brazil, Russia, India, and China. A time-varying coefficients framework is applied to...

The literature on dynamic discrete games often assumes that the conditional choice probabilities and the state transition probabilities are homogeneous across markets and over time. We refer to this as the "homogeneity assumption" in dynamic discrete games. This assumption enables empirical...

Until recently literature on dynamic factor models invariably assumed that the cross sectional error covariance matrix is diagonal. This is due to the many parameters which need to be estimated for large data sets, which is known as the curse of dimensionality. Spencer (2024) introduced a novel...

Besley and Persson (2023) pioneer a political economy model of a green transition with changing preferences. Here we solve for the optimal policy intervention and find that the optimal tax on the polluting good starts high and is subsequently declining, to support the transition in preferences....

It is common to transform data to stationarity, such as by differencing and demeaning, before estimating factor models in macroeconomics. Imposing these transformations, however, limit opportunities to learn about trending behaviour. Trends and deterministic processes can play a central role in...

We study the interaction between an agent of uncertain type, whose project gives rise to both good and bad news, and an evaluator who must decide if and when to fire the agent. The agent can hide bad news from the evaluator at some cost, and will do so if this secures her a significant increase...

This paper introduces a novel dynamic factor model designed to capture common locally explosive episodes, also known as common bubbles, within large-dimensional, potentially non-stationary time series. The model leverages a lower-dimensional set of factors exhibiting locally explosive behavior...