Disentangling Structural Breaks in Factor Models for Macroeconomic Data*

This paper proposes a new testing approach for panel unit roots that is, unlike previously suggested tests, robust to nonstationarity in the volatility process of the innovations of the time series in the panel. Nonstationarity volatility arises for instance when there are structural breaks in the innovation variances. A prominent example is the reduction in GDP growth variances enjoyed by many industrialized countries, known as the ‘Great Moderation.’ The panel test is based on Simes’ [Biometrika 1986, \An Improved Bonferroni Procedure for Multiple Tests of Signicance] classical multiple test, which combines evidence from time series unit root tests of the series in the panel. As time series unit root tests, we employ recently proposed tests of Cavaliere and Taylor [Journal of Time Series Analysis, \Time-Transformed Unit Root Tests for Models with Non-Stationary Volatility]. The panel test is robust to general patterns of cross-sectional dependence and yet straightforward to implement, only requiring valid p-values of time series unit root tests, and no resampling. Monte Carlo experiments show that other panel unit root tests suer from sometimes severe size distortions in the presence of nonstationary volatility, and that this defect can be remedied using the test proposed here. The new test is applied to test for a unit root in an OECD panel of gross domestic products, yielding inference robust to the ‘Great Moderation.’ We nd little evidence of trend stationarity.

This paper argues that typical applications of panel unit root tests should take possible nonstationarity in the volatility process of the innovations of the panel time series into account. Nonstationarity volatility arises for instance when there are structural breaks in the innovation variances. A prominent example is the reduction in GDP growth variances enjoyed by many industrialized countries, known as the “Great Moderation”. It also proposes a new testing approach for panel unit roots that is, unlike many previously suggested tests, robust to such volatility processes. The panel test is based on Simes' (1986) classical multiple test, which combines evidence from time series unit root tests of the series in the panel. As time series unit root tests, we employ recently proposed tests of Cavaliere and Taylor (2008b). The panel test is robust to general patterns of cross-sectional dependence and yet is straightforward to implement, only requiring valid p-values of time series unit root tests, and no resampling. Monte Carlo experiments show that other panel unit root tests suff er from sometimes severe size distortions in the presence of nonstationary volatility, and that this defect can be remedied using the test proposed here. We use the methods developed here to test for unit roots in OECD panels of gross domestic products and inflation rates, yielding inference robust to the “Great Moderation”. We find little evidence of trend stationarity, and mixed evidence regarding inflation stationarity.

Unstable correspondence between salivary testosterone measured with enzyme immunoassays and tandem mass spectrometry

This study examines the effect of the Great Moderation on the relationship between U.S. output growth and its volatility over the period 1947 to 2006. First, we consider the possible effects of structural changes in the volatility process. We employ generalized autoregressive conditional heteroscedasticity in mean (GARCH‐M) specifications, which describe output growth rate and its volatility with and without a one‐time structural break in volatility. Second, our data analyses and empirical results suggest no significant relationship between the output growth rate and its volatility; this favors the traditional wisdom of dichotomy in macroeconomics. Moreover, the evidence shows that the time‐varying variance falls sharply or even disappears once we incorporate a one‐time structural break in the unconditional variance of output starting in 1982 or 1984. That is, the integrated GARCH effect proves spurious. Finally, a joint test of a trend change and a one‐time shift in the volatility process finds that the one‐time shift dominates.

This study examines the relationship between U.S. output growth and its volatility over the period 1875:Q1 to 2008:Q2. We examine the data for outliers and apply corrections when found. Next, we search for possible effects of structural breaks in the growth rate and its volatility. In so doing, we employ autoregressive generalized conditional heteroskedasticity and autoregressive exponential general conditional heteroskedasticity specifications of the process describing output growth rate and its volatility with and without structural breaks in the mean and volatility processes. We discover one break in the mean process – 1936:Q2 – and three breaks in the volatility process – 1916:Q4, 1950:Q3, and 1983:Q4 (or 1984:Q3). After accommodating the breaks in the mean and volatility processes, the integrated generalized autoregressive conditional heteroskedasticity effect proves spurious. Finally, our data analyses and empirical results suggest that higher output-growth volatility stimulates output growth and that higher output growth reduces its volatility. Moreover, the evidence shows that the time-varying variance falls sharply once we incorporate the three structural breaks in the unconditional variance of output.

From time to time, economies undergo far-reaching structural changes. In this paper we investigate the consequences of structural breaks in the factor loadings for the specification and estimation of factor models based on principal components and suggest test procedures for structural breaks. It is shown that structural breaks severely inflate the number of factors identified by the usual information criteria. Based on the strict factor model the hypothesis of a structural break is tested by using Likelihood-Ratio, Lagrange-Multiplier and Wald statistics. The LM test which is shown to perform best in our Monte Carlo simulations, is generalized to factor models where the common factors and idiosyncratic components are serially correlated. We also apply the suggested test procedure to a US dataset used in Stock and Watson (2005) and a euro-area dataset described in Altissimo et al. (2007). We find evidence that the beginning of the so-called Great Moderation in the US as well as the Maastricht treaty and the handover of monetary policy from the European national central banks to the ECB coincide with structural breaks in the factor loadings. Ignoring these breaks may yield misleading results if the empirical analysis focuses on the interpretation of common factors or on the transmission of common shocks to the variables of interest.

The study examined and compared the latent structure of posttraumatic growth (PTG) based on three proposed models: 1-factor, 3-factor and 5-factor models in order to (1) find out the factor structure that has the best fit for the Filipino sample; (2) find out the factor structure that best represents PTG in the immediate aftermath of a flash flood disaster; and (3) examine the generalizability of the best-fitted model across gender. A sample of 895 survivor-respondents answered the Posttraumatic Growth Inventory (PTGI) within a month after a deadly flash flood. Based on the best-fitted model, a multi-group comparison between male and female was conducted to determine gender generalizability. Results showed that the 3-factor model comprising of Changes in Self/Positive Life Attitudes, Philosophy of Life, and Relating to Others fitted best in contrast to the other two models. The data also demonstrated the generalizability of the 3-factor model across gender, with invariance in factor loadings, item intercepts, factor variance and covariance, and factor means.

The idea about structural break in unit root hypothesis under time series model had received great amount of attention over many last decades. The importance of structural break in the mean had been comprehensively studied by Perron [15], Perron and Vogelsang [17], Zivot and Andrews [25] etc. This had also studied in considering of break in variance by Kim et al. [9], Cook [6], Kumar et al. [11] etc. There is sufficient contribution regarding break in mean and variance individually but both are equally important and this was little explored by Bai [1] for panel data and Meligkotsidou et al. [14] for univariate time series. In present paper, we are extending this on panel dataAR(1) time series model under Bayesian framework. Posterior odds ratio has been derived for various models with and without break in mean, variance and both in consideration of unit root hypothesis. A simulation as well as an empirical analysis is also carried out to get more generalized view on the model under study.

B - 96 Assessing the Factor Structure of Social Connectedness and its Psychopathological Correlates.

A state-level analysis of the Great Moderation

Dry bulk shipping is an important element of international trade. However, as dry bulk shipping market is always influenced by unanticipated economic, financial or political events, structural breaks exist in vessel price volatility, while the price volatility characteristics under structural breaks have not been well identified. This paper aims to investigate the vessel price volatility under structural breaks caused by external events. The modified ICSS algorithm is applied to detect the structural break points and GARCH model incorporating structural breaks is used to examine the volatility of newbuilding and second-hand vessel prices of four dry bulk vessel types using monthly data from January 2002 to November 2012. Results show that the price volatility's persistence effect and clustering effect decrease when structural breaks in variance are accounted. The findings may contribute to the literature in vessel price volatility modelling under structural breaks and may be helpful for practitioners.

The present study addresses issues of measurement invariance and comparability of factor parameters of Big Five personality adjective items across age. Data from the Midlife in the United States (MIDUS) survey were used to investigate age-related developmental psychometrics of the MIDUS personality adjective items in 2 large cross-sectional samples (exploratory sample: N = 862; analysis sample: N = 3,000). After having established and replicated a comprehensive 5-factor structure of the measure, increasing levels of measurement invariance were tested across 10 age groups. Results indicate that the measure demonstrates strict measurement invariance in terms of number of factors and factor loadings. Also, we found that factor variances and covariances were equal across age groups. By contrast, a number of age-related factor mean differences emerged. The practical implications of these results are discussed, and future research is suggested.

Supportive interactions on social media have great potential to benefit adolescents’ development. However, there is no instrument to measure online social support (OSS) in China. The study aimed to develop and validate a Chinese short version of the Online Social Support Scale (OSSS). The original scale was translated into Chinese through multiple forward and backward translation protocols. The calibration sample (N = 262) was used to select items and test the reliability, validity, and internal structure of the short form. The cross-validation sample (N = 267) was then used to assess measurement invariance by multigroup confirmatory factor analysis and examine criterion validity based on its relationships with life satisfaction, depression, and time on social media. The 20-item Chinese short version of OSSS (OSSS-CS) includes four factors: esteem/emotional support, social companionship, informational support, and instrumental support. Our results suggest that the OSSS-CS has high internal consistency, construct validity, and criterion validity. Furthermore, evidence of partial cross-validity demonstrated invariance of the variance–covariance matrices, factor structure, factor loadings, and factor variance across independent samples. The results also revealed that the original OSSS could be replicated across cultures. Finally, the short form developed in the study can be used as a reliable and valid measure of online social support among the Chinese adolescent population.

Examining posttraumatic stress disorder's latent structure between treatment-seeking and non-treatment-seeking Filipinos

A statistical model is developed for the study of similarities and differences in factor structure between several groups. The model assumes that the observed variables satisfy a factor analysis model in each group. A method of data analysis is presented which, in contrast to earlier work, makes use of information in the observed means as well as the observed variances and covariances to estimate the parameters in each group, i.e. factor means, factor loadings, factor variances and covariances and unique variances. Usually the units of measurement in the observed variables have no intrinsic meaning and therefore it is only meaningful to compare the relative magnitudes of the parameters for the different groups. The method estimates the parameters for all groups simultaneously and can take into account a priori information about factorial invariance of various degrees.