<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Wooyong Lee | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/wooyong-lee/</link><description>Wooyong Lee</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://macropaperwarehouse.com/authors/wooyong-lee/index.xml" rel="self" type="application/rss+xml"/><item><title>Identification and Estimation of Dynamic Random Coefficient Models</title><link>https://macropaperwarehouse.com/papers/identification-and-estimation-of-dynamic-random-coefficient-models/</link><guid>https://macropaperwarehouse.com/papers/identification-and-estimation-of-dynamic-random-coefficient-models/</guid><description>&lt;p&gt;This paper studies linear panel data models where regression coefficients are individual-specific (random coefficients) and regressors may be predetermined — that is, sequentially exogenous rather than strictly exogenous, as occurs when a lagged dependent variable appears on the right-hand side. The canonical example is the AR(1) model Yit = gamma_i + beta_i * Yi,t-1 + epsilon_it, where both the intercept and the autoregressive coefficient vary across individuals. The setting is short panels (small T), which rules out learning about individual-level coefficient values.&lt;/p&gt;</description></item></channel></rss>