Market timing and return prediction under model instability

Pesaran, M. Hashem; Timmermann, Allan

doi:10.1016/s0927-5398(02)00007-5

Cited by 262 publications

(116 citation statements)

References 24 publications

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“…. 7 As documented in the time series literature, e.g., Pesaran and Timmermann (2002), structural breaks could lead to forecast failure. Watson (2002, 2009) show that forecasts constructed using dynamic factor models are robust to small structural breaks of factor loadings.…”

Section: Heterogeneous Panels With a Common Structural Breakmentioning

confidence: 99%

Estimation of heterogeneous panels with structural breaks

Baltagi

Feng

Kao

2016

Journal of Econometrics

112

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This paper extends Pesaran's (2006) work on common correlated e¤ects (CCE) estimators for large heterogeneous panels with a general multifactor error structure by allowing for unknown common structural breaks. Structural breaks due to new policy implementation or major technological shocks, are more likely to occur over a longer time span. Consequently, ignoring structural breaks may lead to inconsistent estimation and invalid inference. We propose a general framework that includes heterogeneous panel data models and structural break models as special cases. The least squares method proposed by Bai (1997aBai ( , 2010 is applied to estimate the common change points, and the consistency of the estimated change points is established. We …nd that the CCE estimator have the same asymptotic distribution as if the true change points were known. Additionally, Monte Carlo simulations are used to verify the main results of this paper.

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Section: Heterogeneous Panels With a Common Structural Breakmentioning

confidence: 99%

Estimation of heterogeneous panels with structural breaks

Baltagi

Feng

Kao

2016

Journal of Econometrics

112

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“…Because using all images may have affected the BFAST Monitor model fit [32], we measured the reversed-ordered-cumulative (ROC) sum of residuals to generate a cumulative prediction error that helps identify whether a seasonal model no longer offers an accurate fit [59] ( Figure 3). With the historical model in place, we applied BFAST Monitor sequentially from 2001 to 2015, following the approach by DeVries at al.…”

Section: Landsat-based Disturbance Detection With Bfast Monitormentioning

confidence: 99%

Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes

2017

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Abstract:The spatial distribution of disturbances in Andean tropical forests and protected areas has commonly been calculated using bi or tri-temporal analysis because of persistent cloud cover and complex topography. Long-term trends of vegetative decline (browning) or improvement (greening) have thus not been evaluated despite their importance for assessing conservation strategy implementation in regions where field-based monitoring by environmental authorities is limited. Using Colombia's Cordillera de los Picachos National Natural Park as a case study, we provide a temporally rigorous assessment of regional vegetation change from 2001-2015 with two remote sensing-based approaches using the Breaks For Additive Season and Trend (BFAST) algorithm. First, we measured long-term vegetation trends using a Moderate Resolution Imaging Spectroradiometer (MODIS)-based Multi-Angle Implementation of Atmospheric Correction (MAIAC) time series, and, second, we mapped short-term disturbances using all available Landsat images. MAIAC-derived trends indicate a net greening in 6% of the park, but in the surrounding 10 km area outside of the park, a net browning trend prevails at 2.5%. We also identified a 12,500 ha area within Picachos (4% of the park's total area) that has shown at least 13 years of consecutive browning, a result that was corroborated with our Landsat-based approach that recorded a 12,642 ha (±1440 ha) area of disturbed forest within the park. Landsat vegetation disturbance results had user's and producer's accuracies of 0.95 ± 0.02 and 0.83 ± 0.18, respectively, and 75% of Landsat-detected dates of disturbance events were accurate within ±6 months. This study provides new insights into the contribution of short-term disturbance to long-term trends of vegetation change, and offers an unprecedented perspective on the distribution of small-scale disturbances over a 15-year period in one of the most inaccessible national parks in the Andes.

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“…Because structural changes over a long sample period are a concern in finance, rolling window forecasting is widely used to account for SBU (see Fama and MacBeth, 1973;Pesaran and Timmermann, 2002). However, this method is pertinent for addressing small and frequent breaks without relying on estimates of break dates and sizes.…”

Section: Rolling Windowmentioning

confidence: 99%

Improving equity premium forecasts by incorporating structural break uncertainty

Tian

Zhou

2016

Accounting & Finance

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This paper compares five alternative methods for directly dealing with structural break uncertainty in forecasting the U.S. equity premium using 30 widely used bivariate and multivariate predictive regressions. We find that two recently developed methods -Robust Optimal Weights on Observations and Forecast Combination across Estimation Windows -outperform the conventional rolling window and post-break estimation methods. This result indicates that very early historical information is beneficial for U.S. equity premium forecasting but should be discounted to incorporate structural break uncertainty.

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Market timing and return prediction under model instability

Cited by 262 publications

References 24 publications

Estimation of heterogeneous panels with structural breaks

Estimation of heterogeneous panels with structural breaks

Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes

Improving equity premium forecasts by incorporating structural break uncertainty

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