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# Dynamic Models.

## Presentation on theme: "Dynamic Models."— Presentation transcript:

Dynamic Models

Introduction Assess the partial adjustment model as an example of a dynamic model Examine the ARDL model as a means of testing the partial adjustment model Discuss the Sharpe-Lintner model Derive the Error Correction Model from the ARDL model.

Partial Adjustment Model
This type of dynamic model is part of the Koyck distribution class of models. It is used in models where adjustment does not occur immediately, but takes a number of time periods to fully adjust. We can apply a specific restriction to a general ARDL model to determine if partial adjustment is taking place

Partial Adjustment Model
This model has as its dependent variable a desired value or planned value. This desired value is then determined by the usual explanatory variables:

Partial Adjustment Mechanism
The partial adjustment mechanism takes the following form, where the change in y is equal to the difference between the desired value of y and its actual value in the previous time period:

Partial Adjustment Mechanism
If λ is 0, it means there is no adjustment from one time period to the next. If λ is 1, it means there is immediate adjustment from one time period to the next. To produce an estimating equation, we need to substitute in this adjustment mechanism, to have an expression in terms of y as the dependent variable rather than y*.

Estimating Equation The estimating equation, where y is the dependent variable, following the process of substitution is:

ARDL Model The estimating equation is the same as the conventional ARDL model, including a specific restriction. The restriction is that the coefficient on the x(t-1) variable is equal to 0. Where the ARDL model is:

Partial Adjustment Model
In addition the estimated coefficients on the other variables can be used to produce estimates of the coefficients in the original model with the ‘desired’ variable. Based on the previous ARDL model:

Partial Adjustment Model
An example of the partial adjustment model is Lintner’s Dividend-Adjustment Model. The model suggests that dividends are related to company profits, but when profits rise, dividends do not rise in the same proportion immediately. Lintner suggests that firms have a long-run desired/ target pay-out ratio between dividends and profits, in which the dividend payout relative to profits is a desired level.

Lintner Model Lintner then estimated the following model for the US:

Lintner Model The coefficient on D(t-1) is equal to (1-λ)=0.70.
This means that the speed of adjustment λ=(1-0.70)=0.30. This suggests adjustment is relatively slow. The coefficient on π is equal to (β λ), so β =0.15/0.30= This gives a value for the payout ratio of 0.5.

Error Correction Models
The error correction model is a short-run dynamic model, consisting of differenced variables, except the error correction term. The error correction term reflects the difference between the dependent and explanatory variable, lagged one time period. This model can incorporate a number of lags on both the dependent and explanatory variables.

Error Correction Models
As with the partial adjustment model, the error correction model (ECM) can be interpreted as a general ARDL model, in which a specific restriction is applied. The model can be derived from the basic ARDL model:

Error Correction Model
To turn the ARDL model into the ECM involves the following: - Subtract y(t-1) from both sides of the ARDL equation. - Add β(2)x(t-1) and subtract the same amount from the right hand side of equation. - Collect terms.

Error Correction Model
The previous rearrangements produces the following model:

Error Correction Model
Finally to produce an ECM, the following restriction needs to be applied to the lagged level variables:

Error Correction Term The ECM is usually written in the following form, where the parameter τ is the error correction term coefficient.

Long-Run In the long-run, we assume the Δy and the Δx variables (all variables in logs) grow at a constant rate of g. This produces the following expression:

Long-run If we assume the relationship between x and y is of the form: y*=k.x*, taking logs gives: log(y*)=log(k)+log(x*). To find k we need to antilog the previous expression:

ECM The following estimates were produced for an ECM using 60 observations (All variables in logs), where the long-run growth rate of Δx and Δy is 0.02:

ECM This produces the following long-run relationship between x and y, which takes the form y = k. x :

ECM The ECM is used to model the short-run in many situations and is closely associated with cointegration (we will cover this later). The model usually also includes lagged variables in addition to the error correction term.

Conclusion The Partial Adjustment model is a version of the Koyck distribution It provides a theoretical reason for the inclusion of lags in a model. The Error Correction Model (ECM) can be derived from an ARDL model. The estimates from an ECM can also be used to determine the long-run relationship between variables.

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