923 resultados para panel stationarity test


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In this paper, we extend the heterogeneous panel data stationarity test of Hadri [Econometrics Journal, Vol. 3 (2000) pp. 148–161] to the cases where breaks are taken into account. Four models with different patterns of breaks under the null hypothesis are specified. Two of the models have been already proposed by Carrion-i-Silvestre et al.[Econometrics Journal,Vol. 8 (2005) pp. 159–175]. The moments of the statistics corresponding to the four models are derived in closed form via characteristic functions.We also provide the exact moments of a modified statistic that do not asymptotically depend on the location of the break point under the null hypothesis. The cases where the break point is unknown are also considered. For the model with breaks in the level and no time trend and for the model with breaks in the level and in the time trend, Carrion-i-Silvestre et al. [Econometrics Journal, Vol. 8 (2005) pp. 159–175]showed that the number of breaks and their positions may be allowed to differ acrossindividuals for cases with known and unknown breaks. Their results can easily be extended to the proposed modified statistic. The asymptotic distributions of all the statistics proposed are derived under the null hypothesis and are shown to be normally distributed. We show by simulations that our suggested tests have in general good performance in finite samples except the modified test. In an empirical application to the consumer prices of 22 OECD countries during the period from 1953 to 2003, we found evidence of stationarity once a structural break and cross-sectional dependence are accommodated.

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This article applies the panel stationarity test with a break proposed by Hadri and Rao (2008) to examine whether 14 macroeconomic variables of OECD countries can be best represented as random walk or stationary fluctuations around a deterministic trend. In contrast to previous studies, based essentially on visual inspection of the break type or just applying the most general break model, we use a model selection procedure based on BIC. We do this for each time series so that heterogeneous break models are allowed for in the panel. Our results suggest, overwhelmingly, that if we account for a structural break, cross-sectional dependence and choose the break models to be congruent with the data, then the null of stationarity cannot be rejected for all the 14 macroeconomic variables examined in this article. This is in sharp contrast with the results obtained by Hurlin (2004), using the same data but a different methodology.

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This paper applies stationarity tests to examine evidence of market integration for a relatively large sample of food products in Colombia. We Önd little support for market integration when using the univariate KPSS tests for stationarity. However, within a panel context and after allowing for cross sectional dependence, the Hadri tests provide much more evidence supporting the view that food markets are integrated or, in other words, that the law of one price holds for most products.

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We investigate for 26 OECD economies whether their current account imbalances to GDP are driven by stochastic trends. Regarding bounded stationarity as the more natural counterpart of sustainability, results from Phillips–Perron tests for unit root and bounded unit root processes are contrasted. While the former hint at stationarity of current account imbalances for 12 economies, the latter indicate bounded stationarity for only six economies. Through panel-based test statistics, current account imbalances are diagnosed as bounded non-stationary. Thus, (spurious) rejections of the unit root hypothesis might be due to the existence of bounds reflecting hidden policy controls or financial crises.

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The main objetive of this research is to evaluate the long term relationship between energy consumption and GDP for some Latin American countries in the period 1980-2009 -- The estimation has been done through the non-stationary panel approach, using the production function in order to control other sources of GDP variation, such as capital and labor -- In addition to this, a panel unit root tests are used in order to identify the non-stationarity of these variables, followed by the application of panel cointegration test proposed by Pedroni (2004) to avoid a spurious regression (Entorf, 1997; Kao, 1999)

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Les simulations et figures ont été réalisées avec le logiciel R.

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The aim of this work was to encapsulate casein hydrolysate by spray drying with soybean protein isolate (SPI) as wall material to attenuate the bitter taste of that product. Two treatments were prepared: both with 12 g/100 g solids and containing either two proportions of SPI: hydrolysate (70:30 and 80:20), called M1 and M2, respectively. The samples were evaluated for morphological characteristics (SEM), particle size, hygroscopicity, solubility, hydrophobicity, thermal behavior and bitter taste with a trained sensory panel using a paired-comparison test (non-encapsulated samples vs. encapsulated samples). Microcapsules had a continuous wall, many concavities, and no porosity. Treatments M1 and M2 presented average particle sizes of 11.32 and 9.18 mu m, respectively. The wall material and/or the microencapsulation raised the hygroscopicity of the hydrolysate since the free hydrolysate had hygroscopicity of 53 g of water/100 g of solids and M1 and M2 had 106.99 and 102.19 g of water/100 g of solids, respectively. However, the hydrophobicity decreases, the absence of a peak in encapsulated hydrolysates, and the results of the panel sensory test considering the encapsulated samples less bitter (p < 0.05) than the non-encapsulated, showed that spray drying with SPI was an efficient method for microencapsulation and attenuation of the bitter taste of the casein hydrolysate. (c) 2008 Elsevier Ltd. All rights reserved.

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To derive tests for randomness, nonlinear-independence, and stationarity, we combine surrogates with a nonlinear prediction error, a nonlinear interdependence measure, and linear variability measures, respectively. We apply these tests to intracranial electroencephalographic recordings (EEG) from patients suffering from pharmacoresistant focal-onset epilepsy. These recordings had been performed prior to and independent from our study as part of the epilepsy diagnostics. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control. This allowed us to define two distinct sets of signals: One set of signals recorded from brain areas where the first ictal EEG signal changes were detected as judged by expert visual inspection ("focal signals") and one set of signals recorded from brain areas that were not involved at seizure onset ("nonfocal signals"). We find more rejections for both the randomness and the nonlinear-independence test for focal versus nonfocal signals. In contrast more rejections of the stationarity test are found for nonfocal signals. Furthermore, while for nonfocal signals the rejection of the stationarity test increases the rejection probability of the randomness and nonlinear-independence test substantially, we find a much weaker influence for the focal signals. In consequence, the contrast between the focal and nonfocal signals obtained from the randomness and nonlinear-independence test is further enhanced when we exclude signals for which the stationarity test is rejected. To study the dependence between the randomness and nonlinear-independence test we include only focal signals for which the stationarity test is not rejected. We show that the rejection of these two tests correlates across signals. The rejection of either test is, however, neither necessary nor sufficient for the rejection of the other test. Thus, our results suggest that EEG signals from epileptogenic brain areas are less random, more nonlinear-dependent, and more stationary compared to signals recorded from nonepileptogenic brain areas. We provide the data, source code, and detailed results in the public domain.

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El proyecto consiste en determinar las coordenadas de un panel de test por diferentes métodos y comparar los resultados obtenidos mediante calidad posicional. Los métodos serán los siguientes: - Por topografía clásica - Por fotogrametría o Una cámara convencional (cámara primaria) realizará una pasada con tres fotografías del panel de test, se orientarán los dos pares y se restituirán para obtener coordenadas. o Midiendo una serie de marcas de la cámara primaria que serán transformadas desde otro sistema de coordenadas al sistema test podremos calcular los parámetros de orientación externa por métodos geométricos. o Otra cámara convencional (cámara secundaria) realizará tres pasadas con tres fotografías cada una de la cámara primaria para medir fotogramétricamente las marcas de esta cámara y así poder repetir el proceso de orientación directa. El resultado final de la orientación directa no ha sido válido ya que no hemos podido obtener resultados por este método. Como posibles soluciones e propone utilizar un sensor de giro o hacer un estudio en un futuro proyecto fin de carrera.

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DSDP 160 forms part of a series of sites in the eastern equatorial Pacific on the west flank of the East Pacific Rise. Earlier legs of the Deep Sea Drilling Project, in particular Legs 5 and 9, have reported sediments rich in oxides of iron and perhaps other transition metals just above basement in the eastern Pacific. These occurrences roughly define a broad zone on the west flank of the rise. Site DSDP 160 lies on this trend and were selected by the Pacific Site Selection Panel to test the extent of such deposits.

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This paper investigates the performance of the tests proposed by Hadri and by Hadri and Larsson for testing for stationarity in heterogeneous panel data under model misspecification. The panel tests are based on the well known KPSS test (cf. Kwiatkowski et al.) which considers two models: stationarity around a deterministic level and stationarity around a deterministic trend. There is no study, as far as we know, on the statistical properties of the test when the wrong model is used. We also consider the case of the simultaneous presence of the two types of models in a panel. We employ two asymptotics: joint asymptotic, T, N -> infinity simultaneously, and T fixed and N allowed to grow indefinitely. We use Monte Carlo experiments to investigate the effects of misspecification in sample sizes usually used in practice. The results indicate that the assumption that T is fixed rather than asymptotic leads to tests that have less size distortions, particularly for relatively small T with large N panels (micro-panels) than the tests derived under the joint asymptotics. We also find that choosing a deterministic trend when a deterministic level is true does not significantly affect the properties of the test. But, choosing a deterministic level when a deterministic trend is true leads to extreme over-rejections. Therefore, when unsure about which model has generated the data, it is suggested to use the model with a trend. We also propose a new statistic for testing for stationarity in mixed panel data where the mixture is known. The performance of this new test is very good for both cases of T asymptotic and T fixed. The statistic for T asymptotic is slightly undersized when T is very small (

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In this paper, we test the Prebish-Singer (PS) hypothesis, which states that real commodity prices decline in the long run, using two recent powerful panel data stationarity tests accounting for cross-sectional dependence and a structural break. We find that the hypothesis cannot be rejected for most commodities other than oil.

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Most panel unit root tests are designed to test the joint null hypothesis of a unit root for each individual series in a panel. After a rejection, it will often be of interest to identify which series can be deemed to be stationary and which series can be deemed nonstationary. Researchers will sometimes carry out this classification on the basis of n individual (univariate) unit root tests based on some ad hoc significance level. In this paper, we demonstrate how to use the false discovery rate (FDR) in evaluating I(1)=I(0) classifications based on individual unit root tests when the size of the cross section (n) and time series (T) dimensions are large. We report results from a simulation experiment and illustrate the methods on two data sets.

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This paper uses panel unit root and cointegration methods to test the stationarity of the premium on domestic investors’ A shares over foreign investors’ B shares and cointegration between the A and B share prices on the Chinese stock exchanges. We find that the A share price premium is nonstationary until 2001, when the A and B share markets were partially merged, and that the A and B share prices are cointegrated in the panel.Cointegration is more likely to be found for firms in the service sector and for firms that issued B shares recently.