A Parametric Approach to Flexible Nonlinear Inference

By James D. Hamilton. 2001

219 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d352229a09610ef36f6931c27a9416c654fd4022

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In Economics and Econometrics

Citations in the corpus

Environmental Kuznets Curve for CO2 in Canada (2010, methodology): “An alternative approach proposed by Hamilton (2001) consists of considering the function µ(yt) as the realisation of a stochastic process called a random field and to use the observed data to form inferences about what this realisation might be. This fully parametric approach allows one to avoid s”
Semiparametric inference in generalized mixed effects models (2008, background): “Similar ideas appear in random-fields theory; see Hamilton (2001).”
Testing Linearity in Cointegrating Relations With an Application to Purchasing Power Parity (2010, unknown): “From (21) and (22), it follows that the modified RESET statistic is a quadratic form with a limiting χ2 distribution as { ûF ·Dn − E n − S n }( Ω̂uu.”
Threshold cointegration and nonlinear adjustment between CO2 and income: The Environmental Kuznets Curve in Spain, 1857–2007 (2012, methodology): “sults for the case of Canada using the Hamilton's (2001) model.”
Oil Price Shocks and the Macroeconomy: What Has Been Learned Since 1996 (2004, methodology): “Lee, Ni, and Ratti (1995; LNR) experimented with an alternative scaling of the oil price change that divided the change in each period by an index of the recent volatility of the oil price, a specification that Hamilton (2001) subsequently showed to be capable of yielding a stable oil price-GDP re”
Testing for neglected nonlinearity in regression models based on the theory of random fields (2003, methodology): “The specification suggested by Hamilton (2001) can be represented as”
Smooth Transition from Fixed Effects to Mixed Effects Models in Multi-level regression Models (2008, background): “More recently now, they have entered the nonparametric world, see Ruppert, Wand and Carroll (2003), Wand (2003), Hamilton (2001), Opsomer, Claeskens, Ranalli, Kauermann and Breidt (2005), Tutz (2001), and Tutz and Reithinger (2007).”
Bayesian nonparametric vector autoregressive models (2018, unknown): “There have been several main criticisms of VAR models. Their rich parameterisation is attached to the risk of overfitting the data leading to imprecise inference, while the interdependence of the innovations ( t’s) leads to identification issues. Often, the linearity, stationarity, Gaussian innovati”