• 8/20/2019 univariate time series.ppt


    Time Series Analysis

  • 8/20/2019 univariate time series.ppt



    • A time series is a sequence of observationstaken sequentially in time

    • An intrinsic feature of a time series is that,

    typically adjacent observations are dependent

    • The nature of this dependence amongobservations of a time series is of

    considerable practical interest

    • Time Series Analysis is concerned withtechniques for the analysis of this dependence

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      ime Series Forecasting

    ime Series Forecasting

    • Examine the past behavior of a timeExamine the past behavior of a timeseries in order to infer something aboutseries in order to infer something aboutits future behaviorits future behavior

    • A sophisticated and widely usedA sophisticated and widely usedtechnique to forecast the future demandtechnique to forecast the future demand

    ExamplesExamples• Univariate time series: AR, MA, ARMA,Univariate time series: AR, MA, ARMA,ARIMA, ARIMA-GARCHARIMA, ARIMA-GARCH

    • Multivariate: VAR, CointegrationMultivariate: VAR, Cointegration

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    Univariate Time-series Models• The term refers to a time-series that consists of single

    (scalar) observations recorded sequentially over equaltime increments

    • Univariate time-series analysis incorporates making use of

    historical data of the concerned variable to construct a

    model that describes the behavior of this variable (time-


    • This model can, subsequently, be used for forecasting


    •  Appropriate technique for forecasting high frequency time

    series here data on independent variables are either

    non-e!istent or difficult to identify

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    Famous forecasting quotes

    • "I have seen the future an it is very mu!h li"ethe #resent, only longer." - Kehlog Albran, The Profit Kehlog Albran, The Profit  

    – This nugget of pseudo-philosophy is actually a concisedescription of statistical forecasting. We search for statisticalproperties of a time series that are constant in time - levels, trends,

    seasonal patterns, correlations and autocorrelations, etc. We thenpredict that those properties will describe the future as well as thepresent.

    • $%rei!tion is very iffi!ult, es#e!ially if it&sa'out the future($ Nils Bohr, Nobel laureate in Physics

    – This quote serves as a warning of the importance of validating aforecasting model out-of-sample. It's often easy to find a modelthat fits the past data well--perhaps too well! - but quite anothermatter to find a model that correctly identifies those patterns in the

    past data that will continue to hold in the future

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    Time series data

    • Se!ular Tren! long run pattern

    • Cy!li!al )lu!tuation! expansion and

    contraction of overall economy "businesscycle#

    • Seasonality!  annual sales patterns tied

    to weather, traditions, customs

    • Irregular or ranom !om#onent

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  • 8/20/2019 univariate time series.ppt


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    • https"##$youtube$com#atch%v&s'cg


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    Ex-Post vs. Ex-Ante Forecasts

    • $ow can we compare the forecastperformance of our model% 

    • There are two ways&

    *+ Ante! 'orecast into the future, wait forthe future to arrive, and then compare theactual to the predicted

    *+ %ost! 'it your model over a shortenedsample

    • Then forecast over a range of observed data• Then compare actual and predicted&

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    Ex-Post and Ex-Ante

    Estimation & Forecast Periods

    • (uppose you have data covering theperiod )*+&-)./)&-0

    80.1 99.4 2001.4

    Ex-Post  Estimation Period









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    Examining the In-Sample Fit

    • 1ne thing that can be done, once youhave fit your model is to examine the in.sample fit

    That is, over the period of estimation, youcan compare the actual to the fitted data

    2t can help to identify areas where your

    model is consistently under or overpredicting  take appropriate measures

    (imply estimate equation and look atresiduals

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    Model Performance

    • 34(E 5√")6n∑"fi  xi#/ . differencebetween forecast and actual summed  smaller the better

    • 4AE 7 4A8E  smaller the better

    • The Theil inequality coefficient alwayslies between 9ero and one, where 9eroindicates a perfect fit&

    • :ias portion . Shoul 'e ,ero  $ow far is the mean of the forecast from

    the mean of the actual series%

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    Model Performance

    •  ;ariance portion . Shoul 'e ,ero  $ow far is variation of forecast from forecast of

    actual series variance%

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    Autocorrelation function (ACF)

    Auto!orrelation fun!tion AC). of a ranom#ro!ess es!ri'es the !orrelation 'et/een the

    #ro!ess at ifferent #oints in time(

    0et 1 t  'e the value of the #ro!ess at time t  /here t  may 'e an integer for a is!rete-time #ro!ess or a real num'er for a!ontinuous-time #ro!ess.(

    If 1 t  has mean 23 an varian!e 4 5 then the

    efinition of AC) is


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    ACF & PACF

    • The partial autocorrelation at lag k isthe regression coefficient on >t.k when>t is regressed on a constant,>t.)?>t.k 

    • This is a partial correlation since itmeasures the correlation of values thatare periods apart after removing the

    !orrelation from the intervening lags 

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    Stationar !ime Series

    • A stochastic process is said to be stationary  if its mean

    and variance are constant over time and the value ofcovariance between two time periods depends only thedistance or gap or lag between the two time periods andnot the actual time at which the covariance is computed

    • 2n time series literature, such stochastic process isknown as /ea"ly stationary or !ovarian!e stationary 

    • 2n most practical situation, this type of stationary oftensuffices

    • A time series is stri!tly stationary  if all the moments ofits probability distribution and not just the first two"mean 7 variance# are invariant over time


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    Stationar !ime Series

    • $owever, if the stationary process is normal , the weaklystationary process is also strictly stationary as normalstachastic process is fully specified by its two moments,the mean 7 variance

    • @et >t be a stochastic time series with properties!

    Mean ! E">t# 5 Varian!e ! var">t# 5 E ">t  #/ 5 B /

    Covarian!e !Ck 5 E ">t  #">tDk  #  autocovariancebetween >t and >tDk, i&e& between two > values k pariods

    apart• 2f k 5 , we obtain C, which is simply the variance of >

    • 2f k 5 ), C) is the covariance between two adjacent valuesof >

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    Stationary Time Series

    • ow, if we shift the origin from >t to >tDm, themean, variance and autocovariance of >tDm must be same as those of >t

    • This, if a time series is stationary, its mean,variance, autocovariance remains same, nomatter at what point we measure them i&e&

    they are time invariant

    • (uch a time series is tend to returns to itsmean, called mean reversion

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    on-stationary Series

    • A non.stationary time series will have a timevarying mean or variance or both

    • 'or non.stationary time series, we can study itsbehavior only for the time period under

    consideration• Each set of time series data will therefore be

    for a particular episode

    • (o it is not possible to generali9e it to other timeperiods

    • Therefore, for the #ur#ose of fore!asting,non-stationary time series may 'e of little

    #ra!ti!al value

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    • Most statisti!al fore!asting methosare 'ase on the assum#tion that thetime series !an 'e renerea##ro+imately stationary i(e(,

    $stationarie$. through the use ofmathemati!al transformations

    • A stationarie series is relatively

    easy to #rei!t: you sim#ly #rei!tthat its statisti!al #ro#erties /ill 'ethe same in the future as they have

    'een in the #ast6

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    • The #rei!tions for the stationarie series!an then 'e $untransforme,$ 'y reversing/hatever mathemati!al transformations/ere #reviously use, to o'tain #rei!tionsfor the original series

    • The etails are normally ta"en !are of 'ysoft/are

    • Thus, fining the se8uen!e oftransformations neee to stationarie atime series often #rovies im#ortant !luesin the sear!h for an a##ro#riatefore!asting moel(

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    !andom or "hite oise Process

    • 9e !all a sto!hasti! #ro!ess #urelyranom or /hite noise #ro!ess  if it hasa ero mean, !onstant varian!e anserially un!orrelate

    • *rror term entere in C0RM is assumeto 'e /hite noise #ro!ess as u ii ;,4 5.

    • Ranom /al" moel, non-stationary innature, o'serve in asset #ri!e, sto!"#ri!e or e+!hange rates is!uss later.

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    !rend ACF & PACF

    The AC) fun!tion sho/s a efinite #attern, ite!reases /ith the lags(This means there is a tren in the ata(Sin!e the #attern oes not re#eat , /e !an !on!luethat the ata oes not sho/ any seasonality(

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    !rend & Seasonalit ACF & PACF

    The AC) #lots !learly sho/ a re#etition in the #attern ini!atingthat the ata are seasonal, there is #erioi!ity after every

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    Estimation and !emoval of Trend #Seasonality 

    • Classi!al =e!om#osition of a Time Series1t > mt ? st ? @t

    mt ! trend component "deterministic, changes slowly

    with t#F st ! seasonal component "deterministic, period d#F @t ! noise component "random, stationary#&

    • Aim: Extract components mt and st, and hope that

    >t will be stationary& Then focus on modeling >t&

    • =e may need to do preliminary transformations ifthe noise or amplitude of the seasonalfluctuations appear to change over time&

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    Time series ata, 1t > mt ? st ? @t

    AC), %AC), A=) tests

    on-stationary series Stationary Series, 1t>@t

    =e-tren anBor=e-seasonalie

    Stationary Series @t

    Moel for @tAR, MA, ARMA

    Resiual series 9

    *stimate AR, MA, ARMA #arameters

    )ore!ast 1t In-sam#leBut of sam#le.

    Moel for 1t>@tAR, MA, ARMA

    Resiual series 9

    *stimate AR, MA, ARMA#arameters

    )ore!ast 1t>@t In-sam#leBut of sam#le.

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    Backward Shift Operator

    • This operator D plays an important role in themathematics of T(A

    • D1t>1t-< an in general Ds1t > 1t-s

    • A polynomial in the lag operator takes the formG":#5)D G):D G/:/D?&D Gq:q, where G)? Gq areparameters

    • The roots of such a polynomial are defined as qvalues of : which satisfy the polynomialequation G":# 5

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    Backward Shift Operator

    • If 8>

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    Elimination of !rend

    • onseasonal moel /ith tren: 1t > mt ? @t,*@t.>;

    • Methos:

    a. Moving Average Smoothing'. *+#onential Smoothing

    !. S#e!tral Smoothing

    . %olynomial )itting

    e. =ifferen!ing " times to eliminate tren

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    $i%erencing &'( times to eliminatetrend

    • Iefine the backward shift operator  : as follows! : Jt 5Jt.)

    • =e can remove trend by differencing, e&g&


     - 1t-<

    , an,  1t - 51t-< ? 1t-5 

    • 2t can be shown that a polynomial trend of degree kwill be reduced to a constant by differencing k times,that is, by applying the operator ").:#k Jt

    • Kiven a sequence LxtM, we could therefore proceed bydifferencing repeatedly until the resulting series canplausibly be modeled as a reali9ation of a stationaryprocess&

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    Elimination of Seasonalit 

    • Seasonal moel /ithout tren: 1t > st ? @t,


    a.Classi!al =e!om#osition

    Regress level varia'le @. on ummy varia'les /ith or /ithout

    inter!e#t. Cal!ulate resiuals

    A these resiuals to mean value of @

    Resulting series is eseasonalie time series

    '. =ifferen!ing at lag to eliminate #erio

    Sin!e, st - st- > ;, ifferen!ing at lag /ill eliminatea seasonal !om#onent of #erio (


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    Elimination of !rend#Seasonalit 

    • Elimination of both trend and seasonalcomponents in a series, can be

    achieved by using trend as well asseasonal differencing

    • )or e+am#le:

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    Time series ata, 1t > mt ? st ? @t

    AC), %AC), A=) tests

    on-stationary series Stationary Series, 1t

    =e-tren anBor=e-seasonalie

    Stationary Series

    Moel for stn( seriesAR, MA, ARMA

    Resiual series 9

    *stimate AR, MA, ARMA #arameters

    )ore!ast 1t after re-transformationIn-sam#leBut of sam#le.

    Moel for 1t>@tAR, MA, ARMA

    Resiual series 9

    *stimate AR, MA, ARMA#arameters

    )ore!ast 1tIn-sam#leBut of sam#le.

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    on-Seasonal # SeasonalA!) MA # A!MA Process

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    Autoregressi$e Process

    • A3")# model specification is

     @t > m ? @t- m ? ut @t >

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    Autoregressi$e Process

     @t >

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    Autoregressi$e Process•  A.(/) 0rocess"

    t % ' t-' #  t- # ut

    •  A.(p) 0rocess"

    t % ' t-' #  t- # .# * t-* # ut

     t % + * t-*

    • 1efining the A, *olnomial

    ()= 1−

    '− ... −


    * *

    • e can rite the A.(p) model concisely as"

    ()t % ut 

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    Autoregressi$e Process

    • t is sometime difficult to distinguishbeteen A. processes of different orderssolely based on correlograms

    •  A sharper discrimination is possible on thebasis on partial autocorrelation coeff 

    • For an A,(*) PACF $anis/es for lagsFor an A,(*) PACF $anis/es for lagsgreater t/an *. 0/ile ACF of an A,(*)greater t/an *. 0/ile ACF of an A,(*)decas ex*onentialldecas ex*onentiall

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    1o$ing A$erage Process

    • n a pure 2A process, a variable is e!pressed

    solely in terms of the current and pervious hite

    noise disturbances

    1A(') Process t % ut # q' ut-'

    • 2A(q) 0rocess"t % ut # q' u t-' # ... # qqu t-q

    2ut3 ∼ 45(6σ)

    • 1efining the 2A polynomialq() % ' # q' # ... # qq q

     e can rite the 2A(q) model concisely as"

    t % q() ut.

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    1o$ing A$erage Process

    • or parameter identifia7ilit reasons, andin analogy ith the concept of causality for A. processes, e require that all roots of

    θ(3) be greater than 4 in magnitude

    • The resulting process is said to bein$erti7le

    • !/e PACF of an 1A(q) decas!/e PACF of an 1A(q) decasex*onentiallex*onentiall

    • !/e ACF $anis/es for lags 7eond q!/e ACF $anis/es for lags 7eond q

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    !/e single

    negati$e s*i8e atlag ' in t/e ACF

    is an 1A(')


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    A,1A Process

    •5e can put an A.(p) and an 2A(q) processtogether to form the more general A.2A(p,q)


     t −   t-' − ... −* t-* = ut # θ  ut-'  ... θq ut-



    0/ere 2ut3 ∼ 45(6σ().

    • 6y definition, e require that 7yt8 be stationary$

    • Using the compact A. 9 2A polynomial notation,e can rite the A.2A(p,q) as"


    t = θ() ut, 2ut3 ∼45(6σ()

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    A,1A Process

    • or stationarity and invertibility, e requireas before, that all roots of () and θ(3) begreater than 4 in magnitude

    •  A. 9 2A are special cases" an A.(p)&A.2A(p,:), and an


    • ACF & PACF 7ot/ deca ex*onentiallACF & PACF 7ot/ deca ex*onentiall

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    S l ACF9PACF

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    Sam*le ACF9PACF• For an A!*p+ the A,F decays geometrically) and the

    PA,F is ero eyond lag p. The sample A,F/PA,F

    sho0ld exhiit similar ehavior) and signi1cance atthe 234 level can e assessed via the 0s0alo0nds

    • For an MA*5+ the PA,F decays geometrically) and the

    A,F is ero eyond lag 5. The sample A,F/PA,Fsho0ld exhiit similar ehavior) and signi1cance atthe 234 level can also e assessed via the ± 6.27/√no0nds

    • For an A!MA*p)5+) the A,F # PA,F oth decay


    • Examining the sample A,F/PA,F therefore can serveonly as a g0ide in determining possile maxim0mval0es for p # 5 to e properly investigated via AI,,.


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    Sam*le ACF9PACF

    • The 8A

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    :rder Selection91odel ;dentification

    •n real-life data, there is usually no underlying true model$The question then becomes ;ho to select an appropriate 

    statistical model for a given data set%<

    •  A breakthrough as made in the early 4'=:s by the

    apanese statistician Akaike$

    • Using ideas from information t/eor, he discovered a

    ay to measure ho far a candidate model is from the

    ;true< model$

    • 5e should therefore minimi>e the distance from the truth,

    and select the A.2A(p,q) model that minimizes Akaike?s

    nformation @riterion (A;C)"

    ( )   ( )q p L AIC    ++−=   2ˆ,ˆ,ˆlog2   2σ θ φ 

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    :rder Selection91odel ;dentification

    • here denotes the likelihood evaluated at the23?s of φ, θ, and σ2, respectively$ (oadays eactually use a bias-corrected version of A@ called A@@$)

    • The first term in the A@ e!pression measures ho ell

    the model fits the dataB the loer it is, the better the fit$• The second term penali>es models ith more


    • inal model selection can then be based upon goodness-

    of-fit tests and model parsimony (simplicity)$• There are several other information criteria currently in

    use, C6@, 0, C@, 213, etc$, but A;C and S

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    on-stationary Time Series

    - Unit !oot- A!IMA

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    ,andom 4al8 1odel

    •  Although our interest is on stationary timeseries, e often encounters non-stationary

    time series

    • @lassic e!ample" .52 (stock price,

    e!change rate)

    • @an be of to types D ,andom 0al8 0it/out drift =t% =t-' # ut

     D ,andom 0al8 0it/ drift =t% > # =t-' # ut

    , d l8 it/ t d ift

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    ,andom 0al8 0it/out drift

    • 3et ='%=t-'#u'

    • ='%=6#u'? =( % ='#u(  =(%=6#u'#u(

    • E(=t) % =6 and $ar(=t) % t@(

    • 2ean value of E is its initial value, hich is

    constant, but as t increases, its variance

    increases indefinitely, thus violating the

    stationary condition

    • .52 is the persistence of random shocks andimpact of particular shock does not die aay

    • .52 said to have infinite memory

    , d l8 it/ d ift

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    ,andom 0al8 0it/ drift

    • =t%> #=t-' # ut 

    =t% > # ut

    • Et drift upard or donard depending

    upon > being positive or negative

    • ,41 is an exam*le of 0/at is 8no0n

    as unit root *rocess

    B it , t P

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    Bnit ,oot Process

    • Cay, Et&FEt-4GutB -4 H F H4

    • This is an A.(4) process

    • ;f;f %' t/en 0e get ,41 0it/out drift non-%' t/en 0e get ,41 0it/out drift non-

    stationar *rocessstationar *rocess• 4e call it unit root *ro7lem4e call it unit root *ro7lem

    • The term refers to the root of the polynomial in

    the lag operator • !/us t/e terms non-stationarit random 0al8

    and unit root can 7e treated as snonmous

    U it t

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    Unit root

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    Difference Stationar (DS) Process

    • f the trend of a time series is predictable

    and not variable, e call it deterministic


    • f trend is not predictable, e call it

    stochastic trend

    • Sa =t%7'#7(t#7D=t-'#ut ut 45

    • ;f 7'%7(%6 7D%' ,41 0it/out drift 

    non-stationar 'st difference 


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    !rend Stationar Process

    • f 7'%7 6 7%6 =t%7'#7t#ut

    • !/is is called !S *rocess

    • Though mean is not constant, variance is

    • Ince the values of b4 and b/ is knon, the mean canbe forecast perfectly

    • Thus, if e subtract the mean of Et from Et, theresultant series ill be stationary

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    Dic8-Fuller unit root tests

    • Cimple A.(4) model

    xt%xt-'#ut .. (')

    • The null hypothesis of unit root,

    o %' 0it/ ' G '

    • Cubtracting !t-4 from both sides of equ (4), e get

    xt H xt-' % xt-' H xt-' # ut

    xt-' % (-')xt-'# ut

    xt-' % Ixt-'# ut

    • Jere null hypothesis of unit root

    o I % 6 and ' I G 6

    Detection of Bnit ,oot ADF !ests

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    Detection of Bnit ,oot H ADF !ests

    •  A1 test is conducted ith the folloing model"


    • 5here Et is the underlying variable at time t,

    • ut is the error term

    • The lag terms are introduced in order to Kustify

    that errors are uncorrelated ith lag terms$

    • or the above-specified model the hypothesis,hich ould be of our interest, is"

    6 I % 6

    ADF !ests E ie s

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    ADF !ests-E$ie0s

    • To begin, double click on the series name to openthe series indo, and choose Jie09Bnit ,oot!est

    • Cpecify hether you ish to test for a unit root in

    the level, first difference, or second difference ofthe series

    • @hoose your e!ogenous regressors$

     D Lou can choose to include a constant, a constant andlinear trend, or neither 

    • Mies automatically select lag length, othersuse A@, C6@ and other criteria

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    5ull /*ot/esis of an unit rootcannot 7e reKected

    :t/ B it , t ! t

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    :t/er Bnit ,oot !ests

    • 0hillips-0erron (4''N) tests

    • +3C-detrended 1ickey-uller tests

    • (lliot, .othenberg, and Ctock, 4''O)

    • *iatkoski, 0hillips, Cchmidt, and Chin tests

    • (*0CC, 4''/),

    • lliott, .othenberg, and Ctock 0oint Iptimal

    tests (.C, 4''O)• g and 0erron (0, /::4) unit root tests

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    ;ntegrated Stoc/astic Process

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    ;ntegrated Stoc/astic Process

    • .52 is a specific case of more generalclass of stochastic process knon asintegrated process

    • Irder of integration is the minimum

    number of times the series need to be firstdifferenced to yield a stationary series

    • .52 is non-stationary but 4st difference is


     ;(') series• A stationar series is called ;(6) series

    • 4st difference of (:) series still yields (:)series

    ARIMA Models

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     ARIMA Models

    An integrated process 8t is designedas an A!IMA *p)d)5+) if ta'ingdi%erences of order d) a stationaryprocess 9t of the type A!MA *p) 5+ is


      he ARIMA (p, d, q model is e!pressed "# the f$nction

    t φ

    " t - " #φ

     $ t - $  # %%..#φ

    p t - p  # ut -θ

    " ut & " -θ

    $ u t &$ -%% -θq u t &q

    r φ()* (" & )* d+ t θ()* ut 

    Summar of A,1A9A,;1A modeling

  • 8/20/2019 univariate time series.ppt


    Summar of A,1A9A,;1A modeling*rocedures

    4$ 0erform *reliminar transformations (ifnecessary) to stabili>e variance over time

    . Detrend ;and deseasonaliLeM the data (ifnecessary) to make the stationarityassumption look reasonable

    Trend and seasonality are also characteri>edby A@?s that are sloly decaying and nearlyperiodic, respectively

    The primary methods for achieving this areclassical decomposition, and differencing 

    Summar of A,1A9A,;1A modeling

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    Summar of A,1A9A,;1A modeling


    P$ f the data looks nonstationary ithout a ell-defined trend or seasonality, an alternative tothe above option is to difference successi$el 9 use A1 tests

    N. Examine sam*le ACF & PACF to get an ideaof potential p 9 q values$ or an A.(p)#2A(q),

    the sample 0A@#A@ cuts off after lag p#q

    O. Estimate the coefficients for the promisingmodels

    Summar of A,1A9A,;1A modeling

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    Summar of A,1A9A,;1A modeling*rocedures

    O rom the fitted 23 models above, c/oose t/eone 0it/ smallest A;CC

    = nspection of the standard errors of thecoefficients at the estimation stage, may revealthat some of them are not significant f so, su7set models can be fitted by constraining  

    these to be >ero at a second iteration of 23 estimation

    N @heck the candidate models for goodness-of-fit by e!amining their residuals$ This involves inspecting their A@#0A@ for departures

    from 5, and by carrying out the formal 5hypothesis tests

    S l t f A,;1A d l

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    Seasonal *art of A,;1A model

    • The seasonal part of an A.2A model has the same

    structure as the non-seasonal part" it may have an A.factor, an 2A factor, and#or an order of differencing

    • n the seasonal part of the model, all of these factorsoperate across multiples of lag s (the number of periods in

    a season)•  A seasonal A.2A model is classified as an A.2A(0,1,Q)

    model, here 0&number of seasonal autoregressive(CA.) terms, 1&number of seasonal differences,Q&number of seasonal moving average (C2A) terms

    • n identifying a seasonal model, the first  step is todetermine hether or not a seasonal difference is needed,in addition to or perhaps instead of a non-seasonaldifference

    Seasonal *art of A,;1A model

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    Seasonal *art of A,;1A model

    %   he seasonal models ARIMA (&, ', which are not

    stationar# "$t homogeno$s of degree ' can "e

    e!pressed as

    t  Φ" t - s # Φ$  t - $s  # %%..# Φp t & p s #δ# ut - Θ"ut & s - Θ$ ut &$ s-%.

      Φp ()s* (" & )s* , + t  δ # Θ ()s* ut

    • The signature of pure SAR  or pure SMA behavior issimilar to the signature of pure A. or pure 2A behavior,e!cept that the pattern appears across multiples of lag s

    in the A@ and 0A@

    • or e!ample, a pure CA.(4) process has spikes in the A@ at lags s, /s, Ps, etc$, hile the 0A@ cuts off afterlag s

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  • 8/20/2019 univariate time series.ppt


    Seasonal *art of A,;1A model

    • @onversely, a pure C2A(4) process has

    spikes in the 0A@ at lags s, /s, Ps, etc$,

    hile the A@ cuts off after lag s

    •  An CA. signature usually occurs hen the

    autocorrelation at the seasonal period is

     positiv e, hereas an C2A signatureusually occurs hen the seasonal

    autocorrelation is negative

    enera m0 p ca ve seasonamodels

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    models A!IMA *p) d) 5+ *P) $) ;+s

    n integrated process +t is designed as an /I0 (p,d,q*, if ta1ingdifferences of order d, a stationary process t of the type /0 (p, q*is obtained.

      he ARIMA (p, d, q model is e!pressed "# the f$nction

    t  φ" t - " # φ $ t - $ # %%..# φp t - p  # ut - θ" ut & " - θ$ u t &$ - %% - θq u t &q

    ()* (" & )* d+ t θ

    ()* ut 

      he seasonal models ARIMA (&, ', which are not stationar#

    "$t homogeno$s of degree ' can "e e!pressed as

    t  Φ" t - s # Φ$  t - $s  # %%..# Φp t & p s #δ# ut - Θ"ut & s - Θ$ ut &$ s- %.  r Φp ()s* (" & )s* + t  δ # Θ ()s* ut

    )eneral m$ltiplicati*e seasonal models, ARIMA (p, d, q (&, ',


    Φp ()s* φp ()*(" & )s* (" & )* d + t Θ ()s* θq ()* ut. 

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  • 8/20/2019 univariate time series.ppt


    ARIMA Model B$ilding

    RIMA Model B$ilding



    This stage 'asi!ally tries to ientify anThis stage 'asi!ally tries to ientify ana##ro#riate ARIMA moel for the unerlyinga##ro#riate ARIMA moel for the unerlyingstationary time series on the 'asis of AC) anstationary time series on the 'asis of AC) an%AC)%AC)

      If the series is nonstationary it is firstIf the series is nonstationary it is first

    transforme to !ovarian!e-stationary an thentransforme to !ovarian!e-stationary an thenone !an easily ientify the #ossi'le values ofone !an easily ientify the #ossi'le values of

    the regular #art of the moel i(e(the regular #art of the moel i(e(autoregressive orer # an moving average orerautoregressive orer # an moving average orer8 in a univariate ARMA moel along /ith the8 in a univariate ARMA moel along /ith theseasonal #artseasonal #art 

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    ARIMA Model B$ilding

    RIMA Model B$ilding



    %oint estimates of the !oeffi!ients !an 'e%oint estimates of the !oeffi!ients !an 'e

    o'taine 'y the metho of ma+imum li"elihooo'taine 'y the metho of ma+imum li"elihoo

    Asso!iate stanar errors are also #rovie,Asso!iate stanar errors are also #rovie,

    suggesting /hi!h !oeffi!ients !oul 'e ro##esuggesting /hi!h !oeffi!ients !oul 'e ro##e

    %'iagnostic checking

    iagnostic checking

    ne shoul also e+amine /hether the resiuesne shoul also e+amine /hether the resiues

    of the moel a##ear to 'e /hite noise #ro!essof the moel a##ear to 'e /hite noise #ro!ess 



  • 8/20/2019 univariate time series.ppt