Structural change estimation in time series regressions with endogenous variables

Qian, Junhui; Su, Liangjun

doi:10.1016/j.econlet.2014.10.021

Cited by 11 publications

(11 citation statements)

References 15 publications

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“…Part (iii), a by-product of our proof, shows that one imposes more changes than the truth, then our method estimates all the true change points with probability one in the limit (and some additional spurious change points). 6 The intuition for the result in Theorem 1 is similar to Qian and Su (2014) and Perron and Yamamoto (2015): Whilêis in general not consistent for 0 because of the omitted variable bias, it is consistent for the pseudo-true value 0 ; therefore, we can consistently estimate the number and location of change points in 0 . The advantage of our method over Qian and Su (2016) and Li et al (2016) is that we allow for time-varying individual effects without specifying a functional form for the time variation.…”

Section: Change Point Estimationmentioning

confidence: 95%

“…Besides the long line of work on change points in time series models (see, among others, Aue & Horváth, 2013;Bai & Perron, 1998;Chan, Yau, & Zhang, 2014;Csörgö & Horváth, 1997;Harchaoui & Lévy-Leduc, 2010;Perron & Yamamoto, 2015;Qian & Su, 2014, 2016Qu & Perron, 2007), there is a growing body of work on change points in panel data. In panel data models, coefficients may exhibit common changes across customers, firms, or countries due, for example, to policy changes, financial crises, housing bubbles, or technological breakthroughs.…”

Section: Introductionmentioning

confidence: 99%

“…There are two related papers estimating change points in time series models with endogenous regressors: Qian and Su (2014) and Perron and Yamamoto (2015). Qian and Su propose a penalized least squares criterion to estimate the number and location of change points and prove that the estimated number of change points and the estimated change fractions (change points divided over the sample size) are consistent.…”

Section: Introductionmentioning

confidence: 99%

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Change point estimation in panel data with time‐varying individual effects

Boldea

Drepper²,

Gan

2020

J of Applied Econometrics

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This paper proposes a method for estimating multiple change points in panel data models with unobserved individual effects via ordinary least-squares (OLS). Typically, in this setting, the OLS slope estimators are inconsistent due to the unobserved individual effects bias. As a consequence, existing methods remove the individual effects before change point estimation through data transformations such as first-differencing. We prove that under reasonable assumptions, the unobserved individual effects bias has no impact on the consistent estimation of change points. Our simulations show that since our method does not remove any variation in the dataset before change point estimation, it performs better in small samples compared to first-differencing methods. We focus on short panels because they are commonly used in practice, and allow for the unobserved individual effects to vary over time. Our method is illustrated via two applications: the environmental Kuznets curve and the U.S. house price expectations after the financial crisis. * We are grateful to

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Section: Change Point Estimationmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Change point estimation in panel data with time‐varying individual effects

Boldea

Drepper²,

Gan

2020

J of Applied Econometrics

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“…For a general review see Perron (2006). The problem of structural breaks has also been addressed using shrinkage methods: In an autoregressive setting Chan, Yau, and Zhang (2014) uses the group Lasso to estimate clusters of parameters with identical values over time, and Qian and Su (2014) considers the problem of estimating time series models with endogenous regressors and an unknown number of breaks using the group fused Lasso.…”

Section: Introductionmentioning

confidence: 99%

Vector Autoregressions with Parsimoniously Time Varying Parameters and an Application to Monetary Policy

Callot

Kristensen²

2014

SSRN Journal

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This paper studies vector autoregressive models with parsimoniously time-varying parameters. The parameters are assumed to follow parsimonious random walks, where parsimony stems from the assumption that increments to the parameters have a non-zero probability of being exactly equal to zero. We estimate the sparse and high-dimensional vector of changes to the parameters with the Lasso and the adaptive Lasso. The parsimonious random walk allows the parameters to be modelled non parametrically, so that our model can accommodate constant parameters, an unknown number of structural breaks, or parameters varying randomly. We characterize the finite sample properties of the Lasso by deriving upper bounds on the estimation and prediction errors that are valid with high probability, and provide asymptotic conditions under which these bounds tend to zero with probability tending to one. We also provide conditions under which the adaptive Lasso is able to achieve perfect model selection. We investigate by simulations the properties of the Lasso and the adaptive Lasso in settings where the parameters are stable, experience structural breaks, or follow a parsimonious random walk. We use our model to investigate the monetary policy response to inflation and business cycle fluctuations in the US by estimating a parsimoniously time varying parameter Taylor rule. We document substantial changes in the policy response of the Fed in the 1970s and 1980s, and since 2007, but also document the stability of this response in the rest of the sample.

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“…Davis et al (2006) proposed a minimum description length principle to locate the change-points in AR models with multiple structural changes. In an autoregressive setting, Chan et al (2014) used the group Lasso to estimate clusters of parameters with identical values over time and Qian and Su (2014) considered the problems of estimation in time series with endogenous regressors and an unknown number of breaks using the group fused Lasso. Ling (2016) developed an asymptotic theory for the change-point in linear and nonlinear time series models.…”

Section: Introductionmentioning

confidence: 99%

Statistical Inference for Structurally Changed Threshold Autoregressive Models

Gao¹,

Ling²

2019

STAT SINICA

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In this paper, we study the theory and methodology for statistical inferences of threshold and change-point in the threshold autoregressive models. It is shown that the least squares estimators (LSE) of the threshold and change-point are n-consistent, and converge weakly to the minimizer of a compound Poisson process and the location of minima of a two-sided random walk, respectively.When the magnitude of change in the parameters of the state regimes or the time horizon is small, it is further shown that these limiting distributions can be approximated by a class of known distributions. The LSE of slope parameters are √ n-consistent and asymptotically normal. Furthermore, a likelihood-ratio based confidence set is given for the threshold and change-point, respectively. Simulation study is carried out to assess the performance of our procedure. The proposed theory and methodology are further illustrated by a tree-ring dataset.

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Structural change estimation in time series regressions with endogenous variables

Cited by 11 publications

References 15 publications

Change point estimation in panel data with time‐varying individual effects

Change point estimation in panel data with time‐varying individual effects

Vector Autoregressions with Parsimoniously Time Varying Parameters and an Application to Monetary Policy

Statistical Inference for Structurally Changed Threshold Autoregressive Models

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