We present a new class of methods for identification and inference in dynamic models with serially correlated unobservables, which typically imply that state variables are econometrically endogenous. In the context of Industrial Organization, these state variables often reflect econometrically...

We present a new class of methods for identification and inference in dynamic models with serially correlated unobservables, which typically imply that state variables are econometrically endogenous. In the context of Industrial Organization, these state variables often reflect econometrically...

We present a new class of methods for identification and inference in dynamic models with serially correlated unobservables, which typically imply that state variables are econometrically endogenous. In the context of Industrial Organization, these state variables often reflect econometrically...

Non-equilibrium dynamics in the evolution of industries -- Models of industry dynamics -- A dynamic model of Schumpeterian competition -- Growing an industry in silico -- Shakeouts : limited foresight, technological shocks, and transient industry dynamics -- Industry dynamics in the steady state...

1. Non-equilibrium dynamics in the evolution of industries -- 2. Models of industry dynamics -- 3. A dynamic model of Schumpeterian competition -- 4. Growing an industry in silico -- 5. Shakeouts : limited foresight, technological shocks, and transient industry dynamics -- 6. Industry dynamics...

We present a new class of methods for identification and inference in dynamic models with serially correlated unobservables, which typically imply that state variables are econometrically endogenous. In the context of Industrial Organization, these state variables often reflect econometrically...

1. General Introduction -- 2. Dynamics in Econometrics -- 3. Estimating the Model -- 4. Testing the Model -- 5. Non-Stationarity and Cointegration -- 6. Specifying the ARDL Model -- 7. Vector Autoregressions -- 8. Panel Data Models -- 9. Non-Stationary Panels -- 10. The Binary Qualitative Model.