Tests For Structural Change Research Articles

Untangling the coupling effect of water quality and quantity on lake algal blooms in Lake Hulun from a dual perspective of remote sensing and sediment cores

Algal blooms and sediment diatoms are crucial indicators of lake water ecology, influenced by water quantity and quality. However, the coupled effects of water quality and quantity changes on algal blooms are still unclear, especially for lakes in cold and arid regions. This study assessed the long-term variations in algal blooms in Hulun Lake using a novel approach combining remote sensing and sediment core samples for diatom analysis. Two mutation points from the structural change test were identified in approximately 2000 and 2010 for algal bloom area (MBE) and sediment diatom richness, indicating asynchronous algal blooms. A structural equation model (SEM) demonstrated that water level (WL) changes were the dominant influencing factor, co-driving the variations in algal blooms and sediment diatoms in conjunction with total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). The results revealed nonlinear relationships between the lake WL, TN, Chla, and COD. The water level of 543 m emerged as a critical threshold affecting the relationship between water quality and quantity. Distinct differences in this relationship were observed when water levels were above or below this threshold. These variations became particularly pronounced during periods of high and low water levels. The results provide novel insights into the dynamics of algal blooms and can further support lake ecosystem conservation and management.

Testing for distributional structural change with unknown breaks: application to pricing crop insurance contracts

Abstract Agriculture in developed countries is produced under heavily subsidized insurance. The pricing of these insurance contracts, termed premium rates, directly influences farmers profits, their financial solvency, and indirectly, global food security. Changing climate and technology have likely caused significant shifting of mass in crop yield distributions and, if so, has rendered some of the historical yield data irrelevant for estimating premium rates. Insurance is primarily interested in lower tail probabilities and as such the detection of structural change in tail probabilities or higher moments is of great concern for the efficacy of crop insurance programs. We propose a test for structural change with an unknown break(s) which has power against structural change in any moment and can be tailored to a specific range of the underlying distribution. Simulations demonstrate better finite sample performance relative to existing methods and reasonable performance at identifying the break. The asymptotic distribution is shown to follow the Kolmogorov distribution. Our proposed test finds structural change in most major U.S. field crop yields leading to significant premium rate differences. Results of an out-of-sample premium rating game indicate that incorporating structural change in crop yields leads to more accurate premium rates.

Reprint of: The likelihood ratio test for structural changes in factor models

A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but with a change in the variance of its factors. This approach effectively transforms a high-dimensional structural change problem into a low-dimensional problem. This paper considers the likelihood ratio (LR) test for a variance change in the estimated factors. The LR test implicitly explores a special feature of the estimated factors: the pre-break and post-break variances can be a singular matrix under the alternative hypothesis, making the LR test diverging faster and thus more powerful than Wald-type tests. The better power property of the LR test is also confirmed by simulations. We also consider mean changes and multiple breaks. We apply this procedure to the factor modeling of the US employment and study the structural change problem using monthly industry-level data.

The likelihood ratio test for structural changes in factor models

A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but with a change in the variance of its factors. This approach effectively transforms a high-dimensional structural change problem into a low-dimensional problem. This paper considers the likelihood ratio (LR) test for a variance change in the estimated factors. The LR test implicitly explores a special feature of the estimated factors: the pre-break and post-break variances can be a singular matrix under the alternative hypothesis, making the LR test diverging faster and thus more powerful than Wald-type tests. The better power property of the LR test is also confirmed by simulations. We also consider mean changes and multiple breaks. We apply this procedure to the factor modeling of the US employment and study the structural change problem using monthly industry-level data.

On the persistence of near-surface temperature dynamics in a warming world.

We consider issues related to the effect of climate change on the persistence of (trend-corrected) temperatures using global gridded data for both land and oceans. We first discuss how the presence of trends and additive noise affects inference about persistence. Ignoring a trend induces an upward bias, while not accounting for noise induces a downward bias. We show that the increase in persistence in the commonly used Warm Spell Duration Index is simply an artifact of increasing temperatures. To purge the impact of both trends and noise, we adopt a simple state-space model. Of separate interest, we document a much larger noise component for land than for oceans. The estimates of the persistence are much larger for oceans than for land. Inspection of the estimates across various subsamples and the application of tests for structural changes suggest the same pattern of persistence for both land and oceans across time, with few minor exceptions. Hence, our results show that surface temperature persistence has remained constant during the observed period.

Detecting Structural Changes in Time Series by Using the BDS Test Recursively: An Application to COVID-19 Effects on International Stock Markets

Structural change tests aim to identify evidence of a structural break or change in the underlying generating process of a time series. The BDS test has its origins in chaos theory and seeks to test, using the correlation integral, the hypothesis that a time series is generated by an identically and independently distributed (IID) stochastic process over time. The BDS test is already widely used as a powerful tool for testing the hypothesis of white noise in the residuals of time series models. In this paper, we illustrate how the BDS test can be implemented also in a recursive manner to evaluate the hypothesis of structural change in a time series, taking advantage of its ability to test the IID hypothesis. We apply the BDS test repeatedly, starting with a sub-sample of the original time series and incrementally increasing the number of observations until it is applied to the full sample time series. A structural change in the unknown underlying generator model is detected when a change in the trend shown by this recursively computed BDS statistic is detected. The strength of this recursive BDS test lies in the fact that it does not require making any assumptions about the underlying time series generator model. We ilustrate the power and potential of this recursive BDS test through an application to real economic data. In this sense, we apply the test to assess the structural changes caused by the COVID-19 pandemic in international financial markets. Using daily data from the world’s top stock indices, we have detected strong and statistically significant evidence of two major structural changes during the period from June 2018 to June 2022. The first occurred in March 2020, coinciding with the onset of economic restrictions in the main Western countries as a result of the pandemic. The second occurred towards the end of August 2020, with the end of the main economic restrictions and the beginning of a new post-pandemic economic scenario. This methodology to test for structural changes in a time series is easy to implement and can detect changes in any system or process behind the time series even when this generating system is not known, and without the need to specify or estimate any a priori generating model. In this sense, the recursive BDS test could be incorporated as an initial preliminary step to any exercise of time series modeling. If a structural change is detected in a time series, rather than estimating a single predictive model for the full-sample time series, efforts should be made to estimate different predictive models, one for the time before and one for the time after the detected structural change.

Structural change in feedlot cattle death loss rates.

Industry reports and anecdotal evidence indicate that the death loss rate in cattle feedlots has increased over time. Such increases in death loss rates impact feedlot cost and thus profitability. The primary objective of this study is to examine whether feedlot death loss rates in cattle have changed over time, to analyze the nature of any identified structural change, and to identify possible catalysts for that change. Data from the Kansas Feedlot Performance and Feed Cost Summary from 1992 through 2017 is used to model feedlot death loss rate as a function of feeder cattle placement weight, days on feed, time, and seasonality in the form of monthly dummy variables. Commonly used tests of structural change, including the CUSUM, CUSUMSQ, and Bai and Perron methods, are implemented to examine the existence and nature of any structural changes in the proposed model. All tests indicate the presence of structural breaks in the model, including both systematic change and abrupt change. Following a synthesis of structural test results, the final model is modified to include a structural shift parameter for the period from December 2000 to September 2010. Models indicate that days on feed has a significant positive influence on death loss rate. Trend variables indicate that death loss rates have increased systematically over the period studied. However, the structural shift parameter in the modified model is positive and significant for December 2000 to September 2010, indicating that death loss is higher on average during this period. Variance of death loss percentage is also higher during this period. Parallels between evidence of structural change and possible industry and environmental catalysts are also discussed. Statistical evidence does indicate changes in the structure of death loss rates. Ongoing factors such as changes in feeding rations prompted by market forces and feeding technologies may have contributed to systematic change. Other events, such as weather events and beta agonist use could result in abrupt changes. No clear evidence directly connects these factors to death loss rates and disaggregated data would be required to facilitate such a study.

РЕГІОНАЛЬНА ДИФЕРЕНЦІАЦІЯ СПОЖИВЧИХ ЦІН В УКРАЇНІ

Consumer prices are a litmus test for structural changes in any society. Rapid price changes are evidence of serious problems and are the result of the influence of many factors. It could be easily seen from the events in Ukraine in 2022. The occupation and then partial de-occupation of large areas of the country, and the difficulties of ensuring life in the front-line areas led to a rapid rise in prices and inflation in the entire country. The article examines the impact of a large-scale war on consumer prices by Ukraine regions in January 2022 and January 2023. For this, the methodology of collecting information on consumer prices in the territory of Ukraine and the peculiarities of determining the average values, on which the calculations in this work are based, were studied. Also, were analyzed the features of hierarchical clustering algorithms and the appropriateness of dendrograms usage. During the research, the clustering (regional differentiation) of regions by consumer prices was carried out using a hierarchical agglomerative algorithm based on the Ward method with Euclidean distance, using normalized (linearly transformed) data (prices in the specified periods). The research also includes the comparison of the formed groups with the existing division of Ukraine into economic regions. In addition, the analysis of the obtained results of the differentiation of regions, as well as the factors that influenced the sharp growth of consumer prices, and above all food products, was carried out. From the results, it can be easily seen that districts in which the country was divided by consumer prices are weakly correlated with nine economic districts in which the country is divided officially. Consumer prices are affected by factors other than those considered during the division into official districts. The uneven impact of the war on different regions led to a radical change in the division, as the growth of prices in the front-line zones exceeded even those in the capital. Especially, it refers to the Kherson region, more precisely, that part of it that was freed from the long-term occupation. In this region were recorded the highest prices in comparison with all the other regions.

식민지기 부동산등기와 재산권명시체계

1. CONTENTS (1) RESEARCH OBJECTIVES The purpose of this study is to investigate de Soto(2000)’s model of representative property system in the Colonial Korea empirically. (2) RESEARCH METHOD This study was carried out by the method of time series analysis, including unit root test, Johansen cointegration test and Granger causality as well as the structural change test. (3) RESEARCH FINDINGS In the results of this survey, Johansen cointegration test shows that property registration, collateral, bank loan and GDP are cointegrated with each other. Granger causality is exhibited from property registry to GDP, company number and manufacture output, not vice versa.. 2. RESULTS This study shows that the number of the Property Registration was drastically increased after the Land Survey Project was completed in 1918, alongside the collateral and bank loan.

Subsampling ratio tests for structural changes in time series with heavy-tailed AR(p) errors

In this article, we consider that issues related to the mean and trend of heavy-tailed AR(p) series are possibly subject to change at most once at some unknown point in time. Under the inspiration of Shao (J. Time Ser. Anal., 2011, 32, 598–606), two ratio statistics are constructed to test whether unknown changes have occurred. It is shown that asymptotic distributions of these test statistics under the no-change null hypothesis are functional for Lévy processes and their consistencies are given under the alternative. To avoid the nuisance parameter, we provide a subsampling method that returns more accurate critical values for these tests. The validity of the subsampling algorithm is proved. A simulation study shows the subsampling-based ratio tests achieve the correct empirical sizes and comparable empirical powers in large samples. Finally, two practical applications using real data set are presented.

Testing for structural changes in large dimensional factor models via discrete Fourier transform

We propose a new test for structural changes in large dimensional factor models using a discrete Fourier transform (DFT) approach. When structural changes occur, the conventional principal component analysis may fail to estimate the common factors and factor loadings consistently, and the estimated residuals contain information about the structural changes. This allows us to compare the DFT of the estimated residuals weighted by the estimated common factors with the null (zero) spectrum implied by no structural change. The proposed test is powerful against both smooth structural changes and abrupt structural breaks with an unknown number of breaks and unknown break dates in factor loadings. It can detect a class of local alternatives at the rate N−1/2T−1/2, where N and T are the numbers of cross-sectional units and time periods, respectively. Monte Carlo studies demonstrate that the proposed test has reasonable size and excellent power in detecting various structural changes in factor loadings. When applied to the U.S. macroeconomic data, the test reveals significant and robust evidence of time-varying factor loadings for the post-Great Moderation sample and the pre-Great Recession subsample, which the existing literature may fail to address.

Testing for time-varying factor loadings in high-dimensional factor models

This paper proposes a test for structural changes in factor loadings in high-dimensional factor models under weak serial and cross-sectional dependence. The test is an aggregate statistic in the form of the maximum of the variable-specific statistics whose asymptotic null distribution and local power property are studied. Two approaches including extreme value theory and Bonferroni correction are adopted to compute the critical values of the aggregate test statistic. Monte Carlo simulations reveal the non-trivial power of the proposed test against various types of structural changes, including abrupt changes, nonrandom smooth changes, random-walk variations and stationary variations. Additionally, our test can be more powerful than some alternative tests in the considered scenarios. The usefulness of the test is illustrated by an empirical application to Stock and Watson’s U.S. data set.

Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19

Dealing with structural breaks is an essential step in most empirical economic research. This is particularly true in panel data comprised of many cross-sectional units, which are all affected by major events. The COVID-19 pandemic has affected most sectors of the global economy; however, its impact on stock markets is still unclear. Most markets seem to have recovered while the pandemic is ongoing, suggesting that the relationship between stock returns and COVID-19 has been subject to structural break. It is therefore important to know if a structural break has occurred and, if it has, to infer the date of the break. Motivated by this last observation, the present article develops a new break detection toolbox that is applicable to different sized panels, easy to implement and robust to general forms of unobserved heterogeneity. The toolbox, which is the first of its kind, includes a structural change test, a break date estimator, and a break date confidence interval. Application to a panel covering 61 countries from January 3 to September 25, 2020, leads to the detection of a structural break that is dated to the first week of April. The effect of COVID-19 is negative before the break and zero thereafter, implying that while markets did react, the reaction was short-lived. A possible explanation is the quantitative easing programs announced by central banks all over the world in the second half of March.

A Note on the Structural Change Test in Highly Parameterized Psychometric Models

Equal parameter estimates across subgroups is a substantial requirement of statistical tests. Ignoring subgroup differences poses a threat to study replicability, model specification, and theory development. Structural change tests are a powerful statistical technique to assess parameter invariance. A core element of those tests is the empirical fluctuation process. In the case of parameter invariance, the fluctuation process asymptotically follows a Brownian bridge. This asymptotic assumption further provides the basis for inference. However, the empirical fluctuation process does not follow a Brownian bridge in small samples, and this situation is amplified in large psychometric models. Therefore, common methods of obtaining the sampling distribution are invalid and the structural change test becomes conservative. We discuss an alternative solution to obtaining the sampling distribution—permutation approaches. Permutation approaches estimate the sampling distribution through resampling of the dataset, avoiding distributional assumptions. Hereby, the tests power are improved. We conclude that the permutation alternative is superior to standard asymptotic approximations of the sampling distribution.

Testing for Structural Change of Predictive Regression Model to Threshold Predictive Regression Model

This article investigates two test statistics for testing structural changes and thresholds in predictive regression models. The generalized likelihood ratio (GLR) test is proposed for the stationary predictor and the generalized F test is suggested for the persistent predictor. Under the null hypothesis of no structural change and threshold, it is shown that the GLR test statistic converges to a function of a centered Gaussian process, and the generalized F test statistic converges to a function of Brownian motions. A Bootstrap method is proposed to obtain the critical values of test statistics. Simulation studies and a real example are given to assess the performances of the proposed tests.

Testing for parameter instability and structural change in persistent predictive regressions

This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the predictive relation may feature cointegration, we provide sup-Wald break tests that are constructed using the Local speCtruM (LCM) approach. The new tests cover both parameter variation and multiple structural changes with unknown break dates, and the number of breaks being known or unknown. We establish asymptotic limit theory for the tests, showing that it coincides with standard testing procedures. As a consequence, existing critical values for tied-down Bessel processes may be applied, without modification. We implement the new structural change tests to explore the stability of the fractionally cointegrating relation between implied- and realized volatility (IV and RV). Moreover, we assess the relative efficiency of IV forecasts against a challenging time-series benchmark constructed from high-frequency data. Unlike existing studies, we find evidence that the IV–RV cointegrating relation is unstable, and that carefully constructed time-series forecasts are more efficient than IV in capturing low-frequency movements in RV.

Structural change tests under heteroskedasticity: Joint estimation versus two‐steps methods

There has been a recent upsurge of interest in testing for structural changes in heteroskedastic time series, as changes in the variance invalidate the asymptotic distribution of conventional structural change tests. Several tests have been proposed that are robust to general form of heteroskedastic errors. The most popular use a two‐steps approach: first estimate the residuals assuming no changes in the regression coefficients; second, use the residuals to approximate the heteroskedastic asymptotic distribution or take an entire sample average to construct a test for which the variance process is averaged out. An alternative approach was proposed by Perron et al. (Perron et al. (2020). Quantitative Economics 11: 1019–1057) who provided a test for changes in the coefficients allowing for changes in the variance of the error term. We show that it transforms the variance profile into one that effectively has very little impact on the size of the test. With respect to the power properties, the two‐steps procedures can suffer from non‐monotonic power problems in dynamic models and in static models with a correction for serial correlation in the error. Most have power equals to size with zero‐mean regressors. Even when the two‐steps tests have power, it is generally lower than that of the latter test.

Manufacturing internationalization: from distance to proximity? A longitudinal analysis of offshoring choices

PurposeThis paper aims to offer a long-term systematic picture of the evolution of manufacturing offshoring (in terms of intensity, geography and drivers) highlighting the changes in the surrounding context and the resulting transitions points (“points in time”) that have shaped its development path.Design/methodology/approachThree statistical tools were adopted on a dataset of 644 cases. First, the authors resorted to multiple structural change tests to identify the transition points. Second, the authors explored offshoring geography by conducting a network analysis. Finally, the authors adopted gravity models to shed light on offshoring drivers.FindingsResults highlight three offshoring phases: expansion (2002–2006), reconsideration (2007–2009) and rationalization (2010 onwards). During the first phase, characterized by economic growth, firms were mainly interested in economic savings; offshoring to low-cost countries was the prevailing location strategy. Subsequently, during the economic crisis, the number of cases declined and the main drivers became market-based factors together with the research for cost savings. Finally, in the third phase, when the economy was still stagnating and new manufacturing technologies appeared, the number of offshoring cases has further decreased, and technological- and market-based factors have become the main location drivers.Originality/valueThe study is the first to adopt a systematic, empirical and quantitative approach to analyze the evolution of the manufacturing offshoring considering both the phenomenon itself and the triggering changes in the surrounding context. In doing this, the authors also tested the importance of considering the point in time in offshoring strategies.

The great moderation: updated evidence with joint tests for multiple structural changes in variance and persistence

We assess the empirical evidence about the great moderation using a comprehensive framework to test for multiple structural changes in the coefficients and in the variance of the error term of a linear regression model provided by Perron et al. (Quant Econ 11:1019–1057, 2020). We apply it to the US real GDP and its major components for the period 1960:1 to 2018:4. A notable feature of our approach is that we adopt an unobserved component model, allowing for two breaks in the trend function in 1973:1 and 2008:1, in order to obtain a stationary or cyclical component modelled as an autoregressive process. First, we confirm evidence about the great moderation, i.e., a structural change in variance of the errors in the mid-80s for the various series. Second, additional breaks in variance are found in 1970:3 for GDP and production (goods), after which the sample variance increased by three times. Hence, a part of the great moderation can be viewed as a reversion to the 1960–1970 level of volatility. Third, the evidence about systematic changes in the sum of the autoregressive coefficients (a measure of persistence) is weak over the whole sample period. Finally, we find little evidence of structural changes occurring in both the variance and the coefficients following the great recession (2007–2008). These results support views emphasizing the “good luck” hypothesis as a source of the great moderation, which continues even after the great recession.

Bootstrapping non-stationary stochastic volatility

In this paper we investigate to what extent the bootstrap can be applied to conditional mean models, such as regression or time series models, when the volatility of the innovations is random and possibly non-stationary. In fact, the volatility of many economic and financial time series displays persistent changes and possible non-stationarity. However, the theory of the bootstrap for such models has focused on deterministic changes of the unconditional variance and little is known about the performance and the validity of the bootstrap when the volatility is driven by a non-stationary stochastic process. This includes near-integrated exogenous volatility processes as well as near-integrated GARCH processes, where the conditional variance has a diffusion limit; a further important example is the case where volatility exhibits infrequent jumps. This paper fills this gap in the literature by developing conditions for bootstrap validity in time series and regression models with non-stationary, stochastic volatility. We show that in such cases the distribution of bootstrap statistics (conditional on the data) is random in the limit. Consequently, the conventional approaches to proofs of bootstrap consistency, based on the notion of weak convergence in probability of the bootstrap statistic, fail to deliver the required validity results. Instead, we use the concept of ‘weak convergence in distribution’ to develop and establish novel conditions for validity of the wild bootstrap, conditional on the volatility process. We apply our results to several testing problems in the presence of non-stationary stochastic volatility, including testing in a location model, testing for structural change using CUSUM-type functionals, and testing for a unit root in autoregressive models. Importantly, we work under sufficient conditions for bootstrap validity that include the absence of statistical leverage effects, i.e., correlation between the error process and its future conditional variance. The results of the paper are illustrated using Monte Carlo simulations, which indicate that a wild bootstrap approach leads to size control even in small samples.