Joint tests of non-nested models and general error specifications

BEBR

FACULTY WORKING
PAPER NO. 89-1616

Joint Tests of Non-Nested Models
and General Error Specifications

The
JAN 2 5 1990

lilinols
of Uriana-Cha>r.paign

Anil K. Bern
Michael McAleer
M. Has hem Pesamn

College of Commerce and Business Administration
Bureau of Economic and Business Research
University of Illinois Urbana-Champaign

BEBR

FACULTY WORKING PAPER NO. 89-1616

College of Commerce and Business Administration

University of Illinois at Urb ana -Champaign

November 1989

Joint Tests of Non-Nested Models
and General Error Specifications

Anil K. Bera
Department of Economics
University of Illinois

Michael McAleer

Department of Statistics

Australian National University

and

Institute of Social and Economic Research

Osaka University

M. Hashem Pesaran

Trinity College

Cambridge

and

Department of Economics

University of California, Los Angeles

The authors wish to thank Les Godfrey and Yuk Tse for helpful discussions, and
Essie Maasoumi for valuable suggestions and comments. The first author is
grateful for research support from the Research Board and the Bureau of
Economic and Business Research of the University of Illinois, and the second
author acknowledges the financial support of the Australian Research Grants
Scheme and the Australian Research Council.

ABSTRACT
This paper is concerned with joint tests of non-nested models and
simultaneous departures from homoskedasticity, serial independence and
normality of the disturbance terms. Locally equivalent alternative models are
used to construct joint tests since they provide a convenient way to incorporate
more than one type of departure from the classical conditions. The joint tests
represent a simple asymptotic solution to the "pre-testing" problem in the
context of non-nested linear regression models.

Key Words and Phrases: locally equivalent alternative models; non-normal
errors; non-spherical errors; pre-testing problem.

Digitized by the Internet Archive

in 2011 with funding from

University of Illinois Urbana-Champaign

http://www.archive.org/details/jointtestsofnonn1616bera

1. Introduction
In recent years a substantial literature has been developed for testing non-
nested regression models. While the available procedures are now frequently
used for both testing and modelling purposes, in many cases it would seem that
the non-nested models are presumed to have disturbances satisfying the
classical conditions of serial independence (I), homoskedasticity (H) and
normality (N). In practice, while departures from the classical conditions
occur quite frequently, it is not straightforward to modify the available test
procedures to incorporate all the departures, especially non-normality (N) of
the disturbances. Moreover, in the nested testing situation, most of the popular
tests are "one-directional" in that they are designed to test against only a single
alternative hypothesis, and in most cases the tests are valid only when the
other standard assumptions are satisfied. Many researchers have found that
the one-directional tests are not robust in the presence of other
misspecifi cations (see Bera and Jarque (1982) and the references cited therein).
In Section 2, the robustness of several well known tests for both nested and non-
nested hypotheses is discussed briefly. In Section 3, we develop a procedure for
testing non-nested models together with simultaneously checking the
sphericality and normality of the disturbance terms. Locally equivalent
alternative models are used to construct joint tests since they provide a
convenient method for incorporating more than one type of departure from the
classical conditions. The joint tests represent a simple asymptotic solution to
the "pre-testing" problem in the context of non-nested linear regression
models. Some concluding remarks are given in Section 4.

2. Robustness of Several Existing Tests
In testing nested hypotheses, two kinds of situations can occur, namely
undertesting and overtesting. When departures from the null hypothesis are
multi-directional and a one-directional test is used, undertesting is said to

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occur. On the other hand, in overtesting, the test statistic overstates the
alternative hypothesis. In both undertesting and overtesting, a loss of power is
to be expected. However, joint testing of several hypotheses is rarely performed
in practice, so that inferences are generally affected by undertesting. By
drawing upon some Monte Carlo results from Bera (1982) and Bera and Jarque
(1982), we highlight the effects of undertesting and the subsequent non-
robustness of a commonly used test of heteroskedasticity.

Three convenient simplifying assumptions are usually made in standard
regression analysis, namely H, I and N. In what follows, we consider the
Breusch and Pagan (1979) Lagrange multiplier (LM) test for heteroskedasticity
(H). The data are generated under different combinations of N (the t
distribution with five degrees of freedom), H (additive heteroskedasticity) and
serial dependence (I) (first-order serial correlation); for further details, see
Bera (1982) and Bera and Jarque (1982). The estimated powers of the test
statistic under different data generating processes (DGP) are given below.

Nature of Alternative Model
Correct Contaminated Misspecified

DGP HIN HIN HIN HIN HIN HIN HIN

Estimated power -726 -660 -270 -302 084 -222 128

When the DGP contains only heteroskedasticity (that is, HIN), the LM test is
asymptotically optimal against the correct alternative and power is estimated to
be -726. However, the estimated powers fall to -660 and -270 when the data are
contaminated by the t distribution (HIN) and first-order serial correlation

(HIN), respectively. The effect of I on the LM test of H is quite substantial.
When both I and N contaminate the DGP, the estimated power is only -302.
The final three cases are completely misspecified in that the LM test is seeking
to detect H when H does not exist but I, N or both I and N are present. The
interpretation of the estimated powers depends on how the tests are to be

- o -

viewed. If the test is seen exclusively as a test of H, then the estimated powers
should equal the estimated significance levels or sizes. It is clear from the
table that the estimated sizes are significantly greater than the nominal size of
the test in all three cases. However, if the test is used purely as a significance
test, the estimated powers seem to be quite low, and are much lower than those
for the case where the alternative model is contaminated. Regardless of the
interpretation, therefore, the test does not perform well. .The one-directional
test is not robust when the alternative model is contaminated and the test is not
satisfactory in the completely misspecified case, regardless of how it is
interpreted.

Turning now to the case of non-nested hypotheses, extensive Monte Carlo
experiments have been conducted by Godfrey and Pesaran (1983) and Godfrey et
al. (1988). The first of these two papers is concerned with the selection of
regressors in two non-nested linear regression models, and examines the Cox
test of Pesaran (1974), two mean- and variance-adjusted versions of the Cox
test, the J test of Davidson and MacKinnon (1981), the JA test of Fisher and
McAleer (1981), and the standard F test applied to the comprehensive model
constructed as a union of the two models. Since the tests are valid only
asymptotically when the disturbances are not normally distributed, Godfrey
and Pesaran (1983) examine the robustness of the tests to errors drawn from
the log-normal distribution and the chi-squared distribution with two degrees
of freedom. Their experiments indicate that the finite sample significance
levels are not significantly distorted and are broadly similar to those for the
case of normally distributed errors. Although estimated powers tend to be
greater when the errors are drawn from the two non-normal distributions
compared with the normal case, the relative rankings of the tests in terms of
power are not affected by the non-normality.

Various procedures are considered by Godfrey et al. (1988) for testing the
non-nested linear and logarithmic functional forms. The test procedures are

- 4 -

classified as non-nested tests, two versions of the LM test and a variable
addition test based on the more general Box-Cox transformation, and
diagnostic tests of (possible) functional form misspecification against an
unspecified alternative hypothesis. If the logarithmic model is to be taken
seriously, the dependent variable of the linear model cannot take on negative
values and the disturbances of the linear model cannot be normal. Therefore,
it is essential to examine the robustness of the tests to non-normality of the
errors, even if the primary consideration rests with testing the non-nested
functional forms. Godfrey et al. (1988) examine the finite sample significance
levels and powers of the tests when the disturbances follow the gamma (2,1)
distribution, the log-normal distribution and the t distribution with five degrees
of freedom. The two versions of the LM test based on the Box-Cox model are
found to be highly sensitive to non-normality in that the estimated significance
levels are far greater than those predicted by asymptotic theory, even when the
sample size is eighty. On the other hand, the variable addition tests in the
three categories are found to be robust to non-normality of the errors, and their
relative rankings in terms of power are not affected by departures from
normality.

3. Joint Tests

The standard situation for testing non-nested linear regression models
with normal and spherical errors is as follows. It is desired to test the null
model H0 against the non-nested alternative Hi, where the two models are

given as

2
Ho : y = X(3 + uo , uo ~ N(0,a0In)

and

2
Hi : y = Zy + ui , ux ~ N(0,a1In) ,

in which y is the n x 1 vector of observations on the dependent variable, X and Z
are n x k and n x g matrices of observations on k and g linearly independent
regressors, p and y are k x 1 and g x 1 vectors of unknown parameters, and uo
and ui are vectors of normally, independently and identically distributed
disturbances. It is also assumed that X and Z are not orthogonal, and that the
limits of n^X'X, nrlZ'Z and n^X'Z exist, with the first two positive definite and
the third non-zero. If X and Z contain stochastic rather than fixed elements,
the probability limits of the appropriate matrices must exist, and X and Z must
be distributed independently of uo and ui under Ho and Hi, respectively.

In considering the consequences of testing for certain departures from
sphericality, it will be convenient to rewrite the two models as

Ho : yt = xt'p + uot (1)

and Hi : yt = zt'y + uu , (2)

in which xt' and zt are the t'th rows of X and Z, respectively, and t = l,2,...,n.
When the assumptions regarding uot and uit are not satisfied, some of the
properties of the tests will be affected. For example, Pesaran (1974) derived a
test of non-nested linear regression models where the disturbances of each
model follow a first-order autoregressive scheme. However, Pesaran's test is
very complicated to apply in practice and a simpler procedure is given in
McAleer, Pesaran and Bera (1989). The effect of heteroskedasticity will be
similar. In particular, a straightforward application of the tests suggested
in Davidson and MacKinnon (1981) and Fisher and McAleer (1981) will not be
valid since the standard errors will not be correct. However, use of a
heteroskedasticity-consistent covariance matrix estimator will circumvent this
problem. When the errors are not normal, Pesaran's test based on the work of
Cox (1961, 1962) is still valid asymptotically, although its small sample
properties will be affected. Normality is required for the test suggested by

- 6 -

Fisher and McAleer (1981) to have the exact t-distribution under the null
hypothesis; if normality does not hold, the test will be valid only asymptotically.

In the light of the above discussion, a basic requirement for applying
standard non-nested testing procedures to achieve high power is that the
models under consideration be well-specified. This means that tests for
normality and sphericality, for example, are to be performed prior to testing the
non-nested models themselves. An important, and frequently overlooked,
aspect of testing non-nested models in this two-step procedure is the effect that
such "pre-testing" may have on the levels of significance and powers of the
non-nested tests. Therefore, it may be desirable to test the non-nested
specifications jointly with departures from the classical assumptions
regarding the disturbance terms. Such procedures will be particularly useful
when there is a possibility of a non-normal disturbance term of unknown type,
since it is frequently difficult to take account of general forms of non-normality
in a straightforward manner. Joint tests may be constructed in a
straightforward way by developing an approximate model which incorporates
the various departures from the classical conditions into the systematic part of
the model, so that the disturbances of the approximate model are normally,
independently and identically distributed. The advantage of joint tests over the
two-step testing procedure lies in the way the joint testing procedure deals with
the "pre-testing" problem, at least asymptotically.

In the context of deriving Lagrange multiplier (LM) diagnostic tests,

Godfrey and Wickens (1982) suggested a way of obtaining a local approximation

to a given model with a non-standard disturbance structure. Such

approximations are called "locally equivalent alternative" (LEA) models. As

an illustration, consider Ho in (1), where it is now assumed that the

disturbance uot is given by

2
uot = Pouot-i + eot , eot ~ NED(0,a0) , I p0 1 < 1 (3)

- 7 -

for t = 2,3,.. .,n. A LEA model to Ho may be written as

3fc ^*

HQ : yt = xt'p + pouot-i + eot , (4)

in which uot = yt -xt'P and p is the ordinary least squares estimate of p under Ho.

The models HQ in (4) and Ho in (1) and (3) are "equivalent" in the sense that:

*
(i) when po = 0, Ho and HQ are identical;

(ii) when p0 = 0, then 3it(P»^o»Po^Po = ^t^P,aO»Po^PO' wnere

*
it(P>°2>Po) and ^f (P>a2>Po) are the log-density functions for

the t'th observation under Ho and HQ, respectively.

Godfrey (1981) has shown that, in testing po = 0 for local alternatives, the

likelihood ratio test of po = 0 applied to Ho in (1) and (3) and the LM test of po = 0

*
applied to HQ in (4) have similar power. Thus, for values of po in the

*
neighbourhood of zero, Ho and HQ may be regarded as equivalent.

Now let us consider (1) and (2) allowing for the possibility that the
disturbances uit (i = 0,1) follow stationary autoregressive processes of order pj
(i = 0,1), namely AR(pj), as follows:

Pi
uit = I PijUit-j + Eit . i = 0,1
j=l

where t = p+l,p+2,...,n and p = max(po.pi). In this case, a locally equivalent
form of (1) may be written as

* Po ~

HQ:yt = xt'p+ I pojuot-j + eot ,
j=i

where uot = yt - xt P» as before.

Although several procedures are available in the literature (for a recent

review, see McAleer and Pesaran (1986)), a convenient test of the null model

against both the non-nested alternative Hi and AR(po) disturbances can be
performed by testing a = poi = P02 = ••• = Pop0 = 0 in the auxiliary linear

regression required for the J test of Davidson and MacKinnon (1981), namely

- 8 -

PO ^

yt = xt'p + I pojuot-j + ccyu + et ,
where yit is the predicted value of yt from (2), namely

•S •% Pi /N ^

yu = ztY+ I Pijuit-j .

uit = yt - ztY ana" Y is the maximum likelihood estimate of y under Hi.

An attractive feature of this approach is that other departures from the
classical conditions may be handled in an equally straightforward manner.
Consider the following general form of the distribution of the disturbance term
for Ho of (1), where uot follows an autoregressive process of order po, namely

Po
uot = X Pojuot-j + eot »

j=l

and eot is independently distributed. The density of Eot, denoted by g(eot)» is
assumed to be a member of the symmetric Pearson family of distributions.
This is not a very restrictive assumption since this family encompasses many
distributions such as the normal, Student t and F. The density of e0't is then
given by

g(eot) = exp pF(eot)l

J exp pF(eot)l deot

-oo < eot < oo

where

f 2

¥(eot) = J [ - eotAcot + cieot)] deot

When Ci = 0, g(eot) reduces to a normal density with mean zero and variance
cot- Heteroskedasticity is introduced through cot- It is assumed that

cot = h

' qo n

i=l

where the elements of the qo x 1 vector vt = (vit,V2t,.--,vqot)' are fixed and
measured around their means, h() is a twice differentiable function with h(0)

- y -

- o0, and (pi (i = l,2,...,qo) are unknown- parameters. Under these
circumstances, the disturbances for Ho in (1) are now non-normal,
heteroskedastic and serially dependent. A simple local approximation to this
complicated model may be written as

in which

** Po ^ ~ Qo

HQ :yt = xt'(3+ X pojuot-j + uot I <Pi vit + Cirt + £ot ,
j=l i=l

rt = (uQt - 3uota0 )/ (4a0 ) ,

(5)

~2

n

~2

a0 = n-i I uQt,

L— A

with Eot ~ NID(0,a0) for all t.

To verify whether model (5) is a LEA model, we first note that, when po =
(poi,p02,...,pOp0)' = 0, (p = ((pi,92,...,9q0)' = 0 and Ci = c2 = 0, H0 in (1) and HQ in
(5) are identical and eot = uot> so that condition (i) is satisfied. Godfrey and
Wickens (1982) verified condition (ii) above for the case of serial correlation and
heteroskedasticity. It is, therefore, necessary to consider only the non-normal
components. First, it can be shown that there is no contribution from the
Jacobian term. The Jacobian from cirt is asymptotically given by (see Godfrey
and Wickens (1982, p.85))

I in

t

3(u0t-o0)

1-ci

4o,

which, under local alternatives, reduces to

3C1 2 2.

--^Z(uot-G0),

4o0 t

which is 0p(l). Therefore, for the purpose of developing a joint test, we can
ignore the Jacobian term.

It can also be shown that the score with respect to Ci is the same under Ho
**
and HQ . From Bera and Jarque (1982, p.78), it follows that

3it(P.o0'0) uot

2
where ^tCP^o^) *s *he log-density function under Ho, with y =

2
(P0i,p02,-..,p0p0,9i,92,-.MCpq0,ci)'. Using the information that eot ~ NID(0,a0) for

all t, it follows from (5) that

dJtt (p,a0,0)

r = rt uot/a0 ,

dci

where Z ^ (•) is the log-density function under HQ . Since

n 9it(P,^,0) n 3it (P^o.0)

^ ic~i = ^ dci

t=l dCl t=l x

the score under the original and LEA models is the same. One component of

the LM test for normality is based on this score value (see Bera and Jarque

(1981), and Jarque and Bera (1987)).

If it were desired to test serial independence, homoskedasticity and
normality under Ho, we would test the parameter restrictions

po = 0, 9 = 0, ci = 0
in equation (5). This joint test procedure has been suggested by Bera and
Jarque (1982). However, they did not consider the possibility of the non-nested
alternative Hi together with the non-sphericality and non-normality of the
disturbance term under Ho-

If suitable predictions yit from a non-nested alternative Hi are augmented
to equation (5) to yield the auxiliary regression given by

Po «- ~ qo ~

yt = xt'p+ £ pojuot-j + uot I 9i vit + cirt + ayn + eot , (6)

j=l i=l

then the null hypothesis, namely equation (1) with uot = £ot» involves a joint test
of

H : po = 0, 9 = 0, ci = 0, a = 0. (7)

This joint hypothesis can be tested by applying the LM procedure, for example,
directly to equation (6) or by using an appropriately adjusted F test (see Godfrey
and Wickens (1982)). In computing the LM test, the most convenient form is
nR2, that is, the sample size times the (uncentred) coefficient of determination
in the auxiliary regression of a vector of ones on the following variables:

**
dJt

~~* •%>■)

ap

= xt'uot/a0

**

^2 ~~2 ~4
2~= (UOt-<*oy(2CT0>

3g0

**

= uotuot-j/a0 , j = 1,2,. ..,po

3poj

**

aif

= (u^v^t/Oo) - vit , i = l,2,...,q0

a»i "VOt

**
= (rtuot/o0) - 3(u0(.- a0)/(4a0)

and

**

"sr = yituot/°o •

The set of regressors given above can be simplified to

(xt'uot,uot- <yo»u°tuot-i,--,uotuot-p0,(uot- a0)vit,...,(uot- cJ0)vqot,
4rtuota0 - 3(uQt- a0), yituot).

As noted by Godfrey and Wickens (1982, p.86), it is not valid to apply the
standard form of the F statistic to (6) in testing H in (7). For example, Godfrey
and Wickens have shown that, for testing 9 = 0, the usual regression formula
omits the factor 2 arising from the asymptotic distribution of

n^Zuot(uotvit)- Consider now the F statistic for testing ci = 0. The asymptotic
t

variance of n-^ £ uotrt is the same as that of
t

h-»I(u0t-3u0ta0)/(4a0).

2
Under H, uot = £ot ~ NID(0,a0) for all t. Therefore, the above variance can also

be expressed as

[E(u®t) - (E(uJt))2 + 9ao(E(uQt) - E(uot)2)

- 6G20(E(u0t) - E(uJt)E(uQt))]/(16ao)

= [(105oo - 9a®) + 9ao(3ao - aj) - 6o2Q(15ol - dafyo&tf

= 21aJ/8.

However, the limiting value of the regression formula is

a0 plim n-1 £ {\iQt- Bu^^dGa^ = 30^8 ,
t

which is one-seventh of the correct asymptotic variance.
Denote the standard F statistics for testing

- 13 -

(po = 0, a = 0), 9 = 0 and ci = 0
by Fi, F2 and F3, respectively. Note that the regressors corresponding to the
above parameters are asymptotically uncorrelated with each other, and also
the regressors with coefficients 9 and ci are asymptotically orthogonal to the
regressors of the null model. Therefore, the conditions for the decomposition of
the joint test are satisfied (see Godfrey (1988, p.79)). It follows that, under Ho,

(po+DFi + K2q0F2 + V7F3 -d* X2(po+qo+2).

This test statistic will test the standard linear model (1) with normal and
spherical disturbances against a broader alternative of non-spherical and non-
normal disturbances, as well as against a non-nested alternative. Depending
on the situation, it is possible to specialize the test statistic to particular
alternatives by retaining the appropriate regressors in equation (6). For
example, to test the null model Ho against a non-nested alternative Hi and the
possible presence of heteroskedasticity, it would be necessary to test 9 = 0 and
a = 0 in the auxiliary regression given by

yt = xt'p + uot I <Pivit + otyit + eot •
i=l

This auxiliary regression equation is simply a specialization of equation (6)

with p0j = 0 (j=l,2,...,p0) and C\ = 0.

4. Conclusion
In this paper we have presented some simple joint tests of non-nested
models and general error specifications. The joint tests for non-nested
specifications and for one or more departures from the classical conditions of
serial independence, homoskedasticity and normality were developed within
the context of locally equivalent alternatives. These tests represent a simple
asymptotic solution to the "pre-testing" problem as applied to non-nested linear
regression models. If the null hypothesis is not rejected by the joint tests,

- 14 -

standard regression analysis would follow for the underlying null model.
However, if the null is rejected, it is not possible to infer whether it is rejected
because of the non-nested alternative or through departures from the classical
conditions regarding the disturbances.

References

Bera, A.K. (1982), Aspects of Econometric Modelling, Unpublished doctoral
thesis, Australian National University.

Bera, A.K. and CM. Jarque (1981), An efficient large sample test for

normality of observations and regression residuals, Working Paper in
Economics and Econometrics No. 040, Australian National University.

Bera, A.K. and CM. Jarque (1982); Model specification tests : A simultaneous
approach, Journal of Econometrics, 20, 59-82.

Breusch, T.S. and A.R. Pagan (1979), A simple test for heteroscedasticity and
random coefficient variation, Econometrica, 47, 1287-1294.

Cox, D.R. (1961), Tests of separate families of hypotheses, Proceedings of the
Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1
(Berkeley, University of California Press), 105-123.

Cox, D.R. (1962), Further results on tests of separate families of hypotheses,
Journal of the Royal Statistical Society B, 24, 406-424.

Davidson, R. and J.G. MacKinnon (1981), Several tests for model specification
in the presence of alternative hypotheses, Econometrica, 49, 781-793.

Fisher, G.R. and M. McAleer (1981), Alternative procedures and associated
tests of significance for non-nested hypotheses, Journal of Econometrics,
16, 103-119.

Godfrey, L.G. (1981), On the invariance of the Lagrange multiplier test with
respect to certain changes in the alternative hypothesis, Econometrica, 49,
1443-1455.

Godfrey, L.G. (1988), Misspecification Tests in Econometrics: The Lagrange
Multiplier Principle and Other Approaches (Cambridge University Press,
New York).

Godfrey, L.G., M. McAleer and CR. McKenzie (1988), Variable addition and
Lagrange multiplier tests for linear and logarithmic regression models,
Review of Economics and Statistics, 70, 492-503.

- 15 -

Godfrey, L.G. and M.H. Pesaran (1983), Tests of non-nested regression

models : Small sample adjustments and Monte Carlo evidence, Journal
of Econometrics, 21, 133-154.

Godfrey, L.G. and M.R. Wickens (1982), Tests of misspecification using locally
equivalent alternative models, in G.C. Chow and P. Corsi (eds), Evaluating
the Reliability of Macroeconomic Models (Wiley, New York), 71-99.

Jarque, CM. and A.K. Bera (1987), An efficient large-sample test for normality
of observations and regression residuals, International Statistical Review,
55, 163-172.

McAleer, M. and M.H. Pesaran (1986), Statistical inference in non-nested

econometric models, Applied Mathematics and Computation, 20, 271-311.

McAleer, M., M.H. Pesaran and A.K. Bera (1989), Alternative approaches to
testing non-nested models with autocorrelated disturbances : An
application to models of U.S. unemployment, Paper presented at the
Time Series Conference, Osaka, Japan, June 1989.

Pesaran, M.H. (1974), On the general problem of model selection, Review of
Economic Studies, 41, 153-171.