Adaptive expectations and partial adjustment Presented by: Monika Tarsalewska Piotrek Jeżak Justyna Koper Magdalena Prędota

Adaptive expectations

Expectations Either the dependent variable or one of the independent variables is based on expectations. Expectations about economic events are usually formed by aggregating new information and past experience. Thus, we might write the expectation of a future value of variable x, formed this period, as Example: Forecast of prices and income enter demand equation and consumption equations.

Adaptive expectations Regression: The error of past observation: and a mechanism for the formation of the expectation:

Adaptive expectations The expectation variable can be written as Inserting equation (3) into (1) produces the geometric distributed lag model.

Adaptive expectations Koyck transformation

There is a problem of simultaneity as y t-1 is correlated in time with There is nonlinear restriction in our model which should de included in the regression Adaptive expectations

And after Koyck transformation Measurement of permanent income might be approached through the use of the adaptive expectations hypothesis, where permanent income (inc t ) alters between periods in proportion to the difference between actual income (inc t ) in a period, and permanent income in previous period. Adaptive expectations

ivreg conspr (l.conspr = l2.conspr l3.conspr l4.conspr) housedisp Instrumental variables (2SLS) regression Source | SS df MS Number of obs = F( 2, 7) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = conspr | Coef. Std. Err. t P>|t| [95% Conf. Interval] conspr | L1 | housedisp | _cons | Adaptive expectations

Partial adjustment

The partial adjustment model describes the desired/optimal level of y t which is unobservable adjustment equation looks as following where adjustment equation looks as following where denotes the fraction by which adjustment occurs Partial adjustment

If we solve the second equation for y t and insert the first expression for y*, then we obtain: This formulation offers a number of significant practical advantages. It is intrinsically linear in the parameters (unrestricted), error term nonautocorrelated therefore the parameters of this model can be estimated consistently and efficiently by ordinary least squares. Partial adjustment

Consumer is viewed as a having desired level of consumption, which is related to the current income. When current income changes, inertial factors prevent An immediate movement to the new desired level of consumption. Instead, a partial movement is made, so that:with: Partial adjustment

This leads to an estimating form: Partial adjustment

reg conspr l.conspr housedisp Source | SS df MS Number of obs = F( 2, 10) = 1.19 Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = conspr | Coef. Std. Err. t P>|t| [95% Conf. Interval] conspr | L1 | housedisp | _cons | Partial adjustment