By Wai Keung Li, David A. Stanford, Hao Yu

This quantity reports and summarizes a few of A. I. McLeod's major contributions to time sequence research. It additionally comprises unique contributions to the sphere and to similar parts by means of contributors of the festschrift held in June 2014 and associates of Dr. McLeod. protecting a various diversity of cutting-edge subject matters, this quantity good balances utilized and theoretical examine throughout fourteen contributions through specialists within the box. it is going to be of curiosity to researchers and practitioners in time sequence, econometricians, and graduate scholars in time sequence or econometrics, in addition to environmental statisticians, info scientists, statisticians drawn to graphical versions, and researchers in quantitative danger management.

**Read or Download Advances in Time Series Methods and Applications : The A. Ian McLeod Festschrift PDF**

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T and fixed h da L Output: The daLASSO estimator Φ T 1 Start: Set up a grid G = γ0 × γ1 × γ2 with G = |G |. 2 for g ← 1 to G do 5 hK2 j=1 Output End da L ΦT ← : ∀g = 1, . . , G . Φ T (Λ∗T ). 6 Monte Carlo Study We use Monte Carlo to investigate the sampling properties of the PLAC-weighted adaptive LASSO estimator for VAR models. Specifically, we would like to assess its performance in terms of order identification, the parameter estimation, and subset The Doubly Adaptive LASSO for Vector Autoregressive Models 33 selection.

Chan, K. (2011). Subset ARMA selection via the adaptive Lasso. Statistics and Its Interface, 4, 197–205. 8. Donoho, D. , & Temlyakov, V. N. (2006). Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Transactions on Iinformation Theory, 52(1), 6–18. 9. Durbin, J. (1960). The fitting of time series models. Review of the Institute of International Statistics, 28, 233–244. 10. , & Tibshirani, R. (2004). Least angle regression. Annals of Statistics, 32(2), 407–499.

For an m × n matrix A, its entrywise p-norm, denoted as A A p = vec(A) p = m n i=1 j=1 |ai j | p 1/ p p, is defined as . The Frobenius norm, which is the special case p = 2, is defined as A F = m n i=1 j=1 |ai j |2 Definition 3 (The PLAC-weighted adaptive LASSO) The PLAC-weighted adaptive da L LASSO or doubly adaptive LASSO (daLASSO) estimator φˆ T for φ ∗ is defined as φˆ da L = arg min φ ⎧ ⎨ ⎩ h y − X ⊗ IK φ 2 K K wˆ i j,k φi j,k + λT k=1 i=1 j=1 ⎫ ⎬ ⎭ , (14) The Doubly Adaptive LASSO for Vector Autoregressive Models where wˆ i j,k = 1 φ˜ i j,k γ1 h P(s) s=k γ0 γ0 h Ak = P(s) γ2 = γ0 γ0 , 27 1 φ˜ i j,k γ1 γ Ak 2 , (15) (16) s=k φ˜ i j,k is the ordinary least squares estimate or any other consistent estimate for φi j,k , P(s) γ0 = K i=1 K j=1 | Pi j (s)|γ0 1/γ0 is the entrywise γ0 -norm of the sample par- tial lag autocorrelation matrix P(s) at lag s, and γ0 > 0, γ1 ≥ 0, and γ2 ≥ 0 are some fixed constants, and h is the fixed maximum lag set initially.