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Статті в журналах з теми "Structural breaks"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 25 травня 2024

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1

Czech, Katarzyna. "Structural Changes in Wheat Market." Zeszyty Naukowe SGGW w Warszawie - Problemy Rolnictwa Światowego 16, no. 4 (December 31, 2016): 92–98. http://dx.doi.org/10.22630/prs.2016.16.4.102.

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Анотація:

Time series analysis is based on the assumption of stationarity. Stationarity implies the parameters are constant over time. Structural break occurs when at least one of the parameters changes at some date. Structural breaks can lead to huge forecasting errors and unreliability of the model. Modelling structure breaks is very popular in the literature of macroeconomics and finance. However, there are still too few publications about structural breaks in agricultural market. The goal of research is to identify structural breaks in wheat prices time series. A few structural break tests are applied. It has been shown that there is at least one significant structural break in the analysed time series. Both Quandt-Andrews and Bai-Perron tests show that there is a significant breakpoint in 12.09.2007. The estimated break date is associated with the beginning of global financial crisis. It may imply that wheat prices have become more prone to changes in global financial market.

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2

Ngene, Geoffrey, Ann Nduati Mungai, and Allen K. Lynch. "Long-Term Dependency Structure and Structural Breaks: Evidence from the U.S. Sector Returns and Volatility." Review of Pacific Basin Financial Markets and Policies 21, no. 02 (May 27, 2018): 1850008. http://dx.doi.org/10.1142/s021909151850008x.

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The study investigates the impact of structural breaks on the long memory of daily returns and variance of 11 sectors. Using multiple sequential structural breaks tests, we uncover numerous and roughly shared structural breaks. Results from two non-parametric, three semi-parametric, and three parametric fractional differencing models using break-adjusted and break-unadjusted returns reveal incidence of short memory and anti-persistence in sector returns. Regarding variance, we find that the removal of breaks from the sector series dampens the fractional differencing parameter estimates. Therefore, the observed long memory in variance may be attributable to the occurrence of structural breaks in the sector series.

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3

Groothuis, Peter A., Kurt W. Rotthoff, and Mark C. Strazicich. "Structural Breaks in the Game." Journal of Sports Economics 18, no. 6 (July 6, 2015): 622–37. http://dx.doi.org/10.1177/1527002515593113.

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To search for eras in a sports league, we utilize time-series tests with structural breaks in Major League Baseball performance. Using data from 1871-2010, the mean and standard deviation of four different performance measures are examined. Throughout, rather than assume that a break point is known a priori, we identify breaks endogenously from the data. Perhaps most notable among our findings, we identify a deterministic trend in mean slugging percentage with breaks in 1921 and 1992. Interestingly, these years closely coincide with the early years of the free-swinging (Babe Ruth) era and the modern steroid era, respectively.

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4

Galadima, Mukhtar Danladi, and Abubakar Wambai Aminu. "STRUCTURAL BREAKS IN NATURAL GAS CONSUMPTION AND ECONOMIC GROWTH IN NIGERIA: EVIDENCE FROM NEW TIME SERIES TESTS THAT ALLOW FOR STRUCTURAL BREAKS." International Journal of New Economics and Social Sciences 9, no. 1 (June 28, 2019): 275–92. http://dx.doi.org/10.5604/01.3001.0013.3049.

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This paper analyzed the issue of structural breaks in natural gas consumption and economic growth in Nigeria. The newly residual augmented least squares (RALS-LM) unit root test with breaks also known as “RALS-LM test with trend breaks and non-normal errors” proposed by Meng-Lee-Payne (2017) and the new structural breaks testing proposed by Kejriwal–Perron (2010) are among the tools used for the investi-gation. Our empirical findings provide significant evidence that the series of natural gas consumption and economic growth are stationary with one or two trend breaks. Furthermore, the investigation identified significant incidences of structural breaks in the relationship between natural gas consumption and economic growth in 1990, 2004, 2009 and all the break dates were found to be significant. The evaluation of the sub-sample periods based on the break dates revealed that the first and second breaks are potential while the last is destructive. Moreover, the estimate of the long-run elasticity is significant where a 1% increase in natural gas consumption induces the growth of Nigerian economy by 0.15% and all the dummies that represent the breakpoints are also significant where the 2004 break had a bigger effect among other breaks. The implication of the results is that shocks in the series of natural gas consumption and economic growth in Nigeria have transitory effect, modeling the relationship between natural gas consumption and economic growth in Nigeria without taking structural breaks into consideration could produce biased and unreliable statistical results, and there is economically significant dependence of the Nigerian economy on natural gas consumption.

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5

Raifu, Isiaka Akande. "Is Tourism-Led-Growth Hypothesis Valid in the Presence of Structural Breaks?" Tourism 72, no. 2 (April 3, 2024): 270–74. http://dx.doi.org/10.37741/t.72.2.11.

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This study revisited the tourism-led growth hypothesis (TLGH) in the presence of structural breaks using the structural break technique of Ditzen et al. (2021). To estimate the impact of tourism on economic growth along the identified structural breaks, we employed Fixed Effects and Feasible Generalised Least Squares methods. Findings showed four structural break dates (1999, 2004, 2009 and 2014), two of which coincided with the Global Financial Crisis (2008-2009) and the Ebola outbreak (2014). Despite the presence of structural breaks, the TLGH remains valid.

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6

Smith, Simon C., George Bulkley, and David S. Leslie. "Equity Premium Forecasts with an Unknown Number of Structural Breaks." Journal of Financial Econometrics 18, no. 1 (January 12, 2019): 59–94. http://dx.doi.org/10.1093/jjfinec/nby034.

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Abstract Estimation of models with structural breaks usually assumes a pre-specified number of breaks. Previous models which do allow an endogenously determined number of breaks require a simple structural model, and rarely allow for information transfer across the break. We introduce a methodology that allows the number of breaks to be determined endogenously and including an economically motivated model of transition regimes between each break. We demonstrate the usefulness of our approach for forecasts of the equity premium. We find the demonstrated success of the historical average can be improved upon by an economic model with theory informed priors estimated using our methodology.

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7

Huang, Yirong, Liang Ding, Yan Lin, and Yi Luo. "A new approach to detect long memory by fractional integration or short memory by structural break." AIMS Mathematics 9, no. 6 (2024): 16468–85. http://dx.doi.org/10.3934/math.2024798.

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<abstract> <p>Long memory in test statistics can either originate from fractional integration or be spuriously induced by a short memory process with a structural break. This research estimated and detected long memory from the two causes by simulations and empirical analysis. The simulation results showed that fractional integration and structural break processes could demonstrate long memory properties. The 2ELW estimator was stable for fractional integration but not stable for time series with structural breaks. The modified W statistic based on 2ELW was efficient in discriminating fractional integration and structural breaks. Moreover, we found that six volatility time series of stock indexes and individual stocks in the Chinese market experience significant long memory and structural breaks, and the fractional differencing parameter is overestimated without controlling structural breaks.</p> </abstract>

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8

Tsuji, Chikashi. "Structural Breaks and Volatility Spillovers: The Case of the US and Canadian Stock Markets." Journal of Management Research 11, no. 2 (April 3, 2019): 30. http://dx.doi.org/10.5296/jmr.v11i2.14513.

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This paper investigates the relations of structural breaks and volatility spillovers by using the US and Canadian stock return data. Specifically, applying spillover MGARCH models without and with structural break dummy variables to the two stock returns, this study derives the following interesting evidence. (1) First, we reveal that for both the US and Canadian stock returns, the volatility persistence parameter values in our spillover MGARCH models decline when structural break dummy variables are incorporated. (2) Second, we further clarify that when we do not take structural breaks into account, the spillover effect was unidirectional from Canada to the US. However, when we take structural breaks into consideration, the results from our spillover MGARCH model with structural break dummies demonstrate that the volatility spillover effects between the US and Canada become bidirectional. (3) Third, we furthermore reveal that around the Lehman Brothers bankruptcy in 2008, the time-varying volatilities derived from our spillover MGARCH model with structural break dummy variables show slightly higher values than those volatilities from our spillover MGARCH model with no structural break dummy variable.

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9

Skrobotov, Anton. "Structural breaks in cointegration models: Multivariate case." Applied Econometrics 64, no. 4 (2021): 83–106. http://dx.doi.org/10.22394/1993-7601-2021-64-83-106.

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This review discusses methods of testing for a cointegration rank in a multivariate time series in the presence of structural breaks. The review covers both the methods with known and unknown break date. Multiple breaks are also considered. The issues of testing for cointegration with a possible change in the cointegration rank over time are discussed separately.

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10

Jiang, Zhuhua, Walid Mensi, and Seong-Min Yoon. "Risks in Major Cryptocurrency Markets: Modeling the Dual Long Memory Property and Structural Breaks." Sustainability 15, no. 3 (January 24, 2023): 2193. http://dx.doi.org/10.3390/su15032193.

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This study estimates the effects of the dual long memory property and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model, with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets, confirming our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modeling. The ARFIMA-FIGARCH model, with structural breaks and a skewed Student–t distribution, fits the cryptocurrency market’s price dynamics well.

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11

Angelini, Paolo. "Testing for structural breaks." Journal of Monetary Economics 34, no. 3 (December 1994): 561–66. http://dx.doi.org/10.1016/0304-3932(94)90034-5.

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12

Canarella, Giorgio, and Stephen M. Miller. "Inflation persistence and structural breaks." Journal of Economic Studies 43, no. 6 (November 14, 2016): 980–1005. http://dx.doi.org/10.1108/jes-10-2015-0190.

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Анотація:

Purpose The purpose of this paper is to report on a sequential three-stage analysis of inflation persistence using monthly data from 11 inflation targeting (IT) countries and, for comparison, the USA, a non-IT country with a history of credible monetary policy. Design/methodology/approach First, the authors estimate inflation persistence in a rolling-window fractional-integration setting using the semiparametric estimator suggested by Phillips (2007). Second, the authors use tests for unknown structural breaks as a means to identify effects of the regime switch and the global financial crisis on inflation persistence. The authors use the sequences of estimated persistence measures from the first stage as dependent variables in the Bai and Perron (2003) structural break tests. Finally, the authors reapply the Phillips (2007) estimator to the subsamples defined by the breaks. Findings Four countries (Canada, Iceland, Mexico, and South Korea) experience a structural break in inflation persistence that coincide with the implementation of the IT regime, and three IT countries (Sweden, Switzerland, and the UK), as well as the USA experience a structural break in inflation persistence that coincides with the global financial crisis. Research limitations/implications The authors find that in most cases the estimates of inflation persistence switch from mean-reversion nonstationarity to mean-reversion stationarity. Practical implications Monetary policy implications differ between pre- and post-global financial crisis. Social implications Global financial crisis affected the persistence of inflation rates. Originality/value First paper to consider the effect of the global financial crisis on inflation persistence.

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13

Hewag, Rishan Sampath, Jaafar Pyeman, and Norashida Othman. "Effect of Structural Break on Financial Development and Economic Growth Nexus in Middle-Income Countries in Asia: Moderating Role of Technological Advancements." Information Management and Business Review 15, no. 2(I)SI (June 11, 2023): 205–14. http://dx.doi.org/10.22610/imbr.v15i2(i)si.3407.

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Asian countries have experienced many financial catastrophes and pandemics in the past couple of decades. Therefore, evaluating the effect of structural breaks on economies has taken substantive attention in the empirical literature. Thus, this study aims to investigate the effect of structural breaks on the Financial Development (FD) and Economic Growth (EG) of middle-income countries in Asia. The study considered the global financial crisis of 2008 as a key structural break. The sample consisted of 24 middle-income countries while the sample period was 20 years from 2000 to 2019. Technological Advancement was considered a moderator variable that facilitate the smooth functioning of the FD and EG. Structural Break was identified using the "xtbreak" function and the effect was analyzed with panel unit root, panel homogeneity test and short panel cointegration. The findings revealed that before the structural break, FD and moderator variables were the only significant variables but after the structural breaks, Human Capita and Private Consumption also have become significant. FD had a significant negative influence over EG before and after the structural break but the moderator variable had a positive influence. HC was not a significant factor before the structural break but has become significant after the economic downturn with a positive influence on EG. In contrast, PC shows a negative influence after the structural breaks. Hence, it guides the policymakers to decide on reducing investments for FD and direct funds to HC development and developing the technology. Moreover, they must consider making policy decisions to reduce PC.

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14

Perron, Pierre. "Unit Roots and Structural Breaks." Econometrics 5, no. 2 (May 30, 2017): 22. http://dx.doi.org/10.3390/econometrics5020022.

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15

Skrobotov, A. A. "Structural breaks in cointegration models." Applied Econometrics 63 (2021): 117–41. http://dx.doi.org/10.22394/1993-7601-2021-63-117-141.

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16

Aue, Alexander, and Lajos Horváth. "Structural breaks in time series." Journal of Time Series Analysis 34, no. 1 (September 14, 2012): 1–16. http://dx.doi.org/10.1111/j.1467-9892.2012.00819.x.

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17

Caporale, Guglielmo Maria, Nikitas Pittis, and Nicola Spagnolo. "IGARCH models and structural breaks." Applied Economics Letters 10, no. 12 (October 2003): 765–68. http://dx.doi.org/10.1080/1350485032000138403.

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18

Smith, Jeremy, and Jesus Otero. "Structural breaks and seasonal integration." Economics Letters 56, no. 1 (September 1997): 13–19. http://dx.doi.org/10.1016/s0165-1765(97)00156-0.

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19

Delgado, Miguel A., and Javier Hidalgo. "Nonparametric inference on structural breaks." Journal of Econometrics 96, no. 1 (May 2000): 113–44. http://dx.doi.org/10.1016/s0304-4076(99)00052-4.

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20

Chou, Pin-Huang, and Kuan-Cheng Ko. "Characteristics, covariances, and structural breaks." Economics Letters 100, no. 1 (July 2008): 31–34. http://dx.doi.org/10.1016/j.econlet.2007.10.025.

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21

Maheu, John M., and Stephen Gordon. "Learning, forecasting and structural breaks." Journal of Applied Econometrics 23, no. 5 (August 2008): 553–83. http://dx.doi.org/10.1002/jae.1018.

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22

Bastos, Felipe S., Elano F. Arruda, Rafael B. Barbosa, and Roberto T. Ferreira. "Speed of Reversion to PPP with Structural Breaks for Brazilian Cities." International Journal of Economics and Finance 10, no. 4 (March 3, 2018): 15. http://dx.doi.org/10.5539/ijef.v10n4p15.

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This article analyzes the effect of introducing structural breaks in calculating the convergence speed of relative prices for Brazilian cities in the period from 1991.01 to 2016.11. Three structural break dates were endogenously chosen (1996.02, 2001.12 and 2010.10) and they represent different situations of the Brazilian economy, with impacts on intra-national relative prices. The convergence speed, measured by the half-life, declined by approximately 77% after controlling for these structural changes. The result was robust to changes in numeraire both for calculation of the half-life and estimation of the structural break dates, and indicates the importance of considering structural breaks in calculating intra-national purchasing power parity, as found in other studies.

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23

Perez, Maria, Marco Palma, Bridget Behe, and Charles Hall. "Structural Breaks and Future Growth of the Green Industry." Journal of Environmental Horticulture 34, no. 2 (June 1, 2016): 52–55. http://dx.doi.org/10.24266/0738-2898-34.2.52.

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Every industry undergoes changes, but structural changes can abruptly and radically alter business for many firms in that industry. Identifying the substantial shifts in the green industry with regard to consumer spending can help the industry better understand its history. Using an econometric model of that same consumer spending data to look forward to the future can show firms what might lie ahead. We analyzed the personal consumption expenditures for two items measured by the U.S. Bureau of Economic Analysis to identify structural breaks in the green industry. We then conducted an econometric forecast using that same data to predict future consumer spending projections to the year 2020. To accomplish this, we analyzed household expenditures for Tools (including gardening tools and equipment) and Plants (including seeds, flowers, and plants) from 1959 to 2014. We identified one industry structural break using the Schwarz criteria for Tools in 2006 and another one for Plants in 2007 and (separately) identified four breaks using Bayesian Information criteria: one break for Tools in 2006 and three breaks in Plants in 1986, 2003, and 2008. The potential causes of these breaks are discussed (e.g. housing bubble, financial market stress). Lastly, we employed an econometric model to forecast spending and show that it will grow from $65.15 and $86.52 in 2015 to $71.17 and $96.97, for Tools and Plants respectively, in 2020.

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24

Kumar, Saurabh, Jitendra Kumar, Vikas Kumar Sharma, and Varun Agiwal. "Random order autoregressive time series model with structural break." Model Assisted Statistics and Applications 15, no. 3 (October 9, 2020): 225–37. http://dx.doi.org/10.3233/mas-200490.

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This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.

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25

Abu-Bader, Suleiman, and Aamer S. Abu-Qarn. "The Relationship between GATT Membership and Structural Breaks in International Trade." Global Economy Journal 8, no. 4 (October 2008): 1850148. http://dx.doi.org/10.2202/1524-5861.1398.

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Using sequential structural break tests, we attempt to determine if and when a new GATT member experiences statistically significant changes in the paths of its trade with incumbent members. To test for the nature of a change, we compare the averages of the actual postbreak trade shares with the averages of the postbreak extrapolated trade shares. Should a significant structural break be detected, we compare the break year with the accession year of that country to GATT. Our results show that only a small fraction of countries experience significant positive structural breaks in their trade shares. Furthermore, any significant positive breaks generally occur far before or after the time of a country's accession to GATT.

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26

Li, Qiang, Liming Wang, and Fei Qiu. "Detecting the Structural Breaks in GARCH Models Based on Bayesian Method: The Case of China Share Index Rate of Return." Journal of Systems Science and Information 3, no. 4 (August 25, 2015): 321–33. http://dx.doi.org/10.1515/jssi-2015-0321.

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AbstractThis paper investigates the detection for structural breaks in GARCH models based on Bayesian method. The authors firstly introduce the background and significance of this problem, then present the current situation and recent developments in this field. Because the rates of return have heavy tails, the authors present GARCH models. In this paper, the authors innovatively suppose that the error term follows standard studenttdistribution with degree of freedomvinstead of standard normal distribution. The authors give the specific description of estimation using Bayesian method, including a single structural break situation and multiple structural breaks situation when the number of breaks is unknown. In an application, the authors empirically research the volatility of stock market in China. The authors estimate GARCH models with structural breaks for the Shanghai Α-share index and Shenzhen Α-share index rate of return over the period of January 4, 2000–September 30, 2011. The authors explain the breaks together with the nearby big political and economic events. Empirical results show that the detecting method used in this paper is feasible.

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27

Oliveira, Fernando Nascimento, and Fernando Cesar dos Santos Cunha. "Estimando Betas de Mercado com Quebras Estruturais." Brazilian Review of Finance 15, no. 2 (June 18, 2018): 251. http://dx.doi.org/10.12660/rbfin.v15n2.2017.64058.

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This study verifies the contribution of a structural break (if any) to CAPM models. Therefore, we used all the assets listed in Bovespa and New York Stock Exchange in monthly frequencies. Three famous structural breaks tests were used. The results show that structural breaks are relevant in most models for most sectors of the economy. Then, the identified structural breaks are inserted in the models and the betas of CAPM models were re-estimated. The Betas that were statistically significant were chosen and their results compared to Market Beta for each sector of the economy. The results show that the estimated Betas resemble Market Beta in more than 78% of the economic sectors of the Brazilian and North-American markets.

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28

Alammar, Radwan, and Almougheer Wardeh. "The impact of macroeconomic variables on stock market returns: Evidence from a sample of Arabic countries facing political and economic instability." International Journal of Business, Economics and Management 11, no. 1 (February 6, 2024): 1–18. http://dx.doi.org/10.18488/62.v11i1.3633.

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Анотація:

The main purpose of this study is to determine the presence and timing of the structural breaks in the stock market returns and investigate their impact on the relationship between the macroeconomic variables (namely the inflation rate, the exchange rate, the oil prices) and the stock market returns of a sample of Arab countries facing political and economic instability, namely (Syria, Egypt, Tunisia and Bahrain), during the period (2010-2020). CUSUM test is performed in order to test the presence of structural breaks in stock market indexes. In case there is evidence of structural breaks, Bai and Perron Multiple Breakpoints test is used to identify points in time where significant changes may have occurred. The timing of structural breaks is used as a control variable to examine the impact of the macroeconomic variables on the stock market returns through applying Johansen cointegration test. The results provide evidence of the presence and timing of the structural breaks in the Arab stock markets under study and their role in impacting the relationship between the macroeconomic variables and stock market returns. The study concluded that the dynamics of how the macroeconomic variables affecting the stock market returns depends on the nature and timing of the structural break.

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29

Umoru, David, Solomon Edem Effiong, Malachy Ashywel Ugbaka, Salisu Shehu Umar, Orobosa Abraham Ihensekhien, Friday Osaru Ovenseri-Ogbomo, Nkang Enighe Eyam, Ubi Ubi Omini, Anna Nuhu Tizhe, and Rafat Hussaini. "Estimating effects of nominal exchange rates and oil price shocks in the presence of structural breaks." Journal of Governance and Regulation 12, no. 3 (2023): 147–62. http://dx.doi.org/10.22495/jgrv12i3art16.

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Анотація:

Macroeconomic stability is an objective emerging economy desired to achieve but oil price shocks and fluctuations in nominal exchange rates tend to restrain the ability of these economies to achieve such macroeconomic balance. Regrettably, exchange rates and oil price shocks are prone to have structural breaks in defined periods. We therefore, implemented a bivariate diagonal BEKK model, Zivot-Andrews and Bai-Perron breakpoint tests to evaluate the effect of exchange rates and oil price shocks in the presence of structural breaks on macroeconomic stability in developing countries. Break dates were observed for Benin Republic and Côte d’Ivoire between 1997M01 and 1996M09, Gambia in 2003M12, Niger in 2011M04, Ghana in 2000 and 2008, and Nigeria in 2020. All break dates were attributed to various causes including COVID-19 pandemic, the United States (US) invasion of Iraq in 2003, the US recession, and the Persian Gulf crisis. The findings showed variations in oil prices and exchange rates have a hostile impact on the level of the consumer price index (CPI) after controlling for structural breaks for all countries excluding Burkina Faso. Hence, shocks conveyed significant instability in the domestic price levels of Gambia, Benin, Niger, Ghana, and Nigeria. Models of inflation should be examined after controlling for external crises and structural breaks.

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30

Sivri, Uğur. "Is Inflation Rate of Turkey Stationary? Evidence from Unit Root Tests with and Without Structural Breaks." Review of Economic and Business Studies 10, no. 2 (December 1, 2017): 29–52. http://dx.doi.org/10.1515/rebs-2017-0053.

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AbstractTurkey has high inflation experience and in order to bring inflation rate down as well as maintaining macroeconomic stability many policy changes and reforms have been implemented. Despite some success, decreasing inflation rate is still an aim of monetary policy and price stability is still faraway. This article investigates time series properties of Turkish CPI inflation rate in both seasonally unadjusted and adjusted forms. Results of various unit root tests without structural breaks generally show that inflation rate is a nonstationary variable. This article also uses one and two breaks minimum LM unit root tests due to Lee and Strazicich (2004, 2003), respectively. In this case, test results show that inflation rate is a stationary variable with breaks. Although selected break points differ with respect to models and variables to some extent, it is observed that one break occurred around March 1994, and the second break occurred around April 2001.

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31

Emirmahmutoglu, Furkan, Tolga Omay, Syed Jawad Hussain Shahzad, and Safwan Mohd Nor. "Smooth Break Detection and De-Trending in Unit Root Testing." Mathematics 9, no. 4 (February 13, 2021): 371. http://dx.doi.org/10.3390/math9040371.

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Анотація:

This study explores the methods to de-trend the smooth structural break processes while conducting the unit root tests. The two most commonly applied approaches for modelling smooth structural breaks namely the smooth transition and the Fourier functions are considered. We perform a sequence of power comparisons among alternative unit root tests that accommodate smooth or sharp structural breaks. The power experiments demonstrate that the unit root tests utilizing the Fourier function lead to unexpected results. Furthermore, through simulation studies, we investigate the source of such unexpected outcomes. Moreover, we provide the asymptotic distribution of two recently proposed unit root tests, namely Fourier-Augmented Dickey–Fuller (FADF) and Fourier-Kapetanios, Shin and Shell (FKSS), which are not given in the original studies. Lastly, we find that the selection of de-trending function is pivotal for unit root testing with structural breaks.

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32

Lean, Hooi Hooi, and Russell Smyth. "Do Asian Stock Markets Follow a Random Walk? Evidence from LM Unit Root Tests with One and Two Structural Breaks." Review of Pacific Basin Financial Markets and Policies 10, no. 01 (March 2007): 15–31. http://dx.doi.org/10.1142/s0219091507000933.

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Анотація:

This paper applies univariate and panel Lagrange Multiplier (LM) unit root tests with one and two structural breaks to examine the random walk hypothesis for stock prices in eight Asian countries. The results from the univariate LM unit root tests and panel LM unit root test with one structural break suggest that stock prices in each country is characterized by a random walk, but the findings from the panel LM unit root test with two structural breaks suggest that stock prices in the eight countries are mean reverting.

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33

Hegerty, Scott W. "Housing loans and domestic credit in the Baltic States and Poland: Structural breaks and macroeconomic determinants." Journal of Economics and Management 42 (2020): 48–69. http://dx.doi.org/10.22367/jem.2020.42.03.

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Aim/purpose – This study examines the time-series properties of home loans and do-mestic credit in Poland and the three Baltic countries, first in the univariate sense by identifying structural breaks in the series, and then using a multivariate model to identify the key drivers of loan growth.Design/methodology/approach – Structural break tests are conducted using the method of Bai & Perron (1998), while orthgonalised VARs are used for the macroeconomic model.Findings – The Estonian and Lithuanian home lending growth series have structural breaks in 2007, preceding the onset of the 2008 Global Financial Crisis. Estonian home lending has two additional structural breaks in 2009 and 2013. Neither of the two Polish lending series has any break after the sample begins in 2009, indicating more stability in the country’s markets. In the macroeconomic model, consumer price inflation and real effective exchange-rate appreciations have the largest influence on lending and credit growth, and Poland more affected than the Baltic countries.Research implications/limitations – This study opens the door to future research be-hind the specific causes of structural breaks in these series. While there is some evidence of an ‘early warning’ before the 2008 crisis, longer data series are needed for Poland and especially in the case of Latvia.Originality/value/contribution – This study offers insight into the lending markets in an area of the world that was significantly impacted by the 2008 crisis. Understanding the behaviour and causes of lending growth will help avoid future problems.

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34

Stawiarski, Bartosz. "Selected Techniques of Detecting Structural Breaks in Financial Volatility." e-Finanse 11, no. 1 (March 1, 2015): 32–43. http://dx.doi.org/10.1515/fiqf-2016-0104.

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Анотація:

Abstract We investigate several promising algorithms, proposed in literature, devised to detect sudden changes (structural breaks) in the volatility of financial time series. Comparative study of three techniques: ICSS, NPCPM and Cheng’s algorithm is carried out via numerical simulation in the case of simulated T-GARCH models and two real series, namely German and US stock indices. Simulations show that the NPCPM algorithm is superior to ICSS because is not over-sensitive either to heavy tails of market returns or to their serial dependence. Some signals generated by ICSS are falsely classified as structural breaks in volatility, while Cheng’s technique works well only when a single break occurs.

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35

Pástor, Ľluboš, and Robert F. Stambaugh. "The Equity Premium and Structural Breaks." Journal of Finance 56, no. 4 (August 2001): 1207–39. http://dx.doi.org/10.1111/0022-1082.00365.

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36

Pendell, Dustin L., and Allen M. Featherstone. "Structural breaks and agricultural asset allocation." Agricultural Finance Review 67, no. 2 (November 2007): 259–78. http://dx.doi.org/10.1108/00214660780001208.

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37

Arestis, Philip, and Iris Biefang-Frisancho Mariscal. "OECD unemployment: structural breaks and stationarity." Applied Economics 32, no. 4 (March 2000): 399–403. http://dx.doi.org/10.1080/000368400322570.

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38

Dropsy, Vincent. "Real exchange rates and structural breaks." Applied Economics 28, no. 2 (February 1, 1996): 209–19. http://dx.doi.org/10.1080/000368496328849.

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39

Kim, In-Moo. "Detecting the number of structural breaks." Economics Letters 57, no. 2 (December 1997): 145–48. http://dx.doi.org/10.1016/s0165-1765(97)00229-2.

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40

Heinesen, Eskil. "Structural Breaks in Error Correction Models." Oxford Bulletin of Economics and Statistics 59, no. 1 (February 1997): 187–92. http://dx.doi.org/10.1111/1468-0084.00057.

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41

Song, Junmo, and Changryong Baek. "Detecting structural breaks in realized volatility." Computational Statistics & Data Analysis 134 (June 2019): 58–75. http://dx.doi.org/10.1016/j.csda.2018.12.007.

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42

Hadri, Kaddour, and Yao Rao. "Panel Stationarity Test with Structural Breaks." Oxford Bulletin of Economics and Statistics 70, no. 2 (April 2008): 245–69. http://dx.doi.org/10.1111/j.1468-0084.2008.00502.x.

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43

Gil-Alana, Luis A., Yadollah Dadgar, and Rouhollah Nazari. "Iranian inflation: peristence and structural breaks." Journal of Economics and Finance 43, no. 2 (August 9, 2018): 398–408. http://dx.doi.org/10.1007/s12197-018-9446-x.

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44

Kashikar, Akanksha S., Neelabh Rohan, and T. V. Ramanathan. "Integer autoregressive models with structural breaks." Journal of Applied Statistics 40, no. 12 (August 2013): 2653–69. http://dx.doi.org/10.1080/02664763.2013.823920.

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45

Rohan, Neelabh, and T. V. Ramanathan. "Asymmetric Volatility Models with Structural Breaks." Communications in Statistics - Simulation and Computation 41, no. 9 (October 2012): 1519–43. http://dx.doi.org/10.1080/03610918.2011.611403.

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46

Gagliardini, Patrick, Fabio Trojani, and Giovanni Urga. "Robust GMM tests for structural breaks." Journal of Econometrics 129, no. 1-2 (November 2005): 139–82. http://dx.doi.org/10.1016/j.jeconom.2004.09.006.

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47

Gao, Jiti, Irène Gijbels, and Sébastien Van Bellegem. "Nonparametric simultaneous testing for structural breaks." Journal of Econometrics 143, no. 1 (March 2008): 123–42. http://dx.doi.org/10.1016/j.jeconom.2007.08.009.

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48

Smith, Simon C. "Equity premium prediction and structural breaks." International Journal of Finance & Economics 25, no. 3 (November 12, 2019): 412–29. http://dx.doi.org/10.1002/ijfe.1759.

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49

Parsaeian, Shahnaz. "Stein-like Common Correlated Effects Estimation under Structural Breaks." Econometrics 12, no. 2 (April 18, 2024): 11. http://dx.doi.org/10.3390/econometrics12020011.

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Анотація:

This paper develops a Stein-like combined estimator for large heterogeneous panel data models under common structural breaks. The model allows for cross-sectional dependence through a general multifactor error structure. By utilizing the common correlated effects (CCE) estimation technique, we propose a Stein-like combined estimator of the CCE full-sample estimator (i.e., estimation using both the pre-break and post-break observations) and the CCE post-break estimator (i.e., estimation using only the post-break sample observations). The proposed Stein-like combined estimator benefits from exploiting the pre-break sample observations. We derive the optimal combination weight by minimizing the asymptotic risk. We show the superiority of the CCE Stein-like combined estimator over the CCE post-break estimator in terms of the asymptotic risk. Further, we establish the asymptotic properties of the CCE mean group Stein-like combined estimator. The finite sample performance of our proposed estimator is investigated using Monte Carlo experiments and an empirical application of predicting the output growth of industrialized countries.

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50

Aly, Hassan Y., and Mark C. Strazicich. "Did the Global Financial Crisis of 2007-2009 Impact Economic Growth in North Africa?" Perspectives on Global Development and Technology 11, no. 4 (2012): 437–55. http://dx.doi.org/10.1163/15691497-12341235.

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Анотація:

Abstract In this paper, we utilize time series tests with structural breaks to test for evidence of an impact on economic growth rates in North African countries following the 2007−2009 U.S. and global financial crisis. One or two breaks in economic growth are identified in each country, except for Morocco where no break is found. However, breaks that coincide with the financial crisis are found in only two of the six countries (Libya and Mauritania), while other breaks coincide most often with earlier U.S. and EU recessions. To further examine the impact of shocks, impulse response functions are estimated from Vector Auto-Regression models with structural breaks. We again find no evidence that shocks from the financial crisis had a significant impact on economic growth in North Africa. We conclude that shocks from the 2007−2009 financial crisis had only a temporary and relatively small impact on economic growth rates in North Africa.

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