Data-driven local polynomial for the trend and its derivatives in economic time series

Feng, Yuanhua; Gries, Thomas; Fritz, Marlon

doi:10.1080/10485252.2020.1759598

Cited by 10 publications

(14 citation statements)

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“…In addition, Loader () criticizes that the more favourable plug‐in (PI) methods depend on an arbitrary selection of the pilot bandwidth. However, our IPI is a fixpoint search procedure and consequently the estimated bandwidth is not affected by the pilot bandwidth if chosen from a suitable range (Feng and Gries, ). Furthermore, Heidenreich, Schindler and Sperlich () state that PI methods work poorly for small sample sizes, which is refuted in the next subsections.…”

Section: Methodsmentioning

confidence: 99%

Growth Trends and Systematic Patterns of Booms and Busts‐Testing 200 Years of Business Cycle Dynamics

2018

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We study the dynamic pattern of business cycles using US GDP data between 1790 and 2015. To address difficulties in trend and cycle decomposition, we introduce a semiparametric estimation approach with an iterative plug‐in (IPI) algorithm for endogenous bandwidth selection. This algorithm identifies continuously moving growth trends with trend‐supporting growth periods. A simulation study demonstrates the value‐added of our trend identification. Afterwards, nonlinear SETAR models are fitted parametrically. Further, we test the trend using a recently developed test and the estimated SETAR models against their linear alternatives. The results indicate asymmetric characteristics during booms and busts.

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Section: Methodsmentioning

confidence: 99%

Growth Trends and Systematic Patterns of Booms and Busts‐Testing 200 Years of Business Cycle Dynamics

2018

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“…In the introduction, the HP filter is criticized for its suboptimality at boundary points. The LLR has automatic boundary correction [15], ensuring that asymptotic properties of the estimators in the interior still hold at boundary points. We focus on the estimation quality at these points and use an asymmetric boundary kernel to obtain stable boundary estimates, which are the key to obtaining reliable real-time output gap estimates.…”

Section: Local Linear Regressionmentioning

confidence: 99%

“…The values c b and d b can be chosen to select the bandwidth using only observations between these bounds. Details of the data-driven IPI are described in [15]. To address the criticism of [12,16], an asymmetric boundary kernel is used to weight the boundary points and the bandwidth at the boundary is kept constant such that the asymptotic properties at the boundary are the same as in the interior [17].…”

Section: Local Linear Regressionmentioning

confidence: 99%

Improved Output Gap Estimates and Forecasts Using a Local Linear Regression

Fritz

2021

The 7th International Conference on Time Series and Forecasting

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The output gap, the difference between potential and actual output, has a direct impact on policy decisions, e.g., monetary policy. Estimating this gap and its further analysis remain the subject of controversial debates due to methodological problems. We propose a local polynomial regression combined with a Self-Exciting Threshold AutoRegressive (SETAR) model and its forecasting extension for a systematic output gap estimation. Furthermore, local polynomial regression is proposed for the (multivariate) OECD production function approach and its reliability is demonstrated in forecasting output growth. A comparison of the proposed gap to the Hodrick–Prescott filter as well as to estimations by experts from the FED and OECD shows a higher correlation of our output gap with those from those economic institutions. Furthermore, sometimes gaps with different magnitude and different positions above or below the trend are observed between different methods. This may cause competing policy implications which can be improved with our results.

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“…A2 is made for simplicity and is usually practically relevant. A3 is much stronger than the absolute summability and is required by the application of the nonparametric data-driven lag-window estimator of the factor in the variance of the sample mean of a stationary time series (see Bühlmann (1996) as well as Feng., Gries, and Fritz (2019) Under A3, and can be estimated using the nonparametric data-driven lag-window estimator proposed by Bühlmann (1996). Feng.…”

mentioning

confidence: 99%

“…A3 is much stronger than the absolute summability and is required by the application of the nonparametric data-driven lag-window estimator of the factor in the variance of the sample mean of a stationary time series (see Bühlmann (1996) as well as Feng., Gries, and Fritz (2019) Under A3, and can be estimated using the nonparametric data-driven lag-window estimator proposed by Bühlmann (1996). Feng. et al (2019) adjusted his idea slightly and developed an R function for the practical implementation of this approach, which will be applied in the following to test the possible structural break in the volatility of HSI and SSE caused by the Shanghai-Hong Kong stock connect on November 17, 2014.…”

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confidence: 99%

The Shanghai- Hong Kong Stock Connect: An Application of the Semi-CGARCH and Semi-EGARCH

Peitz

Feng

Gilroy

et al. 2020

Asian Economic and Financial Review

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This paper examines the impact on volatility on the Shanghai Stock Exchange and the Hong Kong Stock Exchange before and after the connection on November 17, 2014. We test whether this event led to a structural break. For this purpose, the volatility series are shown and analysed in more detail using two new models. We test both semiparametric GARCH extensions, the Semi-EGARCH based on the EGARCH model (Nelson, 1991) and the Semi-CGARCH model, based on the CGARCH (Engle & Lee, 1999). Both univariate models work with a constrained local linear estimator for the scale function and a fully data-driven algorithm developed under weak moment conditions. The proposed method is applied to the two major Asian financial indices and to stocks from the banking sector. Furthermore, our focus is to improve the quantitative risk management analysis. When a parametric GARCH model provides satisfactory results for the calculation of e.g the Value at Risk (VaR), semiparametric can also be applied to improve the quality of measurement. This article compares the results of various parametric and semiparametric approaches regarding the VaR to show how the extensions increase the performance. Contribution/ Originality: This study contributes to existing literature by examining the impact on volatility on the Shanghai Stock Exchange and the Hong Kong Stock Exchange before and after the connection on

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Data-driven local polynomial for the trend and its derivatives in economic time series

Cited by 10 publications

References 50 publications

Growth Trends and Systematic Patterns of Booms and Busts‐Testing 200 Years of Business Cycle Dynamics

Growth Trends and Systematic Patterns of Booms and Busts‐Testing 200 Years of Business Cycle Dynamics

Improved Output Gap Estimates and Forecasts Using a Local Linear Regression

The Shanghai- Hong Kong Stock Connect: An Application of the Semi-CGARCH and Semi-EGARCH

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