Bootstrapping GMM estimators for time series

By A. Inoue, Mototsugu Shintani. 2003

101 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d773d3af2a353b26328a71d8f66fc5eecff77af8

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In Economics and Econometrics

Citations in the corpus

Higher‐Order Improvements of a Computationally Attractive k‐Step Bootstrap for Extremum Estimators (2002, background): “The former is often called the Künsch (1989) blocking scheme, and the latter, the Carlstein (1986) scheme, although Hall (1985) considers both of these schemes in a related context.”
GMM, GEL, Serial Correlation, and Asymptotic Bias (2005, unknown): “In particular, it follows from Lemmas A1—A4 in Inoue and Shintani (2003) that the leading asymptotically vanishing term in the Edgeworth expansion for recentered and normalized GMM estimators has the order T−1/2 unaffected by HAC estimation, while the order of the next term depends both on the…”
Generalized Method of Moments and Macroeconomics (2002, methodology): “Inoue and Shintani (2001) further showed that for symmetric con dence intervals and the J test, asymptotic re nements essentially require use of the truncated kernel.”
BLOCK BOOTSTRAP HAC ROBUST TESTS: THE SOPHISTICATION OF THE NAIVE BOOTSTRAP (2011, methodology): “Recent work by Inoue and Shintani (2006) extends the Edgeworth analysis to certain testing problems in the GMM framework.”
HIGHER-ORDER ACCURATE, POSITIVE SEMIDEFINITE ESTIMATION OF LARGE-SAMPLE COVARIANCE AND SPECTRAL DENSITY MATRICES (2005, background): “This type of fast-converging albeit positive definite spectral (or HAC covariance) matrix can find immediate application in set-ups like the studentized bootstrap; see Lahiri (2003), or Inoue and Shintani (2006). direction and may have a partially off-setting effect.”
Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes (2007, result): “Clark and McCracken (2001) and Inoue and Kilian (2004)). However, it should be noted that application of different sampling strategies (e.g. the use of rolling windows), and examination of more prediction periods does lead to some rejection using the F-test, in accord with the results of Clark and M”
How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation (2005, unknown): “…compute the variance of √ T bβ∗as bH∗−1 bV ∗ bH∗−1 where bV ∗ = 1 bm bX k=1 mX i=1 mX j=1 (x∗(k−1)m+iε ∗ (k−1)m+i+h)(x ∗ (k−1)m+jε ∗ (k−1)m+j+h) 0, bH∗ = 1 bm bX k=1 mX i=1 (x∗(k−1)m+ix ∗0 (k−1)m+i), ε∗t+h = y ∗ t+h − bβ∗0x∗t , and b is the integer part of T/m (see, e.g., Inoue and Shintani 2003).”
Bootstrap Conditional Distribution Tests In the Presence of Dynamic Misspecification (2006, unknown): “10The main difference between the block bootstrap and the stationary bootstrap of Politis and Romano (PR: 1994a) is that the former uses a deterministic block length, which may be either overlapping as in Künsch (1989) or non-overlapping as in Carlstein (1986), while the latter resamples using block”