We want to capture underlying causal mechanisms which relate variables in our model. This is

why regression of C on Y is correct, but Y on C is not,because causality runs from income to

consumption and not the other way around. [2] Misspecification of causal relationships is VERY

common (like running Y on C) but there is no easy way to detect and fix this problem -- all of the

complex and confusing discussion about exogeneity, endogeneity, is an attempt to tackle this

problem -- but all of these attempts are failures, because they lack understanding of the root of

the problem. This is why best definitions of exogeneity in textbooks and econometric articles are

just -- plain and simple -- WRONG. [3] This problem is too complex to handle here, so let us

assume it away. ASSUME you have managed to get the right dependent variable Y on the LHS

and all variables on the RHS are exogenous. Now the main issue is: IS your model correctly

specified? Have you included all relevant variables? If you miss an important variable, then all

your results will be wrong. For example, if you run Pakistani Consumption on Guatemala GNP

you will get a very good regression with high R-squared significant t statistics, right signs and

everything. The Guatemala GNP will be significant because you have omitted an important

variable, Pakistan GNP from the equation. [4] If you have a static model, there is a chance of

dynamic misspecification -- that is, maybe the past is relevant, but you dont know about this,

because you have not included any lagged variables. If you are omitting an important lagged

variable X(t-1) or Y(t-1) from your regression than your equation is misspecified, and the results

cannot be trusted -- like Guatemala GNP, irrelevant variables may appear to be important. [5]

The problem to test for is DYNAMIC MIS-SPECIFICATION: Is any lagged regressor significant?

The way to do this is to put all lagged regressors into the model, and do a joint F test for

significance of all of them. If this F test fails to reject the null hypothesis that all of the

coefficients are jointly zero, this means that there is no strong evidence for dynamic

misspecfication -- your model has NOT omitted a significant lagged effect. [6] Serial correlation

is just ONE special type of dynamic misspecification which is included as a VERY special case

of general dynamic misspecification which we tested by F test. If model is NOT dynamically

misspecified, than there can be no serial correlation. No need to separately test for serial

correlation [7] If F test in (5) rejects null, model IS dynamically misspecified and one needs

lagged regressors in the model -- there are SEVERAL different possible patterns which could

occur in the lagged variables -- several SPECIAL types of dynamic misspecification. Koyck lags

is one of them. Serial correlation is another one: This one says the lagged regressors affect

current period ONLY in one way, through the error which occurred in the last period. This is

one possible case -- there is no reason to make this special case the ONLY type of dynamic

misspecification that you should consider, test for, and correct.

Hossain Academy Note

MODEL FORMULATION

Ade Kutu

Afolabi Luqman

Abdullah Sonnet

Asad Zaman

Atiq Rehman

Burcu Özcan

Ghumro Niaz Hussain

Muhammad Anees

Mohammad Zhafran

Muzammil Bhatti

Monis Syed

Mine PD

Moulana N. Cholovik

Muili Adebayo Hamid

Nicat Gasim

Najid Iqbal

Nasiru Inuwa

Noman Arshed

Rapelanoro Nady

Seye Olasehinde-Williams

Suborno Aditya

Sayed Hossain

Shishir Sakya

Sheikh Muzammil Naseer

Tella Oluwatoba Ibrahim

Younes Azzouz

Univariate Models |

Multivariate Models |