Identification and Estimation of Categorical Random Coefficient Models

Estimation in nonstationary random coefficient autoregressive models

We investigate the estimation of parameters in the random coefficient autoregressive model Xk = (φ+ bk)Xk−1 + ek, where (φ,ω 2, σ2) is the parameter of the process, Eb0 = ω2, Ee0 = σ 2. We consider a nonstationary RCA process satisfying E log |φ + b0| ≥ 0 and show that σ2 cannot be estimated by the quasi-maximum likelihood method. The asymptotic normality of the quasi-maximum likelihood estimat...

Estimation in Random Coefficient Autoregressive Models

We propose the quasi-maximum likelihood method to estimate the parameters of an RCA(1) process, i.e. a random coefficient autoregressive time series of order 1. The strong consistency and the asymptotic normality of the estimators are derived under optimal conditions.

Identification and estimation of ‘irregular’ correlated random coefficient models1

In this paper we study identification and estimation of the causal effect of a small change in an endogenous regressor on a continuously-valued outcome of interest using panel data. We focus on the average partial effect (APE) over the full population distribution of unobserved heterogeneity (e.g., Chamberlain, 1984; Blundell and Powell, 2003; Wooldridge, 2005a). In our basic model the outcome ...

Random Coefficient Models ∗ Nicholas

Minimum distance estimation for random coefficient autoregressive models

In this paper, we extend the minimum distance method of Beran (1993) to random coefficient autoregressive (RCA) models. After stating the necessary assumptions the asymptotic properties of the minimum distance estimator are derived. A M S classification: 62M05