Nonlinear Vector Autoregressive Models

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This paper studies a special class of vector smooth-transition autoregressive (VS-TAR) models that contains common nonlinear features (CNFs), for which we proposed atriangular representation and developed a procedure of testing CNFs in a VSTAR model.

Transcript of Nonlinear Vector Autoregressive Models

Working papers in transport, tourism, information technology and microdata analysis

Testing Common Nonlinear Features in Nonlinear Vector Autoregressive Models

Dao Li Changli He Editor: Hasan Fleyeh

Nr: 2012:04

Working papers in transport, tourism, information technology and microdata analysis

ISSN: 1650-5581 Authors

TESTING COMMON NONLINEAR FEATURES IN NONLINEAR VECTOR AUTOREGRESSIVE MODELSDao Li , Changli He

First version: September 2010, Second version: September 2011, October 24, 2012Abstract

This paper studies a special class of vector smooth-transition autoregressive (VSTAR) models that contains common nonlinear features (CNFs), for which we proposed a triangular representation and developed a procedure of testing CNFs in a VSTAR model. We rst test a unit root against a stable STAR process for each individual time series and then examine whether CNFs exist in the system by Lagrange Multiplier (LM ) test if unit root is rejected in the rst step. The LM test has standard Chi-squared asymptotic distribution. The critical values of our unit root tests and small-sample properties of the F form of our LM test are studied by Monte Carlo simulations. We illustrate how to test and model CNFs using the monthly growth of consumption and income data of United States (1985:1 to 2011:11).

Keywords: Common features, Lagrange Multiplier test, Vector STAR models.1 . Introduction A family of STAR models has been comprehensively studied for modelling nonlinear structures such as data break and regime switching in time series data since Tersvirta (1994) introduced many theoretical issues and applications of the STAR models. The extensions of the univariate STAR models have been developed for exploring nonlinearity of time series data. Escribano (1987) and Granger and Swanson (1996) introduced smooth-transition error-correction model for nonlinear adjustment. Johansen (1995) incorporated the smooth-transition mechanismCorrespondence to: Dao Li, Statistics, School of Technology and Business Studies, Dalarna University, 78170 Sweden, E-mail: [email protected], Tel: +46-(0)23-778509; rebro University School of Business, Sweden. The material from this paper has been presented at GRAPES workshop, rebro, August 2010, Econometric Society Australasian Meeting, Melbourne, July 2012 and the 66th European Meeting of the Econometric Society, Mlaga, August 2012.

CNFs in VSTAR Models

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in an error-correction model. Moreover, van Dijk and Franses (1999) contributed that multiple regime STAR models could accommodate more than two regimes in which it cannot be done by the STAR models in Tersvirta (1994). Lin and Tersvirta (1994) and He and Sandberg (2006) studied time-varying STAR models which are characteristic for the time series of structural instability in time spans. However, there has not been much research work undertaken for a multivariate nonlinear system and modelling vector nonlinear time series is little considered in the literature. There is a discussion about parameters constancy in a VSTAR model in He et al. (2009). This paper studies a special class of VSTAR models that contains CNFs. A group of time series is said to have CNFs if each of the time series is nonlinear but that their combination does not contain nonlinearity. Modelling vector nonlinear time series can be moderately improved by imposing CNFs. Previous studies of common features are mostly related to the generalized method of moments estimator and canonical correlation, for example, Engle and Kozicki (1993) and Anderson and Vahid (1998). However, we propose an LM test based on a triangular representation of the system that contains CNFs to investigate CNFs in a VSTAR model. In the triangular representation, testing CNFs in a VSTAR model is to test whether the population residual in the regression of CNF relations is linear. The proposed method is convenient to perform. The empirical application in this paper concerns the monthly growth of consumption expenditures and disposable income of United States, dating from 1985:1 until 2011:11. The permanent-income hypothesis and the study in Campbell and Mankiw (1990) imply that consumption and income are linearly cointegrated in the levels (see Engle and Granger; 1987, for more discussions). But are there any other common features in those two dynamic time series? Removing the possibility of cointegration by looking at the growth, we investigate whether there are CNFs between consumption and income when each of them presents nonlinearity. The remaining paper is outlined as follows. Section 2 studies VSTAR models; Section 3 discusses CNFs in a VSTAR model; The procedure of testing CNFs

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CNFs in VSTAR Models

is provided in Section 4; The asymptotic distribution of our LM test is derived in Section 5; Simulation studies are in Section 6; An empirical application is illustrated in Section 7; and conclusions can be found in Section 8. 2 . Vector Smooth-transition Autoregressive Models Consider a vector smooth-transition autoregressive model of order p (VSTAR(p)),p k=1

( k ytk + +

yt = +

p k=1

) k ytk G (st ; , c) + vt(2.1)

in which yt = (y1t , y2t , ..., ynt ) = (y1t , y2t ) denotes an (n 1) vector time series,

and are two (n 1) constant vectors, k and k are (n n) coecient matrices, G (st ; , c) is a smooth-transition (ST) function, and the (n 1) vector vt is theerror term. When n = 1, (2.1) is a simple univariate STAR(p) model, such thatp k=1

( k ytk + +

yt = +

p k=1

) k ytk G (st ; , c) + vt(2.2)

in which , , k and k are scaler parameters. In this paper, we only consider logistic VSTAR models with the ST function

G (st ; , c) dened as follows, ( G (st ; , c) = {k j=1

})1 (sjt cj ) , > 0,(2.3)

1 + exp

in which is a slope parameter, cj are location parameters and sjt are transition variables. We assume sjt = yitd (d p) with d that is called the delay parameter. The ST function G (st ; , c) is a bounded function on [0, 1] and at least fourthorder dierentiable with respect to in a neighborhood of = 0. Given any value of G (st ; , c) between zero and one, yt in (2.1) is a stable process if all roots of In p (k + k G)z k = 0 lie outside the unit circle (G is given). k=1 Due to the statistical properties of the ST function (2.3), (2.1) not only captures data break, dynamic structure or nonlinear shift in a vector of time series

CNFs in VSTAR Models

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1.0

G =50 =2 =1

1.0

G =50

0.8

0.8

=0.1 0.6 0.6

=0.01 0.4 0.4 =0.01 st=1 0.2 0.2 =0.1 =1 =2 0.0 4 2 0 (a) 2 4 st 0.0 4 2 0 (b) 2 4 st st=1

Figure 1: The ST function (2.3) with cj = 1 (j = 1, ..., k) for both k = 2 in panel (a) and k = 3 in panel (b) and sjt N (0, 1).

1.0

G =50

1.0

G =50 st=1 st=0

0.8

0.8

0.6 =0.01

0.6 =0.01

0.4

=0.1

0.4

=0.1

0.2

st=1

=1

st=2

0.2

=1 st=2

=2 0.0 4 2 0 (a) 2 4 st 0.0 4 2 0 (b)

=2 st 2 4

Figure 2: The ST function (2.3) with cj = {1, 2} in panel (a) (k = 2) and cj = {1, 0, 2} in panel (b) (k = 3) and sjt N (0, 1).

well, but also nests the mostly applied vector linear autoregressions and threshold autoregressions. When = 0, (2.1) is reduced to a vector autoregression. When

goes to innity, (2.1) becomes a vector threshold autoregresssion. Figure 1-2

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CNFs in VSTAR Models

and Figure 5-8 depict the featured statistical properties of (2.3) (Figure 5-8 are in Appendix). When k = 1, (2.3) is a non-decreasing nonlinear function. When

k > 1, (2.3) is exibly changeable depending on dierent transition variables stand location parameters c. For example, Figure 1 illustrates (2.3) with k > 1 and

cj that are the same for dierent j (j = 1, ..., k ); Figure 2 illustrates (2.3) with k > 1 and cj that are dierent for dierent j .3 . Common Nonlinear Features in Vector STAR Models Introduced by Engle and Kozicki (1993), common features that some features are present in each of several variables and a linear combination of those variables has no such features are used for analysing data properties in linear frameworks. The cointegrated system by Engle and Granger (1987) is a particular case that contains common stochastic trends. In this paper, we consider a group of nonlinear time series yt as dened in (2.1) and concern whether there are CNFs in yt . An (n 1) nonlinear vector yt is said to contain CNFs if each of the series yit is nonlinear and there is a nonzero vector

such that yt is linear.Suppose yt contains CNFs. Then we can nd a nonzero (n 1) vector such that nonlinearity is vanished in the linear combination yt . Now multiplying both sides of (2.1) by the vector results in

yt = +

p k=1

( k ytk + +

p k=1

) k ytk G (st ; , c) + vt(3.1)

in which yt , k , k , , and vt are the same as dened in (2.1) and G (st ; , c) in (2.3). The existence of CNFs in yt implies that = 0 and k = 0 for all k . Therefore, the linear combination (3.1) is equal to yt = + p k=1

k ytk + vt .

(3.2)

Consider the following numerical example, a bivariate STAR(1) model with a

CNFs in VSTAR Models

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3 2 1 0 1 2 30

20

40 (a)

60

80

100

4 2 0 2 4 60

20

40 (b)

60

80

100

Figure 3: Two nonlinear time series (solid, dashed) with CNFs in panel (a); their linear combination in panel (b).

CNF ( = 0, = 0, n = 2, t = 1, 2, ..., 200): y1t y2t 0.03 0.05 y1t1 =

0.02 0.1 y2t1 2 v