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Oct 20, 2013 (3 years and 9 months ago)

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NonLinear econometrics


Professor Timo Teräsvirta, CREATES, University of Aarhus and SSE (
sttimo@hhs.se
,
tterasvirta@econ.au.dk
)

Professor Changli He, Högskolan Dalarna

and Tianjin University of Finance and Economics (
changli.he@du.se
)

Professor Marcelo C. Medeiros, Pontifical Catholic University of Rio de Janeiro (
mcm@econ.puc
-
rio.br
)

P
rofessor Dick van Dijk, Erasmus University Rotterdam (
djvandijk@few.eur.nl
)

Professor Mika Meitz, Koc University, Istanbul (
mika.meitz@ku.edu.tr
)

Professor Tomoaki Nak
atani, Hokkaido University, Sapporo, and SSE (
naktom2@gmail.com
)

Dr. Andrés González, Banco de la República, Bogotá (
agonzago@banrep.gov.co
)

Dr. Anne Péguin
-
Feissolle, G
REQAM
-
CRNS (
peguin@ehess.crns
-
mrs.fr
)

Dr. Rickard Sandberg, SSE (
rickard.sandberg@hhs.se
)

Dr. Annastiina Silvennoinen, University of Technology, Sydney (
Annastiina.Silvennoinen@uts.edu.au
)

Dr. Birgit Strikholm, Bank of Estonia, Tallinn (
birgit.strikholm@gmail.com
)

Cristina Amado, MSc. (
cristina.amado@hhs.se
)

Ganeshkumar Munnorcode, MSc. (
ganeshkumar.munnorcode@hhs.se
)

†Zhenfang Zhao, MSc.

This project involves research on nonlinear econometric modelling. T
he focus has been on modelling single
-
equation relationships but is shifting towards systems of equations. Another area is the univariate modelling of
high
-
frequency economic series such as return series (stocks, exchange rates, etc.) that involves joint m
odelling of
conditional means and conditional variances. Specification of single
-
hidden layer feedforward artificial neural
network models and forecasting with them is receiving attention. Properties of


univariate and vector models of
autoregressive condi
tional heteroskedasticity are investigated.

Keywords
:
econometric modelling, linearity testing, parameter constancy, smooth transition models, time series

Publications


Books and Journal Special Issues


1.

Granger, C.W.J. and T. Teräsvirta (1993), Modelling

nonlinear economic relationships, Oxford: Oxford
University Press, Chinese edition (2006).
Shanghai: Shanghai University of Finance & Economic Press.

2.

Barnett, W.A., D.F. Hendry, S. Hylleberg, T. Teräsvirta, D. Tjøstheim and A. Würtz, eds.
(2000),
Nonlinear econometric modeling in time series analysis, Cambridge: Cambridge University Press.

3.

Franses, P.H. and T. Teräsvirta, eds. (2001), Nonlinear modeling of multivariate macroeconomic
relations.
Special Issue of Macroeconomic Dynamics, Vol. 5, No. 4
.



4.

Davidson, J. and T. Teräsvirta, eds. (2002),


Long memory and nonlinear time series.
Special issue of
Journal of Econometrics, Vol. 110, No. 2.



Articles published since 1993

1.

Granger, C.W.J., T. Teräsvirta and H. Anderson (1993), "Modeling nonlineari
ty over the business cycle,"
in J.H. Stock and M.W. Watson, eds. Business cycles, indicators, and forecasting, 311
-
325. Chicago,
University of Chicago Press.

2.

Teräsvirta, T. and H.M. Anderson (1993), "Characterizing nonlinearities in business cycles using smooth
transition autoregressive models," in M.H. Pesaran and S. Potter, eds. Nonlinearity and chaos in
econometrics, 111
-
128. New York: Wiley. Originally publ
ished in Journal of Applied Econometrics, 7,
S119
-
S136 (1992).

3.

Teräsvirta, T., C.
-
F. Lin and C.W.J. Granger (1993), "Power of the neural network linearity test," Journal
of Time Series Analysis, 14, 209
-
220.

4.

Deutsch, M., C.W.J. Granger and T. Teräsvirta
(1994), "The combination of forecasts using changing
weights," International Journal of Forecasting, 10, 47
-
57.

5.

Lin, C.
-
F. and T. Teräsvirta (1994), "Testing the constancy of regression parameters against continuous
structural change" Journal of Econometr
ics, 62, 211
-
228.

6.

Teräsvirta, T. (1994a), "Specification, estimation, and evaluation of smooth transition autoregressive
models," Journal of the American Statistical Association, 89, 208
-
218. Reprinted in: P. Newbold and S. J.
Leybourne, eds. (2003), Rece
nt developments in time series.
Cheltenham: Elgar.



7.

Teräsvirta, T. (1994b), "Testing linearity and modelling nonlinear time series," Kybernetika, 30, 319
-
330.

8.

Teräsvirta, T. (1994c), "Contrastes de linealidad y modelizacíon de series temporales no lineal
es,"
Cuadernos Económicos de ICE, 56, 29
-
42.

9.

Teräsvirta, T., D. Tjøstheim and C.W.J. Granger (1994), "Aspects of modelling nonlinear time series," in
R.F. Engle and D.L. McFadden, eds.
Handbook of econometrics, Vol. 4, 2919
-
2957. Amsterdam: Elsevier.

10.

Ter
äsvirta, T. (1995), "Modelling nonlinearity in U.S. gross national product 1889
-
1987," Empirical
Economics, 20, 577
-
597.

11.

Eitrheim, Ø. and T. Teräsvirta (1996), "Testing the adequacy of smooth transition autoregressive
models," Journal of Econometrics, 74,

59
-
75. Reprinted in: P. Newbold and S. J. Leybourne, eds. (2003),
Recent developments in time series.
Cheltenham: Elgar.

12.

Jansen, E.S. and T. Teräsvirta (1996), "Testing parameter constancy and super exogeneity in econometric
equations," Oxford Bulletin o
f Economics and Statistics, 58, 735
-
763. Reprinted in: A. Banerjee and D.F.
Hendry, eds. (1997), The econometrics of economic policy, 165
-
193.
Oxford: Blackwell.

13.

Teräsvirta, T. (1996a), "Power properties of linearity tests for time series," Studies in Nonlinear
Dynamics and Econometrics, 1, 3
-
10.

14.

Teräsvirta, T. (1996b), "Linearity testing and nonlinear modelling of economic time series," in: W.A.
Barnett, A.P. Kir
man and M. Salmon, eds.
Nonlinear dynamics in economics, 281
-
293. Cambridge:
Cambridge University Press.

15.

Teräsvirta, T. (1997), "Smooth transition models" (with Discussion), in: C. Heij, J. M. Schumacher, B.
Hanzon and K. Praagman, eds. System dynamics in

economic and financial models, 109
-
136. New York:
Wiley.

16.

Brännäs, K., J.G. De Gooijer and T. Teräsvirta (1998), "Testing linearity against nonlinear moving
average models," Communications in Statistics, Theory and Methods, 27, 2025
-
2035.

17.

Rydén, T., T. T
eräsvirta and S. Åsbrink (1998), "Stylized facts of daily return series and the hidden
Markov model," Journal of Applied Econometrics, 13, 217
-
244.

18.

Teräsvirta, T. (1998), "Modeling economic relationships with smooth transition regressions," in: A. Ullah
a
nd D.E.A. Giles, eds.
Handbook of applied economic statistics, 507
-
552. New York: Dekker.

19.

Granger, C.W.J and T. Teräsvirta (1999), "A simple nonlinear time series model with misleading linear
properties," Economics Letters, 62, 161
-
165.

20.

He, C. and T. Ter
äsvirta (1999a), "Properties of the autocorrelation function of squared observations for
second order GARCH processes under two sets of parameter constraints," Journal of Time Series
Analysis, 20, 23
-
30.

21.

He, C. and T. Teräsvirta (1999b), "Properties of mo
ments of a family of GARCH processes," Journal of
Econometrics, 92, 173
-
192.

22.

He, C. and T. Teräsvirta (1999c), "Statistical properties of the Asymmetric Power ARCH process," in R.F.
Engle and H. White, eds. Cointegration, causality and forecasting. A Fest
schrift in honour of Clive W.J.
Granger, 462
-
474.
Oxford: Oxford University Press.

23.

He, C. and T. Teräsvirta (1999d), "Fourth moment structure of the GARCH(p, q) process," Econometric
Theory, 15, 824
-
846.



24.

Lin, C.
-
F. and T. Teräsvirta (1999), "Testing par
ameter constancy in linear models against stochastic
stationary parameters," Journal of Econometrics, 90, 193
-
213.

25.

Skalin, J. and T. Teräsvirta (1999), "Another look at Swedish business cycles, 1861
-
1988," Journal of
Applied Econometrics, 14, 359
-
378.

26.

El
iasson, A.
-
C. (2001), " Detecting equilibrium correction with smoothly time
-
varying strength, " Studies
in Nonlinear Dynamics and Econometrics, 5, 115
-
131.

27.

Hall, A.D., J. Skalin

and T. Teräsvirta (2001), "A nonlinear time series model of El Niño," Environmental
Modelling and Software, 16, 139
-
146.

28.

Medeiros, M.C. and T. Teräsvirta (2001), "Statistical methods for modelling neural networks,"
Engineering Intelligent Systems for Ele
ctrical Engineering and Communications, 9, 227
-
235.

29.

Medeiros, M.C., A. Veiga and C.E. Pedreira (2001), "Modelling exchange rates: Smooth transitions,
neural networks, and linear models," IEEE Transactions on Neural Networks, 12, 755
-
764.

30.

Rech, G., T. Ter
äsvirta and R. Tschernig (2001), "A simple variable selection technique for nonlinear
models," Communications in Statistics, Theory and Methods, 30, 1227
-
1241.



31.

Arango, L.E., A. González and C.E. Posada (2002), "Returns and the interest rate: a non
-
linea
r
relationship in the Bogotá stock market," Applied Financial Economics 12, 835
-
842.

32.

He, C., T. Teräsvirta and H. Malmsten (2002), "Moment structure of a family of first
-
order Exponential
GARCH models," Econometric Theory, 18, 868
-
885.



33.

Lundbergh, S. and

T. Teräsvirta (2002a),


"Forecasting with smooth transition autoregressive models," in
M.P. Clements and D.F. Hendry, eds.
A companion to economic forecasting, 485
-
509. Oxford: Blackwell.

34.

Lundbergh, S. and T. Teräsvirta (2002b), "Evaluating GARCH models.
" Journal of Econometrics, 110,
417
-
435.

35.

Medeiros, M.C., A. Veiga and M. Resende (2002), "A combinatorial approach to piecewise linear time
series analysis,"Journal of Computational and Graphical Statistics, 11, 236
-
258.



36.

Skalin, J. and T. Teräsvirta (20
02), "Modelling asymmetries and moving equilibria in unemployment
rates," Macroeconomic Dynamics 6, 202
-
241.



37.

van Dijk, D., T. Teräsvirta and P.H. Franses (2002), " Smooth transition autoregressive models
-
A survey
of recent developments," Econometric Rev
iews, 21, 1
-
47.

38.

Lundbergh, S., T. Teräsvirta and D. van Dijk (2003), "Time
-
varying smooth transition autoregressive
models," Journal of Business and Economic Statistics, 21, 104
-
121.



39.

Medeiros, M.C. and A. Veiga (2003) , "Diagnostic checking in a flexibl
e nonlinear time series model,"
Journal of Time Series Analysis, 24, 461
-
482.



40.

Persson, A. and T. Teräsvirta (2003), "The net barter terms of trade: A smooth transition approach,"
International Journal of Finance and Economics, 8, 81
-
97.



41.

Teräsvirta, T., B. Strikholm and D. van Dijk (2003), "Changing seasonal patterns in quarterly industrial
production in Finland and Sweden," in: R. Höglund, M. Jäntti and G. Rosenqvist, eds. Statistics,
econometrics and society. Essays in honour of Leif Nor
dberg, 229
-
246.
Helsinki: Statistics Finland.

42.

Teräsvirta, T. and D. van Dijk (2003), "Modelling Finnish economic growth: 1860
-
2001," in: K. Alho, J.
Lassila and P. Ylä
-
Anttila, eds. Economic research and decision making
-
Essays on structural change,
growth

and economic policy, 199
-
219.
Helsinki: Research Institute of the Finnish Economy.

43.

van Dijk, D., B. Strikholm and T. Teräsvirta (2003), "The effects of institutional and technological change
and business cycle fluctuations on seasonal patterns in quarter
ly industrial production series,"
Econometrics Journal, 6, 79
-
98 (available here by permission of the Royal Economic Society).

44.

He, C. and T. Teräsvirta (2004), "An extended Constant Conditional Correlation GARCH model and its
fourth
-
moment structure," Eco
nometric Theory, 20, 904
-
926.

45.

Teräsvirta, T. (2004), "Nonlinear smooth transition modeling," in: H. Lütkepohl and M. Krätzig, eds.
Applied time series econometrics, 222
-
242. Cambridge: Cambridge University Press. Chinese Edition,
China Machine Press (2009
).

46.

Eklund, B. (2005), "Estimating confidence regions over bounded domains," Computational Statistics and
Data Analysis, 49, 349
-
360.

47.

Medeiros, M.C. and A. Veiga (2005), "A flexible coefficient smooth transition time series model," IEEE
Transactions in Ne
ural Networks, 16, 97
-
113.

48.

Teräsvirta, T., D. van Dijk and M.C. Medeiros (2005), "Linear models, smooth transition
autoregressions, and neural networks for forecasting macroeconomic time series: A re
-
examination,"
International Journal of Forecasting, 21,

755
-
774.

49.

He, C. and R. Sandberg (2006), "Dickey
-
Fuller type of tests against nonlinear dynamic models," Oxford
Bulletin of Economics and Statistics 68, 835
-
861.

50.

González, A. and T. Teräsvirta (2006), "Simulation
-
based finite
-
sample linearity test agains
t smooth
transition models," Oxford Bulletin of Economics and Statistics 68, 797
-
812.



51.

Granger, C.W.J., T. Teräsvirta and A. J. Patton (2006), "Common factors in conditional distributions for
bivariate time series," Journal of Econometrics, 132, 43
-
57.

52.

L
undbergh, S. and T. Teräsvirta (2006), "A time series model for an exchange rate in a target zone with
applications," Journal of Econometrics, 131, 579
-
609.

53.

Medeiros, M.C., T. Teräsvirta and G. Rech (2006), "Building neural network models for time series:

A
statistical approach," Journal of Forecasting, 25, 49
-
75.

54.

Meitz, M. (2006), "A necessary and sufficient condition for the strict stationarity of a family of GARCH
processes," Econometric Theory, 22, 985
-
988.

55.

Meitz
, M. and T. Teräsvirta (2006), "Evaluating models of autoregressive conditional duration," Journal
of Business and Economic Statistics, 24, 104
-
124.

56.

Strikholm, B. and T. Teräsvirta (2006), “A sequential procedure for determining the number of regimes
in a

threshold autoregressive model,” Econometrics Journal, 9, 472
-
491.

57.

Teräsvirta, T. (2006a), "Univariate nonlinear time series," in: T.C. Mills and K. Patterson, eds. Palgrave
Handbook of Econometrics: Volume 1: Econometric Theory, 396
-
424, Basingstoke: Pa
lgrave Macmillan.

58.

Teräsvirta, T. (2006b), "Forecasting economic variables with nonlinear models," in: G. Elliott, C.W.J
Granger and A. Timmermann, eds.
Handbook of Economic Forecasting. Amsterdam: Elsevier, Vol. 1,
413
-
455.



59.

Eklund, B. and T. Teräsvirta,

(2007), "Testing constancy of the error covariance matrix in vector
models," Journal of Econometrics, 140, 753
-
780.

60.

González, A. and T. Teräsvirta, (2008), "Modelling autoregressive processes with a shifting mean,"
Studies in Nonlinear Dynamics and Econo
metrics, 12, No.1, Article 1.

61.

He, C., H. Malmsten and T. Teräsvirta, (2008), "Higher
-
order dependence in the general Power ARCH
process and the role of power parameter," in Shalabh and C. Heumann, eds. Recent Advances in Linear
Models and Related Areas, 2
31
-
251. New York: Springer.

62.

Meitz, M. and P. Saikkonen, (2008), "Ergodicity, mixing, and existence of moments of a class of Markov
models with applications to GARCH and ACD models," Econometric Theory, 24, 726
-
748.

63.

Meitz, M. and P. Saikkonen, (2008) "Sta
bility of nonlinear AR
-
GARCH models," Journal of Time Series
Analysis, 29, 453
-
475.

64.

Nakatani, T. and T. Teräsvirta, (2008), "Positivity constraints on the conditional variances in the family
of conditional correlation models," Finance Research Letters, 5,

88
-
95.

65.

He, C., T. Teräsvirta and A. González, (2009), "Testing parameter constancy in vector autoregressive
models against continuous change", Econometric Reviews, 28, 225
-
245.

66.

Nakatani, T. and T. Teräsvirta, (2009) "Testing for volatility interactions
in the Constant Conditional
Correlation GARCH model", Econometrics Journal, 12, 157
-
163.

67.

Silvennoinen, A. and T. Teräsvirta, (2009) "Multivariate GARCH models", in T.G. Andersen, R.A. Davis,
J.
-
P. Kreiss and T. Mikosch, eds.
Handbook

of Financial Time Series, 201
-
229. New York: Springer.

68.

Teräsvirta, T., (2009) "An introduction to univariate GARCH models", in: T.G. Andersen, R.A. Davis, J.
-
P. Kreiss and T. Mikosch, Handbook of Financial Time Series, 17
-
42.
New York: Springer.

Doctora
l Dissertations:

1.

Åsbrink, S. E. (1997), Nonlinearities and regime shifts in financial time series.
Stockholm: EFI,
Stockholm School of Economics.

2.

Hagerud, G. E. (1997), A new non
-
linear GARCH model. Stockholm: EFI, Stockholm School of
Economics.

3.

He, C. (
1997), Statistical properties of GARCH processes. Stockholm: EFI, Stockholm School of
Economics.

4.

Skalin, J. (1998), Modelling macroeconomic time series with smooth transition autoregressions.
Stockholm: EFI, Stockholm School of Economics.

5.

Lundbergh
, S. (1999), Modelling economic high
-
frequency time series.
Stockholm: EFI, Stockholm
School of Economics.

6.

Eliasson, A.
-
C. (1999), Smooth transitions in macroeconomic relationships.
Stockholm: EFI, Stockholm
School of Economics.

7.

Medeiros, M.C. (2000), Re
gime
-
switching models: Thresholds, smooth transitions and neural networks.
Pontifical Catholic University of Rio de Janeiro, mimeo.

8.

Rech, G. (2002), Modelling and forecasting economic time series with single hidden
-
layer feedforward
autoregressive artific
ial neural networks, Stockholm: EFI, Stockholm School of Economics.

9.

Eklund, B. (2003), Four contributions to statistical inference in econometrics.
Stockholm: EFI,
Stockholm School of Economics.

10.

Malmsten, H. (2004), Properties and evaluation of volatilit
y models.
Stockholm: EFI, Stockholm School
of Economics.

11.

González, A. (2004), Nonlinear dynamics and smooth transitions. Stockholm: EFI, Stockholm School of
Economics.

12.

Sandberg, R. (2004), Testing the unit root hypothesis in nonlinear time series and pan
el models.
Stockholm: EFI, Stockholm School of Economics.

13.

Strikholm, B. (2004), Essays on nonlinear time series modelling and hypothesis testing.


Stockholm: EFI,
Stockholm School of Economics.

14.

Meitz, M. (2006), Five contributions to econometric theory a
nd the econometrics of ultra
-
high
-
frequency
data.
Stockholm: EFI, Stockholm School of Economics.

15.

Silvennoinen, A. (2006), Essays on autoregressive conditional heteroskedasticity.
Stockholm: EFI,
Stockholm School of Economics.

16.

Ahlersten, K. (2007), Empiri
cal asset pricing and investment strategies.
Stockholm: EFI, Stockholm
School of Economics.

Papers Accepted for Publication:

1.

Teräsvirta, T., "GARCH models", in: R. Cont, ed. Encyclopedia of Quantitative Finance. New York: Wiley.



Working Papers:

1.

Eliasson, A.
-
C. (1999a), Smooth transitions in a UK consumption function. Stockholm School of
Economics, Working Paper Series in Economics and Finance, No. 328.

2.

Eliasson
, A.
-
C. (1999c), Is the short
-
run Phillips curve nonlinear? Empirical evidence for Australia,
Sweden, and the United States. Stockholm School of Economics, Working Paper Series in Economics and
Finance, No. 330.

3.

Eliasson, A.
-
C. and T. Teräsvirta (2002), E
rror correction in DHSY. SSE/EFI Working Paper Series in
Economics and Finance, No. 517.

4.

Eklund, B. (2003), Testing the unit root hypothesis against the logistic smooth transition autoregressive
model, SSE/EFI Working Paper Series in Economics and Finance
, No. 546.

5.

Eklund, B. (2003), A nonlinear alternative to the unit root hypothesis, SSE/EFI Working Paper Series in
Economics and Finance, No. 547.

6.

González, A. (2004), A smooth permanent surge process, SSE/EFI Working Paper Series in Economics
and Financ
e, No. 572.

7.

González, A. and T. Teräsvirta and D. van Dijk (2005), Panel smooth transition regression models,
SSE/EFI Working Paper Series in Economics and Finance, No. 604.

8.

He, C. (2000), Moments and the autocorrelation structure of the Exponential GARC
H (p,q) process.
Stockholm School of Economics, Working Paper Series in Finance and Economics, No. 359.

9.

He, C., T. Teräsvirta (2002), An application of the analogy between vector ARCH and vector random
coefficient autoregressive models. SSE/EFI Working Pa
per Series in Economics and Finance, No.516.

10.

He, C. and R. Sandberg (2005a), Testing parameter constancy in unit root autoregressive models against
continuous change, SSE/EFI Working Paper Series in Economics and Finance, No. 579.



11.

He, C. and R. Sandber
g (2005b), Inference for unit roots in a Panel Smooth Transition Autoregressive
Model where the time dimension is fixed, SSE/EFI Working Paper Series in Economics and Finance, No.
581.

12.

He, C. and R. Sandberg (2005c), Testing for unit roots in nonlinear dy
namic heterogeneous panels,
SSE/EFI Working PaperSeries in Economics and Finance, No. 582.



13.

Lundbergh, S. and T. Teräsvirta (1998), Modelling economic high
-
frequency time series with STAR
-
STGARCH models. Stockholm School of Economics, Working Paper Series

in Economics and Finance,
No. 291.

14.

Malmsten, H. (2004). Evaluating exponential GARCH models. SSE/EFI Working Paper Series in
Economics and Finance, No. 564.

15.

Malmsten, H. and T. Teräsvirta (2004), Stylized facts of financial time series and three popular

models of
volatility. SSE/EFI Working Paper Series in Economics and Finance, No. 563.



16.

Péguin
-
Feissolle, A. and T. Teräsvirta (1999), A general framework for testing the Granger noncausality
hypothesis. Stockholm School of Economics, Working Paper Ser
ies in Economics and Finance, No. 343.
(Also available as GREQAM, Document du travail No. 99A42.)

17.

Péguin
-
Feissolle, A., B. Strikholm and T. Teräsvirta (2007), Testing the Granger noncausality hypothesis
in stationary nonlinear models of unknown functional

form. SSE/EFI Working Paper Series in
Economics and Finance, No. 672.

18.

Rech, G. (2002), Forecasting with artificial neural network models. SSE/EFI Working Paper Series in
Economics and Finance, No. 491.

19.

Skalin, J. (1998), Testing linearity against smooth

transition autoregression using a parametric
bootstrap. Stockholm School of Economics, Working Paper Series in Economics and Finance, No. 276.

20.

Silvennoinen, A. and T. Teräsvirta (2005), Multivariate autoregressive conditional heteroskedasticity
with smoo
th transitions in conditional correlations, SSE/EFI Working Paper Series in Economics and
Finance, No. 577.

21.

Silvennoinen, A. and T. Teräsvirta (2007a), Modelling multivariate autoregressive conditional
heteroskedasticity with the Double Smooth Transition
Conditional Correlation GARCH model, SSE/EFI
Working Paper Series in Economics and Finance, No. 652.



22.

Strikholm, B. (2006), Determining the number of breaks in a piecewise linear regression model. SSE/EFI
Working Paper Series in Economics and Finance, No
. 648.

23.

Teräsvirta, T. (1996), Two stylized facts and the GARCH(1,1) model. Stockholm School of Economics,
Working Paper Series in Economics and Finance, No. 96.



24.

Teräsvirta, T. and Z. Zhao (2007), Stylized facts of return series, robust estimates, and th
ree popular
models of volatility. SSE/EFI Working Paper Series in Economics and Finance, No. 662.




GAUSS Software:

1.

Lundbergh, S. (1998). A simple package for estimating STAR and GARCH models.
(See
Lundbergh and Teräsvirta, 1998).







MATLAB
Software:

1.

Medeiros, M.C. (2004). A package for neural network modelling. (See Medeiros, Teräsvirta and Rech,
2006).