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Title: Testing and Estimating Structural Breaks in Time Series and Panel Data in Stata
Abstract: Identifying structural change is a crucial step in analysis of time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed as a result of major disruptive events, such as the 2007-2008 financial crisis and the 2020 COVID-19 outbreak. Detecting the existence of breaks, and dating them is therefore necessary not only for estimation purposes but also for understanding drivers of change and their effect on relationships. This article introduces a new community contributed command called xtbreak, which provides researchers with a complete toolbox for analysing multiple structural breaks in time series and panel data. xtbreak can detect the existence of breaks, determine their number and location, and provide break date confidence intervals. The new command is used to explore changes in the relationship between COVID-19 cases and deaths in the US using both country-level time series data and state-level panel data.
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Structural Breaks in Financial Panel Data
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- Yiannis Karavias 3
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The purpose of this chapter is to review recently developed methods for analyzing structural breaks in panel data. Structural breaks are caused by events that change the parameters of economic and financial models, such as the 2007–2008 financial crisis and the COVID-19 crisis. If not correctly accounted for they lead to an invalid inference. It is not always clear if a significant event has affected a particular economic model or relationship, or the date on which the relationship suffered the change. Answers to these questions can only be given through formal econometric analysis. This chapter reviews methods for both detecting and dating structural breaks. Furthermore, the usefulness of structural break method is demonstrated in examining trade credit determinants in a panel of small and medium European enterprises. A structural break was detected and dated in the year 2010. Before the break, firm profitability, sales growth, and bank loans all contributed to an increase in trade credit. After the break, the bank loans’ effect was significantly reduced, providing evidence that the trade credit’s redistribution channel collapsed after the 2007–2008 financial crisis.
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Yiannis Karavias
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Karavias, Y. (2021). Structural Breaks in Financial Panel Data. In: Lee, CF., Lee, A.C. (eds) Encyclopedia of Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-73443-5_95-1
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DOI : https://doi.org/10.1007/978-3-030-73443-5_95-1
Received : 10 February 2021
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Published : 22 September 2021
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The paper makes recommendations for practitioners concerned with variation issues on model and method selection. 1 INTRODUCTION. Stability and structural breaks can occur during interventions and regulations, changes in formulations and climate, after treatment, between different social and economic groups, between homogenous and heterogeneous ...
The paper an attempt has been made to review the different forms of the presence of structural break(s) over the past. Discover the world's research 25+ million members
The pervasiveness of structural breaks in many macroeconomic time series is widely acknowledged (Stock and Watson 1996; Paye and Timmermann 2006) and they are an important source of a forecast failure (Hendry 2000; Hendry and Clements 2003).This paper considers a scenario in which a discrete and permanent change in model coefficients may occur during the sample period used for estimation.
Identifying structural change is a crucial step in analysis of time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed as a result of major disruptive events, such as the 2007-2008 financial crisis and the 2020 COVID-19 outbreak. Detecting the existence of breaks, and dating them is therefore necessary not only for estimation ...
A partial structural change model can be beneficial in terms of obtaining more precise estimates and more powerful tests. The method of estimation is standard least-squares (OLS), i.e., minimizing the overall sum of squared residuals (SSR) generalization with fractionally integrated errors can be found in Chang and Perron (2016).
This is as in our one-shot structural break setting, where the data-inspired hypothesis ('there is a structural break in the data') is more likely to be accepted even when wrong, producing a false negative. The remainder of the paper proceeds as follows. Section 2 states and discusses the main theoretical results of this paper. The proofs ...
There has been a growing interest in structural changes in economic literature. In the study we have focused on structural changes which can be captured by the structural breaks testing methodology and unit-root testing. We examined structural breaks of four countries, i.e. Ireland, Spain, Turkey and the Netherlands.
Structural Breaks in Time Series. This article covers methodological issues related to estimation, testing, and computation for models involving structural changes, and focuses on the so-called off-line methods whereby one wants to retrospectively test for breaks in a given sample of data and form confidence intervals about the break dates.
However, the structural break model optimally extracts any predictive value from this unstable relationship. The best-performing model is a structural break specification in which the prior on the variance parameter σ 2 of each new submodel is calibrated to the last period's posterior mean of σ 2. This model produces the best marginal ...
Zivot and Andrews (1992) endogenous structural break test is a sequential test. which utilizes the full sample and uses a different dummy variable for each possible. break date. The break date is ...
the financial crisis, Great Recession, the COVID-19 pandemic, and war. While structural breaks pose significant econometric challenges, machine learning provides an incisive tool for detecting and quantifying breaks. The current paper presents a unified framework for analyzing breaks;
This paper analyses the overall trend of key macroeconomic variables of BRICS nations impacted by the US financial crises (2008-2009) through structural break analysis for period 2000-2013. Dummy variable model using exogenous break-date examines intercept and trend shift simultaneously in a single linear equation.
The heart of Perron's paper is his test for structural change (Table 8.2). This is calculated for real GNP with using the code below. ... Research on structural breaks now includes extensions to panel data. González et al. , for example, extend the smooth transition literature to panels. They also now consider different break methods, such ...
Structural Breaks in an Endogenous Growth Model Timothy Cogley and Boyan Jovanovic NBER Working Paper No. 28026 October 2020 JEL No. E1 ABSTRACT We study the effects of parameter uncertainty prompted by structural breaks. In our model, agents respond differently to uncertainty prompted by regime shifts in shock processes than they react to
Dealing with Structural Breaks. Pierre Perron. Published 1 April 2005. Economics. Research Papers in Economics. This chapter is concerned with methodological issues related to estimation, testing and computation in the context of structural changes in the linear models. A central theme of the review is the interplay between structural change ...
Research Paper. A finite mixture analysis of structural breaks in the G-7 gross domestic product series. ... In this paper we apply a clustering procedure to detect trend changes in macroeconomic data, focusing on the GDP time series for the G-7 countries. A finite mixture of regression models is considered to show different patterns and ...
This paper explores a joint test of predictability and one-time structural break, both of which are assumed to be absent under the null hypothesis. The test combines IVX estimator with a sup-Wald-type statistic. The limiting distribution of the test statistic is expected to be non-pivotal under (near-)integration. Nevertheless, for univariate cases, the distribution is highly insensitive to ...
We combine standard structural-break methods with high-frequency data to identify shifts in inflation trends. We use this approach to study the inflation dynamics of 25 countries from January 2022 to April 2023 and find evidence of a broad-based slowdown in about half of them, including the US.
The paper concludes with some areas of future research. 2 Consequences of Structural Breaks in Financial Time Series This section discusses some of the statistical inference implications of struc-tural breaks on the persistence of financial time series, such as long range dependence and integrated GARCH effects. Assume that financial returns, r
Research Papers. Structural breaks in Box-Cox transforms of realized volatility: a model selection perspective. Simon Behrendt d-fine GmbH, An der Hauptwache ... We draw upon the fact that the HAR model is a constrained AR model and cast the problem of estimating structural breaks in the autoregressive volatility dynamics as a model selection ...
As it was argued in the introduction, financial relationships are subject to structural breaks frequently enough, so that it is suggested that testing for breaks should be reported together with descriptive statistics in any research paper. If a break is detected, this will impact the rest of the econometric analysis.