Matthew Harding is an economist who conducts research on theoretical and applied econometrics. He is an Assistant Professor in the Department of Economics at Stanford University and a SIEPR Faculty Fellow.

His work is concerned with the estimation of econometric models with latent variables and unobserved heterogeneity. He uses Random Matrix Theory to uncover meaningful economic structures from large panel data. This work focuses on the statistical properties of the spectral decompositions of generalized dependency matrices such as delay correlations, commutators and adjacency matrices in both linear and nonlinear factor models. His research also explores the use of nonparametric Bayesian methods and quantile regression methods in the estimation of nonlinear econometric models with unobserved heterogeneity such as random coefficients models, panel probit and heterogeneous treatment models. This research has important implications for the estimation of financial risk, estimation of social networks, the measurement of consumer preferences from scanner data, predicting the effect of economic news on trading activity, modeling unemployment durations and evaluating the determinants of R&D activity.

Professor Harding believes that solving the current energy and environmental crisis requires an in-depth understanding of both consumer preferences and polical economy. His research on energy is primarily concerned with (a) the political economy of the Middle East and its complex connections to world economic factors (b) understanding the role of consumer expectations and macroeconomic factors as drivers of the oil market (c) the measurement of preferences for energy efficiency and the impact of economic policy on consumer choice. His research on energy economics is financed by two major grants from the Presidential Fund for  Innovation in International Studies and the Precourt Institute for Energy Efficiency.

Matthew Harding is also a founding member of the Carbon Action Partnership, an academic interdisciplinary group focused on the implementation of large scale field experiments designed to measure and evaluate the success of behavioral and technological innovations in the energy sector resulting from implementing the Smart Grid.