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Yushu Li
Department of Mathematics, University of Bergen, Bergen, Norway

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Article
Published: 22 February 2020 in Empirical Economics
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Hong and Kao (2004) proposed a class of general applicable wavelet-based tests for serial correlation of unknown form in the residuals from a panel regression model. The tests can be applied to both static and dynamic panel models. Their test, however, is computationally difficult to implement, and simulation studies show that the test has poor small-sample properties. In this paper, we extend Gençay’s (2010) time-series test for serial correlation to panel data case. Our new test is also wavelet based and maintains the advantages of the Hong and Kao (2004) test, but it is much simpler and easier to implement. Furthermore, simulation results show that our test has quicker convergence rate and hence better small-sample properties, compared to Hong and Kao (2004) test. We also compare our test with several other existing tests for series correlation, and our test has in general better statistical properties in terms of both size and power.

ACS Style

Yushu Li; Fredrik N. G. Andersson. A simple wavelet-based test for serial correlation in panel data models. Empirical Economics 2020, 60, 2351 -2363.

AMA Style

Yushu Li, Fredrik N. G. Andersson. A simple wavelet-based test for serial correlation in panel data models. Empirical Economics. 2020; 60 (5):2351-2363.

Chicago/Turabian Style

Yushu Li; Fredrik N. G. Andersson. 2020. "A simple wavelet-based test for serial correlation in panel data models." Empirical Economics 60, no. 5: 2351-2363.

Preprint
Published: 20 August 2019
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This paper utilizes the hierarchical model structure from the Bayesian Lasso in the Sparse Bayesian Learning process to develop a new type of probabilistic supervised learning approach. This approach has several performance advantages, such as being fast, sparse and especially robust to the variance in random noise. The hierarchical model structure in this Bayesian framework is designed in such a way that the priors do not only penalize the unnecessary complexity of the model but also depend on the variance of the random noise in the data. The hyperparameters in the model are estimated by the Fast Marginal Likelihood Maximization algorithm and can achieve low computational cost and faster learning process. We compare our methodology with two other popular Sparse Bayesian Learning models: The Relevance Vector Machine and a sparse Bayesian model that has been used for signal reconstruction in compressive sensing. We show that our method will generally provide more sparse solutions and be more flexible and stable when data is polluted by high variance noise.

ACS Style

Ingvild M. Helgøy; Yushu Li. A Noise-Robust Fast Sparse Bayesian Learning Model. 2019, 1 .

AMA Style

Ingvild M. Helgøy, Yushu Li. A Noise-Robust Fast Sparse Bayesian Learning Model. . 2019; ():1.

Chicago/Turabian Style

Ingvild M. Helgøy; Yushu Li. 2019. "A Noise-Robust Fast Sparse Bayesian Learning Model." , no. : 1.

Research article
Published: 17 May 2019 in Journal of Forecasting
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In this paper, we propose a likelihood ratio based method to evaluate density forecasts which can jointly evaluate the unconditional forecasted distribution and dependence of the outcomes. Unlike the well‐known Berkowitz test, the proposed method does not requires a parametric specification of time dynamics. We compare our method with the method proposed by several other tests and show that our methodology has very high power against both dependence and incorrect forecasting distributions. Moreover, the loss of power, caused by the non‐parametric nature of the specification of the dynamics, is shown to be small compared to Berkowitz test, even when the parametric form of dynamics is correctly specified in the latter method.

ACS Style

Yushu Li; Jonas Andersson. A likelihood ratio and Markov chain‐based method to evaluate density forecasting. Journal of Forecasting 2019, 39, 47 -55.

AMA Style

Yushu Li, Jonas Andersson. A likelihood ratio and Markov chain‐based method to evaluate density forecasting. Journal of Forecasting. 2019; 39 (1):47-55.

Chicago/Turabian Style

Yushu Li; Jonas Andersson. 2019. "A likelihood ratio and Markov chain‐based method to evaluate density forecasting." Journal of Forecasting 39, no. 1: 47-55.

Journal article
Published: 07 August 2018 in Sustainability
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This paper applies wavelet multi-resolution analysis (MRA), combined with two types of causality tests, to investigate causal relationships between three variables: real oil price, real interest rate, and unemployment in Norway. Impulse response functions were also utilised to examine effects of innovation in one variable on the other variables. We found that causal relations between the variables tend to be stronger as the wavelet time scale increases; specifically, there were no causal relationships between the variables at the lowest time scales of one to three months. A causal relationship between unemployment rate and interest rate was observed during the period of two quarters to two years, during which time a feedback mechanism was also detected between unemployment and interest rate. Causal relationships between oil price and both interest rate and unemployment were observed at the longest time scale of eight quarters. In conjunction with Granger causality analysis, impulse response functions showed that unemployment rates in Norway respond negatively to oil price shocks around two years after the shocks occur. As an oil exporting country, increases (or decreases) in oil prices reduce (or increase) unemployment in Norway under a time horizon of about two years; previous studies focused on oil importing economies have generally found the inverse to be true. Unlike most studies in this field, we decomposed the implicit aggregation for all time scales by applying MRA with a focus on the Norwegian economy. Thus, one main contribution of this paper is that we unveil and systematically distinguish the nature of the time-scale dependent relationship between real oil price, real interest rate, and unemployment using wavelet decomposition.

ACS Style

Hyunjoo Kim Karlsson; Yushu Li; Ghazi Shukur. The Causal Nexus between Oil Prices, Interest Rates, and Unemployment in Norway Using Wavelet Methods. Sustainability 2018, 10, 2792 .

AMA Style

Hyunjoo Kim Karlsson, Yushu Li, Ghazi Shukur. The Causal Nexus between Oil Prices, Interest Rates, and Unemployment in Norway Using Wavelet Methods. Sustainability. 2018; 10 (8):2792.

Chicago/Turabian Style

Hyunjoo Kim Karlsson; Yushu Li; Ghazi Shukur. 2018. "The Causal Nexus between Oil Prices, Interest Rates, and Unemployment in Norway Using Wavelet Methods." Sustainability 10, no. 8: 2792.

Journal article
Published: 06 June 2016 in Agribusiness
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Understanding of price behavior is a critical element to make decisions in uncertain conditions that significantly influence the return of dairy market participants. Increased variability in both the world milk price and the world feed price during the last 7-8 years has increased the need for research on price dynamics and price forecasting. The aims of this paper are to explore the dynamics embedded in and between the world milk and feed prices, and to produce reliable forecasts for both prices.We collected the world milk price and the world feed price series from 2002 to 2015 from the International Farm Comparison Network (IFCN). The analysis revealed that the two price series contain business cycles of approximately 32 months. Further, the two series are co- integrated, with the feed price as the leading variable. A combination of three different forecasting models can provide reasonably good forecasts of both prices.

ACS Style

Bjørn Gunnar Hansen; Yushu Li. An Analysis of Past World Market Prices of Feed and Milk and Predictions for the Future. Agribusiness 2016, 33, 175 -193.

AMA Style

Bjørn Gunnar Hansen, Yushu Li. An Analysis of Past World Market Prices of Feed and Milk and Predictions for the Future. Agribusiness. 2016; 33 (2):175-193.

Chicago/Turabian Style

Bjørn Gunnar Hansen; Yushu Li. 2016. "An Analysis of Past World Market Prices of Feed and Milk and Predictions for the Future." Agribusiness 33, no. 2: 175-193.