DO REAL INTEREST RATES REALLY CONTAIN A UNIT ROOT? MORE EVIDENCE FROM A BOOTSTRAP COVARIATE UNIT ROOT TEST

This paper re‐examines the empirical finding that international real interest rates usually have a unit root. This conclusion is put forth in Rapach and Weber (2004 ), using the Ng and Perron (2001 ) tests. We use Rudebusch's (1993 ) approach to construct the small sample distributions of the Ng and Perron tests, and calculate their asymptotic sizes, size‐adjusted powers and rejection rates. These numbers show that the lack of power in the Ng and Perron tests might account for the findings of Rapach and Weber (2004 ): that the unit root null cannot be rejected for most OECD countries. Size distortions are mild in the case of Ng and Perron tests for two series, but are serious for the Phillips and Perron Z‐test on inflation rates. We then apply a powerful covariate augmented Dickey–Fuller unit root test to examine the series for which stationarity cannot be determined with the Ng and Perron tests. The bootstrap technique is also used to control possible size distortions. In contrast to the results of Rapach and Weber (2004 ), the bootstrap covariate augmented Dickey–Fuller test yields striking evidence that real interest rates are stationary for 14 of 16 OECD countries, because nominal interest rates are stationary for the 14 countries, while inflation rates are stationary for all countries.     

A REVISIT TO THE STATIONARITY OF OECD INFLATION: EVIDENCE FROM PANEL UNIT‐ROOT TESTS AND THE COVARIATE POINT OPTIMAL TEST*

This paper re‐examines the stationarity of inflation rates in 19 Organisation for Economic Cooperation and Development countries with the use of cross‐sectional information. We employ the panel unit‐root tests that allow for cross‐sectional dependency and the covariate point optimal test. These tests have high power in common due to the exploitation of cross‐sectional information, and they can assist mutually to draw a concrete conclusion on inflation dynamics for all series in the panel. Our empirical results show that allowing for cross‐sectional dependency rejects the null hypothesis that all series in the panel have a unit root, implying that there is at least one stationary series in the panel. With the help of the results of the covariate test, we can distinguish the panel into a group of stationary and a group of non‐stationary series. For robustness, the two groups of series are re‐confirmed by the panel tests. Our results reveal evidence of mean reversion in inflation for 15 of 19 countries, which is significantly stronger as compared to that obtained by the state‐of‐the‐art univariate unit‐root tests.     

Structural break,nonlinearity and asymmetry: a re-examination of PPP proposition

In this study, we examine the validity of the PPP proposition for 28 European countries. For this purpose, we propose a new unit root test procedure that allows for both gradual structural breaks and asymmetric nonlinear adjustment towards the equilibrium level. Small-sample properties of the new tests are examined through Monte-Carlo simulations. The simulation results suggest that the new tests have satisfactory size and power properties. We then apply these new tests along with other unit root tests to examine stationarity properties of real exchange rate series of the sample countries. Our tests reject the null of unit root in more cases when compared to alternative tests. Overall, we find that the PPP proposition holds in majority of the European countries examined in this article.     

On the power of bootstrap tests for stationarity: a Monte Carlo comparison

This paper studies the behavior of recently proposed bootstrap tests for the null hypothesis of stationarity when the data are generated under the alternative hypothesis of a unit root. Using Monte Carlo experiments and empirical examples, it is shown that the power of these tests critically depends on the type of bootstrap employed. Specifically, while tests based on the stationary bootstrap have power functions that are increasing with respect to sample size, those based on the sieve bootstrap have non-monotonic power functions. We argue that this difference arises from the fact that the latter procedure does not impose the null hypothesis when generating the bootstrap samples while the former ensures that the bootstrap samples are stationary, conditional on the original data. Our results therefore suggest that while both forms of bootstrap are effective at providing improved distributional approximations under the null hypothesis, it is important to pay careful attention to the particular type of bootstrap being employed when attempting to distinguish between the unit root and stationarity hypotheses as the choice of bootstrap can have crucial implications for the power of the resulting tests.     

Panel unit root tests under cross section dependence with recursive mean adjustment

Utilizing recursive mean adjustment (RMA) we provide two unit root tests: the covariate RMA unit root test and the panel feasible generalized RMA unit root test. The proposed panel unit root tests are precise and powerful, especially when T > N.     

Panel LM unit root tests with level and trend shifts

This paper considers a Lagrange multiplier (LM) based panel unit root test that allows for heterogeneous structural breaks in both the intercepts and slopes of a series. We note that many popular time series variables are likely to exhibit changing means and/or trends over time. Given that the usual tests will depend on the nuisance parameters indicating the locations of the trend breaks, we adopt a transformation procedure that makes our new panel unit root tests invariant to the nuisance parameters. To illustrate the importance of the power gain provided by our test, we examine the convergence hypothesis using relative ratios of per capita health care expenditures in 20 OECD countries. Our results provide evidence that the convergence hypothesis is supported.     

Are OECD consumption–income ratios stationary after all?

This paper investigates the stochastic properties of the consumption–income ratio for a sample of 23 OECD countries over the period 1960–2005. For that purpose, we employ a battery of recently developed panel unit root and stationarity tests. Our findings from panel unit root tests which do not control for structural breaks appear in line with those from previous studies since they are clearly supportive of the unit root hypothesis. In stark contrast stand the results obtained from the application of a panel stationarity test with multiple breaks, which support the existence of regime-wise stationarity in OECD consumption–income ratios once we control for cross-sectional dependence through bootstrap methods. These findings are reinforced by the median-unbiased estimates of half-lives obtained from impulse-response functions which are found to be finite for the 23 OECD countries.     

A nonlinear approach to testing the unit root null hypothesis: an application to international health expenditures

In this article, we examine the unit root null hypothesis for per capita total Health Expenditures (HEs), per capita private HEs and per capita public HEs for 29 Organization for Economic Co-operation and Development (OECD) countries. The novelty of our work is that we use a new nonlinear unit root test that allows for one structural break in the data series. We find that for around 45% of the countries, we are able to reject the unit root hypothesis for each of the three HE series. Moreover, using Monte Carlo simulations, we show that our proposed unit root model has better size and power properties than the widely used Augmented Dickey–Fuller (ADF) and Lagrange Multiplier (LM) type tests.     

TESTING FOR STATIONARITY WITH A BREAK IN PANELS WHERE THE TIME DIMENSION IS FINITE

In this paper, we consider the case of finite time dimension in the panel stationarity tests with structural breaks. By fixing T, the finite sample properties of the tests for both micro (T small and N large) and macro (both T and N large) panel data are generally greatly improved. More importantly, the derivation of the tests for finite T and , as opposed to joint asymptotic where N and simultaneously, avoids the imposition of the rate condition making the test valid for any (T, N) blend. Four models corresponding to the usual combination of breaks are considered. The asymptotic distributions of the test are derived under the null hypothesis and are shown to be normally distributed. Their moments for T fixed are derived analytically employing Ghazal’s corollary 1. The case with unknown breaks is also considered. The proposed tests have generally empirical sizes that are very close to the nominal size. The Monte Carlo simulations show that the power of the test statistics increases substantially with N and T.     

A simple unit root test against asymmetric STAR nonlinearity with an application to real exchange rates in Nordic countries

Existing tests of the unit root hypothesis against the alternative hypothesis of exponential smooth transition autoregressive (ESTAR) nonlinearity implicitly assume symmetry under the alternative. This paper proposes a simple unit root test against the alternative of symmetric or asymmetric ESTAR nonlinearity. In the event that the unit root hypothesis is rejected, a simple test of symmetric versus asymmetric ESTAR nonlinearity is also proposed. The asymptotic distributions of the test statistics are straightforward to establish and finite-sample performance is studied with Monte Carlo simulations. An empirical application involving the real exchange rates of four Nordic countries against the U.S. dollar illustrates the usefulness of the new tests.     

Testing for unemployment hysteresis in nonlinear heterogeneous panels: International evidence

Nonlinear behavior of unemployment is well documented in the literature, and thus linear unit root tests may not be appropriate in this case. This paper tests for hysteresis of unemployment for 29 OECD countries through the use of a new nonlinear panel unit root test developed by Ucar and Omay (2009). The test examines the joint null hypothesis of linearity and a unit root against the alternative hypothesis of nonlinear stationarity. Large power gains are achieved by both combining cross-sectional information with nonlinearities in the data. In addition, after the unit root null being rejected, we use a sequential panel selection method suggested by Chortareas and Kapetanios (2009) to classify the whole panel into a group of stationary countries and a group of non-stationary countries. The empirical findings show that the nonlinear panel test gives strong evidence in favor of the natural rate hypothesis of unemployment for 23 of 29 OECD countries, in contrast to those obtained by Chang's (2002) linear panel test that 17 countries display evidence of stationary unemployment.     

Exchange rates dynamics revisited: a panel data test of the fractional integration order

We test the possibility that exchange rates from nine developed countries have a unit root against the alternate possibility that they are fractionally integrated. Theoretically, exchange rates are only expected to follow a random walk under restrictive assumptions. However, most traditional unit root tests cannot reject a unit root in exchange rates, and time series tests that allow for fractional integration have given inconclusive results. To increase the power of the test of the integration order we develop two panel data tests of the fractional integration order. Monte Carlo simulations show that these tests are correctly sized and have relatively high power compared to other similar tests. Moreover, our empirical results show that we can reject a unit root in exchange rates with a high probability, but the integration order is close to one. This indicates that exchange rates are mean-reverting, although the reversion is slow, resulting in long swings.     

Breaking Trend Functions in Real Exchange Rates: Evidence from Seventeen OECD Countries

This paper tests for unit roots in dollar-based and DM-based real exchange rates using quarterly data (from 1957:i to 1995:iv) for seventeen OECD countries. The results show that the unit root hypothesis cannot be rejected even if allowance is made for the possibility of a one-time change in the mean of the series at an unknown point in time. This is evidence against the hypothesis of absolute long-run purchasing power parity over this period.     

A reappraisal of the evidence on PPP: a systematic investigation into MA roots in panel unit root tests and their implications

Panel unit root tests of real exchange rates—as opposed to univariate tests—usually reject non-stationarity. These tests, however, could be biased if the real exchange rate contained MA roots. Indeed, two independent arguments claim that the real exchange rate, being a sum of a stationary and a non-stationary component, is possibly an ARIMA (1, 1, 1) process. Monte Carlo simulations show how systematic changes in the parameters of the components, of the test equation and of the correlation matrix affect the size of first and second-generation panel unit root tests. Two components of the real exchange rate—the real exchange rate of a single good and a weighted sum of relative prices—are constructed from the data for a panel of countries. Computation of the relevant parameters reveals that panel unit root tests of the real exchange rate are severely oversized, usually much more so than simple augmented Dickey-Fuller tests. Thus, the evidence for purchasing power parity from first and second-generation panel unit root tests may be merely due to extreme size biases.     

Quantile nonlinear unit root test with covariates and an application to the PPP hypothesis

In this article, we employ the methods initiated by Hansen (1995) to develop new quantile nonlinear unit root tests with covariates. The limiting distributions of our proposed tests are derived, which are dependent on nuisance parameter reflecting the correlation between the equation error and the covariates. To deal with this inferential difficulty, two alternative procedures based on either consistent estimate of the nuisance parameter or bootstrap implementation of the test are proposed. Monte Carlo simulations show that the proposed tests perform very well in finite samples and large power gains can be achieved by including correlated covariates in the testing equation. The proposed tests are applied to the PPP hypothesis. The empirical results indicate that the real exchange rates are not constant unit root processes.     

Improving the Performance of a Long‐Run Variance Ratio Test for a Unit Root

Cai and Shintani (2006, Econometric Theory, 22, 347–372) considered the impact of introducing an inconsistent long‐run variance estimator when constructing a class of kernel‐based ratio tests for testing non‐stationarity in the series. They found that the quotient of two estimators with different rates of convergence under the null and the alternative hypotheses may lead to a test having an interesting size and power trade‐off. This paper develops modified versions of this test, presents new asymptotic results and tabulates critical values. The finite sample performance is explored through Monte Carlo simulations. The results show that the modifications proposed lead to more powerful unit root tests.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Testing for a unit root under the alternative hypothesis of ARIMA (0, 2, 1)

Showing a dual relationship between ARIMA (0,?2,?1) with parameter θ?=??1 and the random walk, a new alternative hypothesis in the form of ARIMA (0,?2,?1) is established in this article for evaluating unit root tests. The power of four methods of testing for a unit root is investigated under the new alternative, using Monte Carlo simulations. The first method testing θ?=??1 in second differences and using a new set of critical values suggested by the two authors in finite samples, is the most appropriate from the integration order point of view. The other three methods refer to tests based on t and?φ?statistics introduced by Dickey and Fuller, as well as, the nonparametric Phillips–Perron test. Additionally, for cases where for the first method a low power is met, we studied the validity of prediction interval for a future value of ARIMA (0,?2,?1) with θ close but greater of ?1, using the prediction equation and the error variance of the random walk. Keeping the forecasting horizon short, the coverage of the interval ranged at expected levels, but its average half-length ranged up to four times more than its true value.     

Evidence on the Convergence of Per Capita Income: A Comparison of Founder Members of the Association of South East Asian Nations and the South Asian Association of Regional Cooperation

This paper investigates the per capita income convergence patterns of a set of Association of South East Asian Nations (ASEAN) and South Asian Association of Regional Cooperation (SAARC) countries. We obtained a time‐series analysis for stochastic convergence by applying unit‐root tests in the presence of two endogenously‐determined structural breaks. We then supplemented the results by tests that produced evidence for β convergence. The evidence shows that the relative per capita income series of ASEAN‐5 countries were consistent with stochastic convergence and β convergence, but this was not found for SAARC‐5 countries. For the ASEAN‐5 countries, the structural breaks associated with the world oil crisis and the Asian crisis impacted heavily on the convergence/divergence process.     

City CPI Convergence in Mexico

This paper tests the purchasing power parity hypothesis within a single developing country currency area: Mexico. This work stems from research in comparing price movements across countries and a growing literature on price dynamics within a single currency area. The author uses city Consumer Price Index data for 34 cities in Mexico over the period 1982–2000. He followed the standard procedure of testing for I(1) processes in relative city prices, or city real exchange rates, using univariate and panel unit root tests. The main results of the paper are: First, Mexican city relative prices are stationary—the data rejects the hypothesis that city real exchange rates contain a random walk, but only using panel unit root techniques. Finally, regional demand and supply homogeneity implies stronger evidence for price parity within regions, while there is considerable evidence of regional price convergence, regional homogeneity does not guarantee faster convergence.