nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒03‒20
three papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Disentangling Structural Breaks in High Dimensional Factor Models By Bonsoo Koo; Benjamin Wong; Ze-Yu Zhong
  2. An Application of Smooth Transition Regression Models to Homeless Research By Jadidzadeh, Ali
  3. Forecasting realized volatility in turbulent times using temporal fusion transformers By Frank, Johannes

  1. By: Bonsoo Koo; Benjamin Wong; Ze-Yu Zhong
    Abstract: We disentangle structural breaks in dynamic factor models by establishing a projection based equivalent representation theorem which decomposes any break into a rotational change and orthogonal shift. Our decomposition leads to the natural interpretation of these changes as a change in the factor variance and loadings respectively, which allows us to formulate two separate tests to differentiate between these two cases, unlike the pre-existing literature at large. We derive the asymptotic distributions of the two tests, and demonstrate their good finite sample performance. We apply the tests to the FRED-MD dataset focusing on the Great Moderation and Global Financial Crisis as candidate breaks, and find evidence that the Great Moderation may be better characterised as a break in the factor variance as opposed to a break in the loadings, whereas the Global Financial Crisis is a break in both. Our empirical results highlight how distinguishing between the breaks can nuance the interpretation attributed to them by existing methods.
    Keywords: factor space, structural instability, breaks, principal components, dynamic factor models
    JEL: C12 C38 C55 E37
    Date: 2023–03
  2. By: Jadidzadeh, Ali
    Abstract: This article discusses the limitations of linear models in explaining certain aspects of homelessness-related data and proposes the use of nonlinear models to allow for state-dependent or regime-switching behavior. The threshold autoregressive (TAR) model and its smooth transition autoregressive (STAR) extensions are introduced as a popular class of nonlinear models. The article explains how these models can be applied to univariate time series data to investigate variations in weather conditions on the flow of homeless shelters over time. The objective is to identify the sensitivity of publicly-funded emergency shelter use to changes in weather conditions and better inform social agencies and government funders of predictable and unpredictable changes in demand for shelter beds. The smooth transition regression (STR) model is proposed as a useful tool for investigating nonlinearities in non-autoregressive contexts using both time series and panel data. The article concludes by highlighting the advantages of STR models and their three-stage modeling procedure: model specification, estimation, and evaluation.
    Keywords: Homelessness; nonlinear models; smooth transition regression (STR) model.
    JEL: C01 C53 I32
    Date: 2022–12
  3. By: Frank, Johannes
    Abstract: This paper analyzes the performance of temporal fusion transformers in forecasting realized volatilities of stocks listed in the S&P 500 in volatile periods by comparing the predictions with those of state-of-the-art machine learning methods as well as GARCH models. The models are trained on weekly and monthly data based on three different feature sets using varying training approaches including pooling methods. I find that temporal fusion transformers show very good results in predicting financial volatility and outperform long short-term memory networks and random forests when using pooling methods. The use of sectoral pooling substantially improves the predictive performance of all machine learning approaches used. The results are robust to different ways of training the models.
    Keywords: Realized volatility, temporal fusion transformer, long short-term memory network, random forest
    JEL: C45 C53 C58 E44
    Date: 2023

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