A monitoring procedure for detecting structural breaks in factor copula models

Structural breaks are considered as permanent changes in the series mainly because of shocks, policy changes, and global crises. Hence, making estimations by ignoring the presence of structural breaks may cause the biased parameter value. In this context, it is vital to identify the presence of the structural breaks and the break dates in the series to prevent misleading results. Accordingly, the first aim of this study is to compare the performance of unit root with structural break tests allowing a single break and multiple structural breaks. For this purpose, firstly, a Monte Carlo simulation study has been conducted through using a generated homoscedastic and stationary series in different sample sizes to evaluate the performances of these tests. As a result of the simulation study, Zivot and Andrews (J Bus Econ Stat 20(1):25–44, 1992) are the best-performing tests in capturing a single break. The most powerful tests for the multiple break setting are those developed by Kapetanios (J Time Ser Anal 26(1):123–133, 2005) and Perron (Palgrave Handb Econom 1:278–352, 2006). A new Bootstrap algorithm has been proposed along with the study’s primary aim. This newly proposed Bootstrap algorithm calculates the optimal number of statistically significant structural breaks under more general assumptions. Therefore, it guarantees finding an accurate number of optimal breaks in real-world data. In the empirical part, structural breaks in the real interest rate data of the US and Australia resulting from policy changes have been examined. The results concluded that the bootstrap sequential break test is the best-performing approach due to the general assumption made to cover real-world data.

Group fused Lasso for large factor models with multiple structural breaks

We consider a shrinkage quantile regression model for high‐dimensional panel data with multiple structural breaks. The structural breaks are assumed to be common across all individuals, but may vary across different quantile levels while sharing an identical location shift effect. We impose an L1 penalty on the individual effects and an L1‐type fusion penalty to estimate both the slope coefficients and the structural breaks by combining information at multiple quantile levels. The proposed method can detect “partial” changes of the regression coefficients and consistently estimate both the number and dates of the breaks with probability tending to 1. We establish the asymptotic properties of the proposed regression coefficient estimators as well as their post‐selection counterparts, where the dimensionality of the covariates is allowed to diverge. Simulation results demonstrate that the proposed method works well in finite‐sample cases. Using the proposed method, we obtain many interesting results by analyzing a dataset concerning environmental Kuznets curves.

The Structural Relationship Between Current and Capital Account Balance in India: A Time Series Analysis

Factor copula models are useful for modeling high-dimensional data sets that typically exhibit complex dependence structures. However, the practical merits of factor copula models have not been fully explored from an estimation point of view. This thesis proposes a simple, yet useful framework for estimating factor copula models, which allows for a richer and more precise understanding of the underlying dependence in the data sets.

The present paper attempts to find out the spatial and temporal movement of rice yield in twelve countries in Asia which is its lifeline over the period 1961 to 2016 by following Bai-Perron multiple structural break tests. The results based on endogenous multiple structural break analysis show that many of these countries passed through several phases of acceleration and deceleration in rice yield over this period The results also demonstrate that except Japan, all the twelve countries considered in our study experienced at least one structural break in rice yield at level during the period 1961 to 2016. The acceleration phases of rice yield growth observed mostly during 1970's and 1980's are resulted from improved technological innovations in agriculture whereas deceleration phases observed in the last two decades are mainly caused by environmental challenges on rice production. Cross country yield gaps have been showing declining trend since early 80's.

The sugar industry has been the backbone of the Fijian economy since the late 1950s. Owing to its poor performance over the recent years, and especially the expiry of preferential agreements with the European markets after September 2017, the long term sustainability of the industry has come under the scrutiny. In light of this scrutiny, this paper investigates the response of Fiji’s sugar export earnings to domestic and external shocks using a suite of unit root tests that allows for endogenous multiple structural breaks. Our analysis reveals that sugar export earnings is a non-stationary process, implying that shocks to sugar export earnings will have a permanent impact. The empirical results suggest that the impact of adverse shocks (for e.g. expiry of preferential agreement with the European Union and natural disasters) will not only be restricted to sugar export earnings but will also be transmitted to other related variables and sectors of the Fijian economy. Moreover, the results also suggest that temporary (short-term) measures to increase sugar export earnings will mostly be ineffective, as the export earnings will not revert back to its equilibrium path. Our finding underscores the necessity of policy intervention through permanent policy changes to minimise fluctuations in the sugar export earnings, and shelter the industry from adverse external and domestic shocks.

PurposeThe purpose of this paper is to investigate the time series behavior of co‐movements among 11 European real estate securities markets, with each other as well as between country‐averages, over the sample period from January 1999 to January 2010 by utilizing the asymmetric dynamic conditional correlation (ADCC) technique, long‐memory tests and multiple structural break methodology.Design/methodology/approachFirst the ADCC from the multivariate GJR‐GARCH model is used to estimate the pair‐wise conditional correlations between the 11 securitized real estate markets. Then, the 11 country‐average conditional correlation series is subject to a battery of four long‐memory tests to form an “on the balance of evidence” picture; the semi‐parametric Geweke and Porter‐Hudak procedure and Robinson test, as well as the non‐parametric Hurst‐Mandelbrot R/S and Lo's modified R/S tests. Finally, the Bai and Perron's multiple structural break methodology seeks to test whether the average conditional correlations are subject to regime switching via the detection of breaks in the co‐movements of real estate securities returns.FindingsLow to moderate conditional correlations are found for these European real estate securities market and a higher level of correlation in the aftermath of the global financial crisis. The long‐memory correlation effect is present for nine European real estate securities markets. In addition, the conditional correlations are subject to regime switching with two structural breaks in four country‐average correlation series. Across the regimes, a higher level of correlation is linked to a higher level of volatility and a lower level of return, and this happened around the global financial crisis period.Research limitations/implicationsThe findings that national real estate securities correlations exhibit time‐varying and asymmetric behavior can help investors understand how real estate securities will co‐move in different market scenarios (e.g. “crisis” and “non‐crisis” times). Moreover, the process of dynamic covariance analysis and forecasting (the ultimate objective in portfolio management) should not rely too much on short‐term autoregressive moving average models. Instead, a combination of some appropriate long‐range dependence models and regime‐switching specifications is needed.Originality/valueThis paper offers useful insights into the time series behavior of average dynamic conditional correlations in European public property markets.

Multiple Structural Breaks and Unit Root Hypothesis: Evidence from India's Service Sector

The existing relationship between financial openness and economic growth is significant for a healthy analysis of country economies. In this study, it is aimed to investigate the relationship between Turkey's financial openness and economic growth by using annual data from 1985 to 2018. In the study, Turkey's data from 1985 to 2018 were used. The variables used are economic growth (GDP), financial openness (FA) and trade openness (DA). All variables are handled in terms of current ($). Here, the natural logarithm of GDP, FA and DA variables is taken and included in the model. In the study, firstly, the stationarities of the variables were determined by Carrion-i-Silvestre et al. (2009) analyzed with unit root tests. Maki (2012) cointegration test, which allows multiple structural breaks, was used after it was determined that the variables were stationary at the I(1) level. Causality analysis between variables was tested with Hacker & Hatemi (2006) bootstrap causality test. When the structural break dates are examined, it has been determined that the economic crises in 1994, 1998 and 2001 in Turkey and the global economic crises that started in 2007 and intensified in 2008 caused structural breaks by deeply affecting the Turkish economy. By determining the cointegration relationship between the variables, it was found that they would act together in the long run. In addition, a bidirectional causality relationship was found between financial openness and economic growth. In the light of these findings, it was concluded that policy makers should focus more on financial openness when they develop policies for economic growth.

Numerous studies have estimated the linkage of economic growth and
\nenvironmental degradation in the framework of EKC theory with typical CO2
\nemissions proxy. However, the complexity of environmental degradation (ED)
\nis better measured by ecological footprint (ECF) in any geographical territory.
\nAgainst this background, the present study is an effort to contribute to the
\nexisting literature by re-investigating the EKC hypothesis with ecological
\nfootprint and CO2 emissions proxy in the largest population of the world.
\nMoreover, the role of population density is also considered with maximum data
\navailable from 1961 to 2016 for China. To estimate the said linkage, we apply first, second, and third-generation econometric approaches i. e. Augmented
\nDickey-Fuller unit root test, Zaviot Andrew’s unit root test with structural
\nbreaks, and Carrion-i-Silvestre’s general least-squares based test with several
\nstructural breaks. Likewise, the co-integration relationship is examined by
\napplying Maki’s co-integration econometric approach with multiple structural
\nbreaks. Furthermore, the autoregressive distributive lag model is applied to
\ninvestigate the long-run and short-run relationships by incorporating year
\ndummies highlighted by MBk. The results report the U-shaped EKC for China,
\nwhich means economic growth is helping to clean the environment while the
\npopulation density (PD) is found to be a cause of increasing ED. Findings have
\nrobust policy implications for China

Further evidence on property–casualty insurance premiums: Do multiple breaks and country characteristics matter?

Dependent Defaults and Losses with Factor Copula Models

In order to have a better understanding whether or not an additional observation has changed the covariance structure, a new statistic will be introduced. Under normality assumption, its asymptotic distribution will be presented and for practical purpose a chi-square approximation will be discussed. An industrial application in monitoring process variability will be presented to illustrate its advantage.

Estimating Nonlinear DSGE Models by the Simulated Method of Moments