Changes In Panel Data Research Articles

A combined SR-CUSUM procedure for detecting common changes in panel data

A procedure based on the sum of N Shiryayev–Roberts processes is proposed to detect common changes in panel data and shown to perform better for small portions of changed panels. The change-point for each panel is estimated by using the CUSUM process calculated in parallel. The changed panels are isolated by using the scores formed by the post-change parameter estimations and the common change point is then estimated from the isolated changed panels. A real example is used for illustration. An adaptive detection procedures is also proposed when the unknown post-change parameters are estimated adaptively in each panel.

Change Point in Panel Data with Small Fixed Panel Size: Ratio and Non-Ratio Test Statistics

The main goal is to develop and, consequently, compare stochastic methods for detection whether a structural change in panel data occurred at some unknown time or not. Panel data of our interest consist of a moderate or relatively large number of panels, while the panels contain a small number of observations. Testing procedures to detect a possible common change in means of the panels are established. Ratio and non-ratio type test statistics are considered. Their asymptotic distributions under the no change null hypothesis are derived. Moreover, we prove the consistency of the tests under the alternative. The main advantage of the ratio type statistics compared to the non-ratio ones is that the variance of the observations neither has to be known nor estimated. A simulation study reveals that the proposed ratio statistic outperforms the non-ratio one by keeping the significance level under the null, mainly when stronger dependence within the panel is taken into account. However, the non-ratio statistic rejects the null in the simulations more often than it should, which yields higher power compared to the ratio statistic.

Erratum to: Testing structural changes in panel data with small fixed panel size and bootstrap

In the original publication, the form of the change point estimate presented in Section 4 is not suitable. The correct form of the estimate with correct assertion of Theorem 3 is given below. The corrected version of the original paper can be found on arXiv http://arxiv.org/abs/1509.01291. Having a sequence of weights {w(t)}T t=2, let us define the estimate of τ as τN := arg min t=2,...,T 1 w(t) N ∑

Impact of mixed modes on measurement errors and estimates of change in panel data

Mixed mode designs are receiving increased interest as a possible solution for saving costs in panel surveys, although the lasting effects on data quality are unknown. To better understand the effects of mixed mode designs on panel data we will examine its impact on random and systematic error and on estimates of change. The SF12, a health scale, in the Understanding Society Innovation Panel is used for the analysis. Results indicate that only one variable out of 12 has systematic differences due to the mixed mode design. Also, four of the 12 items overestimate variance of change in time in the mixed mode design. We conclude that using a mixed mode approach leads to minor measurement differences but it can result in the overestimation of individual change compared to a single mode design.

Testing structural changes in panel data with small fixed panel size and bootstrap

Panel data of our interest consist of a moderate or relatively large number of panels, while the panels contain a small number of observations. This paper establishes testing procedures to detect a possible common change in means of the panels. To this end, we consider a ratio type test statistic and derive its asymptotic distribution under the no change null hypothesis. Moreover, we prove the consistency of the test under the alternative. The main advantage of such an approach is that the variance of the observations neither has to be known nor estimated. On the other hand, the correlation structure is required to be calculated. To overcome this issue, a bootstrap technique is proposed in the way of a completely data driven approach without any tuning parameters. The validity of the bootstrap algorithm is shown. As a by-product of the developed tests, we introduce a common break point estimate and prove its consistency. The results are illustrated through a simulation study. An application of the procedure to actuarial data is presented.

The Retirement-Consumption Puzzle: Actual Spending Change in Panel Data

The simple one-good model of life-cycle consumption requires that consumption be continuous over retirement; yet prior research based on partial measures of consumption or on synthetic panels indicates that spending drops at retirement, a result that has been called the retirement-consumption puzzle. Using panel data on total spending, nondurable spending and food spending, we find that spending declines at small rates over retirement, at rates that could be explained by mechanisms such as the cessation of work-related expenses, unexpected retirement due to a health shock or by the substitution of time for spending. In the low-wealth population where spending did decline at higher rates, the main explanation for the decline appears to be a high rate of early retirement due to poor health. We conclude that at the population level there is no retirement consumption puzzle in our data, and that in subpopulations where there were substantial declines, conventional economic theory can provide the main explanation.

Are Nominal Wage Changes Skewed Away From Wage Cuts?

This article sheds light on an important question for macroeconomic policy and economic theory: Are nominal wage changes skewed away from wage cuts? In particular, does downward nominal rigidity censor some would be wage cuts, transforming some wage changes that would be negative into zero-wage changes? To answer this question, the author documents key properties of the distribution of wage changes in panel data. 

Testing for Structural Change in Panel Data: Application to a Study of U.S. Foreign Trade in Manufacturing Goods

A multivariate cusum test, along with extensions for partitioned parameters, serial correlation, and error components, is developed for panel data analysis. These techniques are then applied to a study of U.S. foreign trade in manufacturing goods examining changes in the determinants, i.e., the relationships between the factor contents and the net exports. Copyright 1989 by MIT Press.