Estimation of Dynamic Causal Effects

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Presentation transcript:

Estimation of Dynamic Causal Effects Chapter 15 Estimation of Dynamic Causal Effects

Estimation of Dynamic Causal Effects (SW Chapter 15)

The Orange Juice Data (SW Section 15.1)

Initial OJ regression

Dynamic Causal Effects (SW Section 15.2)

Dynamic causal effects, ctd.

Dynamic causal effects, ctd.

Dynamic causal effects and the distributed lag model

Exogeneity in time series regression

Estimation of Dynamic Causal Effects with Exogenous Regressors (SW Section 15.3)

The distributed lag model, ctd.

The distributed lag model, ctd.

Under the Distributed Lag Model Assumptions:

Heteroskedasticity and Autocorrelation-Consistent (HAC) Standard Errors (SW Section 15.4)

HAC standard errors, ctd.

HAC standard errors, ctd.

HAC standard errors, ctd.

Expression for var(), general T

HAC Standard Errors

HAC SEs, ctd.

Example: OJ and HAC estimators in STATA

Example: OJ and HAC estimators in STATA, ctd

Example: OJ and HAC estimators in STATA, ctd.

FAQ: Do I need to use HAC SEs when I estimate an AR or an ADL model?

Estimation of Dynamic Causal Effects with Strictly Exogenous Regressors (SW Section 15.5)

Analysis of the OJ Price Data (SW Section 15.6)

Digression: Computation of cumulative multipliers and their standard errors

Computing cumulative multipliers, ctd.

Computing cumulative multipliers, ctd.

Computing cumulative multipliers, ctd.

Are the OJ dynamic effects stable?

OJ: Do the breaks matter substantively?

When Can You Estimate Dynamic Causal Effects When Can You Estimate Dynamic Causal Effects? That is, When is Exogeneity Plausible? (SW Section 15.7)

Is exogeneity (or strict exogeneity) plausible? Examples:

Exogeneity, ctd.

Estimation of Dynamic Causal Effects: Summary (SW Section 15.8)