Nonparametric rank tests for non-stationary panels

We develop a set of nonparametric rank tests for non-stationary panels based on multivariate variance ratios which use untruncated kernels. As such, the tests do not require the choice of tuning parameters associated with bandwidth or lag length and also do not require choices with respect to numbers of common factors. The tests allow for unrestricted cross-sectional dependence and dynamic heterogeneity among the units of the panel, provided simply that a joint functional central limit theorem holds for the panel of differenced series. We provide a discussion of the relationships between our setting and the settings for which first- and second generation panel unit root tests are designed. In Monte Carlo simulations we illustrate the small-sample performance of our tests when they are used as panel unit root tests under the more restrictive DGPs for which panel unit root tests are typically designed, and for more general DGPs we also compare the small-sample performance of our nonparametric tests to parametric rank tests. Finally, we provide an empirical illustration by testing for income convergence among countries.

The effect of recursive detrending on panel unit root tests

This paper analyzes the properties of panel unit root tests based on recursively detrended data. The analysis is conducted while allowing for a (potentially) non-linear trend function, which represents a more general consideration than the current state of affairs with (at most) a linear trend. A new test statistic is proposed whose asymptotic behavior under the unit root null hypothesis, and the simplifying assumptions of a polynomial trend and iid errors are shown to be surprisingly simple. Indeed, the test statistic is not only asymptotically independent of the true trend polynomial, but also is in fact unique in that it is independent also of the degree of the fitted polynomial. However, this invariance property does not carry over to the local alternative, under which it is shown that local power is a decreasing function of the trend degree. But while power does decrease, the rate of shrinking of the local alternative is generally constant in the trend degree, which goes against the common belief that the rate of shrinking should be decreasing in the trend degree. The above results are based on simplifying assumptions. To compensate for this lack of generality, a second, robust, test statistic is proposed, whose validity does not require that the trend function is a polynomial or that the errors are iid.

On the importance of the first observation in GLS detrending in unit root testing

First-differencing is generally taken to imply the loss of one observation, the first, or at least that the effect of ignoring this observation is asymptotically negligible. However, this is not always true, as in the case of generalized least squares (GLS) detrending. In order to illustrate this, the current article considers as an example the use of GLS detrended data when testing for a unit root. The results show that the treatment of the first observation is absolutely crucial for test performance, and that ignorance causes test break-down.

Pooled panel unit root tests and the effect of past initialization

This paper analyzes the role of initialization when testing for a unit root in panel data, an issue that has received surprisingly little attention in the literature. In fact, most studies assume that the initial value is either zero or bounded. As a response to this, the current paper considers a model in which the initialization is in the past, which is shown to have several distinctive features that makes it attractive, even in comparison to the common time series practice of making the initial value a draw from its unconditional distribution under the stationary alternative. The results have implications not only for theory, but also for applied work. In particular, and in contrast to the time series case, in panels the effect of the initialization need not be negative but can actually lead to improved test performance.

Testing slope homogeneity in large panels with serial correlation

Pesaran and Yamagata (Pesaran, M.H., Yamagata, T., Testing slope homogeneity in large panels, Journal of Econometrics 142, 50-93, 2008) propose a test for slope homogeneity in large panels, which has become very popular in the literature. However, the test cannot deal with the practically relevant case of heteroskedastic and/serially correlated errors. The present note proposes a generalized test that accommodates both features. © 2013 Elsevier B.V.

Farmland prices, structural breaks and panel data

Previous time series evidence has indicated that farmland prices and cash rents are not cointegrated, a finding at odds with the present value model of farmland prices. We argue that this failure to find cointegration may be due to low power of tests and to the presence of structural change representing a shifting risk premium on farmland investments. To accommodate this possibility, we use panel unit root and cointegration methods that are more powerful than conventional time series methods and allow for breaks in the cointegration relationship. Our results, based on a large panel covering 31 US states between 1960 and 2000, suggest that the present value model of farmland prices cannot be rejected. © Oxford University Press and Foundation for the European Review of Agricultural Economics 2007; all rights reserved.

Is there really a unit root in the inflation rate? More evidence from panel data models

Time series unit root evidence suggests that inflation is nonstationary. By contrast, when using more powerful panel unit root tests, Culver and Papell (1997) find that inflation is stationary. In this article, we test the robustness of this result by applying a battery of recent panel unit root tests. The results suggest that the stationarity of inflation holds even after controlling for cross-sectional dependence and structural change.

Developing a primary care patient measure of safety (PC PMOS): a modified Delphi process and face validity testing

BACKGROUND: Patients are a valuable source of information about ways to prevent harm in primary care and are in a unique position to provide feedback about the factors that contribute to safety incidents. Unlike in the hospital setting, there are currently no tools that allow the systematic capture of this information from patients. The aim of this study was to develop a quantitative primary care patient measure of safety (PC PMOS). METHODS: A two-stage approach was undertaken to develop questionnaire domains and items. Stage 1 involved a modified Delphi process. An expert panel reached consensus on domains and items based on three sources of information (validated hospital PMOS, previous research conducted by our study team and literature on threats to patient safety). Stage 2 involved testing the face validity of the questionnaire developed during stage 1 with patients and primary care staff using the 'think aloud' method. Following this process, the questionnaire was revised accordingly. RESULTS: The PC PMOS was received positively by both patients and staff during face validity testing. Barriers to completion included the length, relevance and clarity of questions. The final PC PMOS consisted of 50 items across 15 domains. The contributory factors to safety incidents centred on communication, access to care, patient-related factors, organisation and care planning, task performance and information flow. DISCUSSION: This is the first tool specifically designed for primary care settings, which allows patients to provide feedback about factors contributing to potential safety incidents. The PC PMOS provides a way for primary care organisations to learn about safety from the patient perspective and make service improvements with the aim of reducing harm in this setting. Future research will explore the reliability and construct validity of the PC PMOS.

Testing for predictability in panels of any time series dimension

The few panel data tests for the predictability of returns that exist are based on the prerequisite that both the number of time series observations, T, and the number of cross-section units, N, are large. As a result, it is impossible to apply these tests to stock markets, where lengthy time series of data are scarce. In response to this, the current paper develops a new test for predictability in panels where N is large and T≥. 2 can be either small or large, or indeed anything in between. This consideration represents an advancement relative to the usual large-. N and large-. T requirement. The new test is also very general, especially when it comes to allowable predictors, and is easy to implement. As an illustration, we consider the Chinese stock market, for which data are available for only 17 years, but where the number of firms is relatively large, 160.

Testing for predictability in panels with general predictors

The difficulty of predicting returns has recently motivated researchers to start looking for tests that are either more powerful or robust to more features of the data. Unfortunately, the way that these tests work typically involves trading robustness for power or vice versa. The current paper takes this as its starting point to develop a new panel-based approach to predictability that is both robust and powerful. Specifically, while the panel route to increased power is not new, the way in which the cross-section variation is exploited also to achieve robustness with respect to the predictor is. The result is two new tests that enable asymptotically standard normal and chi-squared inference across a wide range of empirically relevant scenarios in which the predictor may be stationary, moderately non-stationary, nearly non-stationary, or indeed unit root non-stationary. The type of cross-section dependence that can be permitted in the predictor is also very general, and can be weak or strong, although we do require that the cross-section dependence in the regression errors is of the strong form. What is more, this generality comes at no cost in terms of complicated test construction. The new tests are therefore very user-friendly.

Persistent and contemporaneous effects of job stressors on mental health: a study testing multiple analytic approaches across 13 waves of annually collected cohort data

OBJECTIVES: This study investigated the extent that psychosocial job stressors had lasting effects on a scaled measure of mental health. We applied econometric approaches to a longitudinal cohort to: (1) control for unmeasured individual effects; (2) assess the role of prior (lagged) exposures of job stressors on mental health and (3) the persistence of mental health.

METHODS: We used a panel study with 13 annual waves and applied fixed-effects, first-difference and fixed-effects Arellano-Bond models. The Short Form 36 (SF-36) Mental Health Component Summary score was the outcome variable and the key exposures included: job control, job demands, job insecurity and fairness of pay.

RESULTS: Results from the Arellano-Bond models suggest that greater fairness of pay (β-coefficient 0.34, 95% CI 0.23 to 0.45), job control (β-coefficient 0.15, 95% CI 0.10 to 0.20) and job security (β-coefficient 0.37, 95% CI 0.32 to 0.42) were contemporaneously associated with better mental health. Similar results were found for the fixed-effects and first-difference models. The Arellano-Bond model also showed persistent effects of individual mental health, whereby individuals' previous reports of mental health were related to their reporting in subsequent waves. The estimated long-run impact of job demands on mental health increased after accounting for time-related dynamics, while there were more minimal impacts for the other job stressor variables.

CONCLUSIONS: Our results showed that the majority of the effects of psychosocial job stressors on a scaled measure of mental health are contemporaneous except for job demands where accounting for the lagged dynamics was important.

Error correction testing in panels with common stochastic trends

This paper develops panel data tests for the null hypothesis of no error correction in a model with common stochastic trends. The asymptotic distributions of the new test statistics are derived and simulation results are provided to suggest that they perform well in small samples. Copyright © 2015 John Wiley & Sons, Ltd.