What type of model is presented below – Model 3. What impact do d_y and d_rs have on the dependent variable d_m_p? Model 3: OLS, using observations 1980:2-1999:3 (T = 78)Dependent variable: d_m_p CoefficientStd. Errort-ratiop-value const0.01139710.001300788.7617<0.0001***d_y−0.01069110.100221−0.10670.9153 d_rs0.001514620.001106131.36930.1751 d_rl0.0001277440.001419540.09000.9285 d_p−0.3542040.093535−3.78690.0003*** Mean dependent var 0.007578 S.D. dependent var 0.004753Sum squared resid 0.001399 S.E. of regression 0.004378R-squared 0.195607 Adjusted R-squared 0.151531F(4, 73) 4.437922 P-value(F) 0.002885Log-likelihood 315.5363 Akaike criterion−621.0725Schwarz criterion−609.2890 Hannan-Quinn−616.3554rho 0.485424 Durbin-Watson 1.001216

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Description: What type of model is presented below – Model 3. What impact do d_y and d_rs have on the dependent variable d_m_p? Model 3: OLS, using observations 1980:2-1999:3 (T = 78)Dependent variable: d_m_p CoefficientStd. Errort-ratiop-value const0.01139710.

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What type of model is presented below – Model 3. What impact do d_y and d_rs have on the dependent variable d_m_p?

Model 3: OLS, using observations 1980:2-1999:3 (T = 78)

Dependent variable: d_m_p

	Coefficient	Std. Error	t-ratio	p-value
const	0.0113971	0.00130078	8.7617	<0.0001	***
d_y	−0.0106911	0.100221	−0.1067	0.9153
d_rs	0.00151462	0.00110613	1.3693	0.1751
d_rl	0.000127744	0.00141954	0.0900	0.9285
d_p	−0.354204	0.093535	−3.7869	0.0003	***

Mean dependent var	0.007578	S.D. dependent var	0.004753
Sum squared resid	0.001399	S.E. of regression	0.004378
R-squared	0.195607	Adjusted R-squared	0.151531
F(4, 73)	4.437922	P-value(F)	0.002885
Log-likelihood	315.5363	Akaike criterion	−621.0725
Schwarz criterion	−609.2890	Hannan-Quinn	−616.3554
rho	0.485424	Durbin-Watson	1.001216

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