. . Time Series: Lecture 5 Giacomo Livan [email protected] March 24, 2015 Giacomo Livan Time Series: Lecture 5 Recap of lecture 4 Yule-Walker estimators (3.30)-(3.31) Method of moments for AR(1) and AR(2) models Method of moments for MA(1) model (example 3.19) Least squares estimation for AR(1) and AR(2) models Forecasting (3.45)-(3.49) Best linear prediction for AR(2) models (example 3.24) Giacomo Livan Time Series: Lecture 5 Spectral analysis Spectral analysis: study of time series in the frequency domain Discrete Fourier transform of a time series X1 , . . . , Xn n 1 ∑ d(ωj ) = √ Xt e−2πiωj t , n t=1 where ωj = j/n (j = −(n − 1), . . . , 0, . . . , n − 1) are known as fundamental frequencies The Fourier transform “breaks” a signal (a time series in this case) into basic sinusoids with frequencies ωj . The inverse transform reads: n−1 1 ∑ Xt = √ d(ωj ) e2πiωj t n j=0 Reminder: given a sinusoid A cos(2πωt + ϕ) we have the following A: amplitude ω: frequency (1/ω: period) ϕ: phase Giacomo Livan Time Series: Lecture 5 Periodogram Definition: I(ωj ) = |d(ωj )|2 = 1 n = 1 n ( ( n ∑ )2 Xt cos (2πωj t) t=1 + 1 n ( n ∑ )2 Xt sin (2πωj t) t=1 ( n ( ))2 ( ))2 j 1 ∑ j Xt cos 2π t Xt sin 2π t + n n t=1 n t=1 n ∑ Symmetry around 0: I(ωj ) = I(−ωj ) Symmetry around 1/2 (folding frequency) when sampling time and natural time coincide: I(ωj ) = I(1 − ωj ) Rescaled periodogram: P (ωj ) = 4/nI(ωj ) = 4/n|d(ωj )|2 For each sinusoid in the original signal (time series) the periodogram picks up the corresponding amplitude Giacomo Livan Time Series: Lecture 5 Periodogram: example 1 Time series: Xt = A cos(2πωt) + ϵt , where ϵt is Gaussian white noise with variance σ 2 Time series 2 1.5 1 X(t) 0.5 0 -0.5 -1 -1.5 -2 1 2 3 4 5 6 7 8 9 10 0.8 1 t Periodogram 60 50 I(ω) 40 30 20 10 0 -1 -0.8 -0.6 -0.4 -0.2 0 ω 0.2 0.4 0.6 In this example: A = 1, ω = 0.3, σ = 0.5 Giacomo Livan Time Series: Lecture 5 Periodogram: example 2 Time series: Xt = A1 cos(2πω1 t) + A2 cos(2πω2 t) + ϵt , where ϵt is Gaussian white noise with variance σ 2 Time series 2.5 2 1.5 1 X(t) 0.5 0 -0.5 -1 -1.5 -2 -2.5 1 2 3 4 5 t 6 7 8 9 10 0.9 1 Periodogram 60 50 I(ω) 40 30 20 10 0 0 0.1 0.2 0.3 0.4 0.5 ω 0.6 0.7 0.8 In this example: A1 = 0.75, A2 = 1, ω1 = 0.3, ω2 = 0.7, σ = 0.5 Giacomo Livan Time Series: Lecture 5 Spectral representation . Lemma . The autocovariance function γ(h) of an ARMA(p, q) process satisfies the absolute summability condition: ∞ ∑ |γ(h)| < ∞ h=−∞ . . Spectral representation theorem . If the autocovariance function of a stationary process satisfies the absolute summability condition, then it can be represented as ∫ 1/2 γ(h) = h = 0, ±1, ±2, . . . , e2πiωh f (ω)dω −1/2 where f (ω) is the spectral density f (ω) = ∞ ∑ γ(h) e−2πiωh − 1/2 ≤ ω ≤ 1/2 h=−∞ From its definition it is easy to verify that the spectral density is even (f . (ω) = f (−ω)), so that it is enough to study it in [0, 1/2] Giacomo Livan Time Series: Lecture 5 Spectral representation 2 . Propositions . The periodogram I(ωj ) is an estimator of the spectral density f (ω) I(ω) = fˆ(ω) . Increasing n does not guarantee that estimates will improve Example 1: white noise { γ(h) = σ2 0 for h = 0 for h = ̸ 0 =⇒ f (ω) = σ 2 Example 2: ARMA(p, q), Φ(B)Xt = Θ(B)ϵt f (ω) = σ 2 Giacomo Livan |Θ(e−2πiω )|2 |Φ(e−2πiω )|2 Time Series: Lecture 5 Least squares estimates of Fourier coefficients From Fourier analysis we know that any time series can be written as n−1 1 ∑ d(ωj ) e2πiωj t Xt = √ n j=0 Taking into account the symmetry of the periodogram around 1/2 we can rewrite the above (for n odd) as ∑( n−1 2 Xt = β1 + j=1 ( ) ( )) j j aj cos 2π t + bj sin 2π t n n Least squares estimation of the parameters yields (recall problem set 2) a ˆj = ˆbj = ) ( n 2∑ j Xt cos 2π t n t=1 n ( ) n j 2∑ Xt sin 2π t n t=1 n The rescaled periodogram reads: P (ωj ) = a2j + b2j Giacomo Livan Time Series: Lecture 5 Recap Discrete Fourier transform and its inverse Periodic processes / sinudoids (4.1) Periodogram, rescaled periodogram, least squares estimation of Fourier coefficients (example 4.2) Spectral density and spectral representation (4.12)-(4.14) Spectral density of white noise (example 4.4) and ARMA processes (property 4.2) Giacomo Livan Time Series: Lecture 5
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