ОЦЕНКА ИННОВАЦИОННЫХ ПРОЦЕССОВ В РА С ПОМОЩЬЮ МОДЕЛИРОВАНИЯ ЛАТЕНТНОГО ПЕРЕМЕННОГО Текст научной статьи по специальности «Строительство и архитектура»

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JEL: R11, R12, 030, O31

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Uju hmdmmbpummd' mqqmjfiO mOmbunLpjnLOnLd OnpmdnLdm^mO qnpd-pOpmgObp| L mOmbunLpjmO dn^b[m4npmdp pmim^mOfiO pmp^

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0|Omd|4 qnpdnOmj|O ibp[nLdnLpjnLOp h|dO4md t ^mrcnLgimdpmjfiO dm-dmOm^mj|O 2mppbp| dn^b[| ipm4, npp pnLj[ t mm[|u dmdmOm^mj|O 2mppp mpnhb[ d| 2mpp pm^^mgnLg|^Obp|: dmdmOm^mj|O 2mppbp| ^|Omd|^mO dn^b[minpinLd t np^bu ^mmmhm^mO mmmmOnLd (Random Walk model):

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1 Sbu Bartholomew D. J. and Knott M., Latent Variable Models and Factor Analysis. London: Arnold Publishers, 1999, t2 19-44:

2 Sbu Geweke J.F., The dynamic factor analysis of economic time series models. In: Aigner D.J., Goldberger A.S. (Eds.), Latent Variables in Socio-economic Models. Amsterdam, North-Holland, 1977, t2 365-382:

3 Sbu Zuur Alain, Tuck Ian & Bailey N., Dynamic factor analysis to estimate common trends in fisheries time series. Canadian Journal of Fisheries and Aquatic Sciences, 2003, t2 543:

4 Sbu A.S. (Eds.), Latent Variables in Socio-economic Models. Amsterdam, North-Holland, t2 365- 382: Harvey A.C., Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. 1989:

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5 Sb'u Ritter A., Muñoz-Carpena R., Dynamic factor modeling of ground and surface water levels in an agricultural area adjacent to Everglades National Park, Journal of Hydrology, Volume 317, Issues 3-4, 2006, ¿2 340-354:

6 Sb'u Holmes E. E., Ward E. J. and Wills K., MARSS: multivariate autoregressive state-space models for analyzing time-series data. R Journal, 2012, ¿2 12-13:

Uju^Çuni' ûnrçb[Ç bpïnL u^miùbpÇ pmrçmrçpÇ^ùbpù nLùbù pmqûm^m^i ùnpùmi pm2ËnLÙ, pùrç npnLÙ, wt-Ç pm2Ëùmù ïnimpÇmgÇnù ùmmpÇgp umh-ûwùiwô ¿t: ^bpçÇùÇu Ùmuni Ïmù ÙÇ 2mPù mmppbpm^ùbp: Uju^bu'

1. UËwiùbpÇ impÇmgÇmùbpp hmimump bù, L pmgm^mjnLÛ bù Ïnim-pÇmgÇmùbpp:

2. UËwiùbpù mùbù mmppbp impÇmgÇmùbp, L pmgm^mjnLÛ bù Ïnim-pÇmgÇmùbpp:

3. <w4wuwp impÇmgÇmùbp L ïnimpÇmgÇmùbp:

4. Swppbp impÇmgÇmùbp L ïnimpÇmgÇmùbp (¿Ïmù uwhûwùw^w-Ïmûùbp):

<mûmàmjù QmpÇ' xt ib^mnpÇ u^qpùm^mù 4Ç^m^p umhûmùimû t hbinlijw[ L|bpu|

npmbrç x0 -ù pw2Ë4wô t pmqûm^m^i ùnpùmi pm2Ëùmù opbùpni, npp pùnL-pmqpinLÙ t 0 ùÇçÇùni L pmim^mùm^m^ ûbô impÇmgÇmùbpni nL 0 Ïnim-pÇmgÇmùbpni ùmmpÇgni:

Uju^Çuni' ûnrçb[Ç mrcmùgpmjÇù ^mpmûbmpbpù bù.

1. Lmmbùm ^n^n^m^mùùbpÇ pmùm^p: ^bùp, np qnjmpjnLù ¿nLùÇ npLt 4bp[nLÔwïwù ûbpnrç, npp hùmpminpnLpjnLù mm ûÇwù2wùwïn-pbù npn2b[nL lmmbùm ^n^n^m^mùùbpÇ o^mÇûw[ pmùmïp: Q-npô-ùmïmùnLÛ rçm pùmpinLÛ t Çù^bu $npûm[ pmùm^mïmù ûbpnrçùb-pni, opÇùmï UïmjïbÇ Çù^npûmgÇnù ¿w^iwùÇ2n48 (AIC), mjù^bu tl ummgimô mp^jnLùpùbpÇ ùbïùmpmùb[ÇnLpjmûp:

2. UËmiùbpÇ pm2Ëùmù ïménLgimôpp:

7 Sbu Zuur A. F., Fryer R. J., Jolliffe I. T., Dekker R. and Beukema J. J., Estimating common trends in multivariate time series using dynamic factor analysis. Environmetrics, 14(7), 2003, tç 665-685:

8 Sbu Akaike H., A new look at the statistical model identification, IEEE Transactions on Automatic Control, 19 (6), 1974, tç 716-723:

Un^b[nLd mOhmjm ^mpmdbmpbpp qOmhmminLd bO u^munLdObpi dmpu^dm[mgdmO (Expectation Maximization) m[qnpfipdn49:

Uju dnmbgdmdp ^Omdfi^ qnpdnOmj^O 4bp[nLdnLpjnLOp hmumOb[i t Brodgar ^ndbpgfinO ^ipmrcm^mO dpmqpmjfiO ^mpbpnLd, iO^bu OmL n^ ^ndbpg^nO R m^m^mqpm^mO [bqi|10 «MARSS» (Multivariate Autoregressive State Space)11 ^mpbpnLd:

0n^b[| mb^b^mmim^mO hfidp bO hmdmpinLd Uqqmj^O m^m^mqpm-^mO dmrcmjnLpjmO12, <mdm2^mphmjiO mOmbum^mO $npnLdi13, <mdm2^mp-hmj|O pmO^|14, «<bp|pb2» h|dOm^pmd|15 mpmdm^pmd' <mjmummO|O ib-pmpbpn^ dmdmOm^mj^O 2mppbpp: UmnpL Obp^mjmgimd bO ^fipmrcin^ 2mp-pbpp L ^pmOg m^pjmpObpp: Cmppbpi pOmpnLpjmO umhdmOm^m^nLdO mjO t, np 4bp2|OObpu, pum mmpim ^md brcmdujm^Obpi, hmumOb[i [|ObO 2007-2017 pp. hmdmp:

^bpnO2jm[ 2mppbp| h^dOm^mO dmup hmumOb[i t mmpb^mO ^mpimd-pni: Pwiw^wOw^w^i ^fimmp^mdObp m^mhn4b[nL L ibp2|OObp|u brcmdu-jm^mjfiO hmumOb[| 2mppbpfiO hmdm^mmmu^mObgOb[nL hmdmp ^fipmrcb[ bOp dmdmOm^mj^O 2mppbpi ^b^nd^nq|g|wj| dbpn^p, dmuOminpm^bu' Boot-Feibes-Lisman16 (BFL) dnmbgdmdp:

0bpn^| hfidpmd mrc^m u^qpnLOpp dmdmOm^m2p2mOObpi di2L ^n^n-^mpjnLOObpp OimqmqnLjO| hmugOb[O t' hm2i| mrcOb[n4 mmpb^mO mijm[-Obp| umhdmOm^m^nLdObpp: Ubpn^fi O^mmm^mj^O $nLO^g|mO nLO| hb-inlijui[ inbupp'

Uju dbpn^ni dmdmOm^mj^O 2mppbpi ^b^nd^nq|g|mO |pw^wOmg4b[ t R m^m^mqpm^mO [bqi| «tsdissag2» ^mpbp|17 d|2ngni:

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LmmbOm 2mppbpp ummOm[nLg hbmn mOhpmdb2m t ^pmOp db^Ompm-Ob[: Rp^buqfi pmgmmpb[| [|ObO ummgimd [mmbOm 2mppbpp, ibp2|OObpu bOpmp^nLd bOp ^mmdmO ^md ¿2^pmdmO: Uju ^O^pi [nLddmO hmdmp mrcm-

9 Sb'u Zuur A. F., Fryer R. J., Jolliffe I. T., Dekker R. and Beukema J. J., Estimating common trends in multivariate time series using dynamic factor analysis. Environmetrics, 14(7), 2003, t2 665-685:

10 Sb'u https://www.r-project.org/

11 Sb'u Holmes E. E., Ward E. J. and Scheuerell M. D., Analysis of multivariate timeseries using the MARSS package, March 30, 2018, Northwest Fisheries Science Center, NOAA: https://cran.r-project.org/web/packages/MARSS/index.html

12 Sb'u http://www.armstat.am/

13 Sb'u https://www.weforum.org/

14 Sb'u http://www.doingbusiness.org/data/exploreeconomies/armenia

15 Sb'u https://www.heritage.org/index/

16 Sb'u Boot J., Feibes W. and Lisman J., Further methods of derivation of quarterly figures from annual data, Cahiers Economiques de Bruxelles, 36, 1967, t2 539-546:

17 Sb'u https://cran.r-project.org/web/packages/tsdisagg2/index.html

T

ib[ lipmrcb[fi t VARIMAX m[qnpipdp , npp pmgmmpinLd t mju^bp^. hb-mLjm[ bplnL dn|b[Obpp hmdmpdbp bO'

b

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VARIMAX m[qnpipdi O^mmm^O t qmOb[ mjO^fiufi m x m ¿m^i H ^mmdmO dmmpfig, npi |b^pnLd Z dmmpigi qnpdmligObpp InLObOmO mRmib[mqnLjO imp^mg^m: UmnpL Obp^mjmgimd bO ummgimd lnrcb[jm-ginO qnpdm^igObpp |immplb[i ^n^n|mlmOObpi L [mmbOm qnpdpOpmg-Obpi di2L: QnpdpOpmgObpp dblOmpmOb[nL hmdmp mrcm4b[m^bu hm2ii bO mpOib[ |fimmplb[i ^n^in|mlmOObpi ipm 4bp2fiOObpfiu ipmlmO mqibgnL-pjnLOObpp.

UqjnLum^ 1

LuiinbQw qnpdQOpuigCibpfi uiqribgnLpjnLt/Q ijfjinLupQbifi i^ni^nluuiliiuDQbpfi ipLU

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SS m2|mmnLd 0.37098977 0.31627458 0.062055502 0.04824405

SS mpmmipnLpjmO 6m4m[Obp 0.34654777 0.29114749 0.049727969 0.0257277

S<S Imqdmlbp^nLpjnLOObpi pmOml| 0.34033303 0.20721556 0.020124404 0.02627517

bnpmqnLjO mb|On[nqfimObpfi hmumOb[inLpjnLO 0.33945677 -0.07735197 0.034268237 -0.026297

Pmp^pmqnLjO IppnLpjmO npml| 0.33037407 0.02895113 0.070755879 -0.04508076

bnpmdnL6ml|mO Impn^nLpjnLO 0.32181217 0.27288154 0.009304622 -0.01036773

R&D n[npmnLd hmdm[umpmO -l|mq-

dmlbp^nLpjnLOObp hmdmqnp6ml|- 0.3211058 0.08501335 0.025695859 -0.02357315

gnLpjnLO

Ui^pmOpObpfi 2nLl|mji mpijnLOmibmnLpjnLO 0.31653438 0.17528328 0.349089777 -0.07331508

PfiqObufi qmpqmgim6nLpjnLO 0.30436836 0.1551652 0.210699546 -0.02752351

Mmqdmlbp^nLpjnLOObpi R&D 6m|ubp 0.25901321 0.37062691 0.17892508 -0.02835458

^bmnLpjmO l|nr^dfig mb|On[nqfiml|mO mpmmipmOpi qOnLd 0.18091952 0.30024086 0.436958982 0.12165116

hOumfimnLgfinOm[ qmpqmgnLd 0.11905524 0.13440556 0.206997559 0.30543565

QfimOml|mOObpfi L fiOdbObpObpfi hmumOb[inLpjnLO 0.10707241 0.41296235 0.185980566 0.04503793

JfiOmOuml|mO hmdml|mpqfi qmpqmgim6nLpjnLO 0.10507797 0.07125601 0.47857909 0.17027415

Mmqdmlbp^nLpjnLOObpi l|nr^dfig mb|On[nqfimObpfi jnLpmgnLd 0.09128109 0.28710617 0.418130012 0.29213151

Qfimml|mO l|mrcnLjgObpfi npml| 0.01596287 0.48487836 0.105832579 0.0549497

SOmbuml|mO mqmmnLpjmO hmdmpii -0.02873044 0.16539559 0.088058126 0.37621502

U2|mmnLdi mpijnLOmibmnLpjnLO -0.1072763 -0.11935278 0.148439638 0.316459

18 Sb'u Herve Abdi, Factor Rotations in Factor Analyses. Encyclopedia of social sciences research methods, 2003:

SbSbUUUUÂblJUShîUîUb 1Л~1ЧЬШЧПРП|-и 183

^bp[nLÓb[ní pшgшhшJmЦшá ^mbûm qnpápûpшgûbpp U r|mшpïbф фnфnËшïшûûbpÇ Цpш ibpçÇûûbpÇu nLÛbgшá шqrbgnLpJnLÛp, umшgЦnLÚ bû 2шpùbpÁ.

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Qâm^mm^bp 1. Lшmbû^л ûnpшúnlóшL^шû qnpâQûpwgûbp! r^flûшúf^l^шû «-nú 2007-2017 pp.

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ûb[ni ШJ[ mpшûuфnpúшg|шûbp: 0^ш1штЦш0 ^mbûm 2шpùbpp ^^ш^ф bû «únpшúnLÔnLpJnLÚ - mûmbunLpjnLû» фnËÏшщшïgЦшánLpJnLÛp ùnrçb^in-pb[nL hшúшp, ^ûÇ np ШJu úbpnrl Ï|pшßúшû rçb^pnLÙ hûшpшЦnpnLpJnLÛ t umb^áínLÜ r|mшpïb[| U щшpq gnLgшû|2Ûbp| ùÇçngni ûïшpшqpb[nL ûnpш-ùnL^^û pшpr U |ûmbqpш[ qnpápûpшgûbp: ЪbpïшJшgЦшá úbpnrшpшûnL-pjnLûp ûnpшúnLáшïшû qnpápûpшgûbp| ùnrçb^inp^û mbuшûÏJnLÛ|g [nL-ónLÜ t bpïnL Ëûrlp' únpшúnLáшïшú qnpápûpшgûbpû шршЦЬ[ шúpn^2шïшû pûnLpшqpn^ 2шpùbp| qûшhшmnLÚ, Çû^bu ûшU mûmbuшq|mшïшû ùnrçb[-ûbpnLÙ ûnpшúnLáшïшû qnpápûpшgûbp| ûbpïшJшgúшû ¿шфnrçшïшûnLpJШû û^qbgnLÙ:

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SbSbUUUU»blJUSh4U4Ub 1Л~1ЧЬШЧПРП|-и 185

АЛЬБЕРТ САРГСЯН

Аспирант кафедры экономической информатики и информационных систем АГЭУ

Оценка инновационных процессов в РА с помощью моделирования латентного переменного.- Количественная оценка инновационных процессов в национальной экономике имеет решающее значение для анализа последних, а также для разработки национальной инновационной политики. Учитывая ряд проблем при оценке инновационных процессов, представлен новый подход, основанный на динамическом факторном анализе, позволяющий рассматривать инновационный процесс как ненаблюдаемый процесс и описать с помощью латентных переменных.

Ключевые слова: инновационные процессы, динамическое моделирование факторов, скрытые процессы, оценка инновационных процессов. JEL: R11, R12, 030, O31

ALBERT SARGSYAN

Post-graduate at the Chair of Economic Informatics and Information Systems at ASUE

Estimating innovation Processes in the RRA Via Latent Variable Modeling.- Quantitative estimation of the innovation processes in national economy has crucial importance for analyzing the latter as well as for developing national innovation policy. Taking into account a number of challenges in estimation innovation processes a new approach is presented which is based on dynamic factor analysis and allows to consider innovation process as latent and unobservable process.

Key words: innovative processes, dynamic factor modeling, Latent Processes, estimation of innovative processes. JEL: R11, R12, 030, O31