Construction of Stationarity Tests with Less Size Distortions

We propose a (trend) stationarity test with a good finite sample size even when a process is (trend) stationary with strong persistence; this is useful for distinguishing between a (trend) stationary process with strong persistence and a unit root process. It could be considered as a modified version of Leybourne and McCabe's test (1994, LMC), but with adi fferent correction method for serial correlation. A Monte Carlo simulation reveals that in terms of empirical size, our test is closer to the nominal one than the original LMC test and is more powerful than the LMC test with size-adjusted critical values.LM test, stationary, unit root

The Rank of a Sub-Matrix of Cointegration

This paper proposes a test of the rank of the sub-matrix of b, where b is a cointegrating matrix. In addition, the sub-matrix of d, an orthogonal complement to b, is investigated. We show that information on the rank of the sub-matrix of b and/or d is useful in several situations. We construct the test statistic by using the eigenvalues of the quadratic form of the sub-matrix. We show that the test statistic has a limiting chi-squared distribution when the data is non-trending, and we propose a conservative test when the data is trending. Finite sample simulations show that, although the simulation settings are limited, the proposed test works well.

Testing the Rank of a Sub-Matrix of Cointegration with a Deterministic Trend

In this paper we consider the test of the rank of the sub-matrix of b, the cointegrating matrix, when the process has a deterministic linear trend. We review the problem of the testing procedure proposed by Kurozumi (2003) and give the alternative test statistic that is symptotically chi-square distributed. We also propose the test of the rank of the sub-matrix of d, the orthogonal matrix to b. Monte Carlo simulations show that our tests proposed in this paper work fairly well in finite samples even when the tests proposed by Kurozumi (2003) perform poorly.

Testing for Multiple Structural Changes with Non-Homogeneous Regressors

This paper investigates tests for multiple structural changes with non-homogeneous regressors, such as polynomial trends. We consider exponential-type, supremum-type and average-type tests as well as the corresponding weighted-type tests suggested in the literature. We show that the limiting distributions depend on regressors in general, and we need to tabulate critical values depending on them. Then, we focus on the linear trend case and obtain the critical values of the test statistics. The Mote Carlo simulations are conducted to investigate the finite sample properties of the tests proposed in the paper, and it is found that the specification of the number of breaks is an important factor for the finite sample performance of the tests. Since it is often the case that we cannot prespecify the number of breaks under the alternative but can suppose only the maximum number of breaks, the weighted-type tests are useful in practice.Multiple Breaks, Exp-type Test, Sup-type Test, Avg-type Test, Mean-type Test

Construction of Stationarity Tests with Less Size Distortions

We propose a (trend) stationarity test with a good finite sample size even when a process is (trend) stationary with strong persistence; this is useful for distinguishing between a (trend) stationary process with strong persistence and a unit root process. It could be considered as a modified version of Leybourne and McCabe's test (1994, LMC), but with a different correction method for serial correlation. A Monte Carlo simulation reveals that in terms of empirical size, our test is closer to the nominal one than the original LMC test and is more powerful than the LMC test with size-adjusted critical values.LM test, stationary, unit root

Do investment-specific technological changes matter for business fluctuations? Evidence from Japan

The observed decline in the relative price of investment goods to consumption goods in Japan suggests the existence of investment-specific technological (IST) changes. We examine whether IST changes are a major source of business fluctuations in Japan, by estimating a dynamic stochastic general equilibrium model with Bayesian methods. We show that IST changes are less important than neutral technological changes in explaining output fluctuations. We also demonstrate that investment fluctuations are mainly driven by shocks to investment adjustment costs. Such shocks represent variations of costs involved in changing investment spending, such as financial intermediation costs. We then find that the estimated series of the investment adjustment cost shock correlates strongly with the diffusion index of firms' financial position in the Tankan (Short-term Economic Survey of Enterprises in Japan). We thus argue that the large decline in investment growth in the early 1990s is due to an increase in investment adjustment costs stemming from firms' tight financial constraint after the collapse of Japan's asset price bubble

Do investment-specific technological changes matter for business fluctuations? Evidence from Japan

The observed decline in the relative price of investment goods to consumption goods in Japan suggests the existence of investment-specific technological (IST) changes. We examine whether IST changes are a major source of business fluctuations in Japan, by estimating a dynamic stochastic general equilibrium model with Bayesian methods. We show that IST changes are less important than neutral technological changes in explaining output fluctuations. We also demonstrate that investment fluctuations are mainly driven by shocks to investment adjustment costs. Such shocks represent variations of costs involved in changing investment spending, such as financial intermediation costs. We then find that the estimated series of the investment adjustment cost shock correlates strongly with the diffusion index of firms' financial position in the Tankan (Short-term Economic Survey of Enterprises in Japan). We thus argue that the large decline in investment growth in the early 1990s is due to an increase in investment adjustment costs stemming from firms' tight financial constraint after the collapse of Japan's asset price bubble.Business fluctuation; Investment-specific technology; Investment adjustment cost shock; Financial intermediation cost; Firms' financial constraint

Testing for the Null Hypothesis of Cointegration with Structural Breaks (Subsequently published in "Econometric Reviews", Volume 26, Issue 6 November 2007, pages 705 - 739. )

In this paper we propose residual-based tests for the null hypothesis of cointegration with structural breaks against the alternative of no cointegration. The Lagrange Multiplier test is proposed and its limiting distribution is obtained for the case in which the timing of a structural break is known. Then the test statistic is extended in two ways to deal with a structural break of unknown timing. The first test statistic, a plug-in version of the test statistic for known timing, replaces the true break point by the estimated one. We also propose a second test statistic where the break point is chosen to be most favorable for the null hypothesis. We show the limiting properties of both statistics under the null as well as the alternative. Critical values are calculated for the tests by simulation methods. Finite-sample simulations show that the empirical size of the test is close to the nominal one unless the regression error is very persistent and that the test rejects the null when no cointegrating relationship with a structural break is present.

Identifying News Shocks with Forecast Data

Recent studies attempt to quantify the empirical importance of news shocks (ie., anticipated future schocks) in business cycle fluctuations. This paper identifies news schocks in a dynamic stochastic general equilibrium model estimated with not only actual data but also forecast data. The estimation results show new empirical evidence that antecipated future technology shocks are the most important driving force of U.S. business cycles. The use of the forecast data makes the anticipated shocks play a much more important role in fitting model-implied expectations to this data, since such shocks have persistent effects on the expectaions and thereby help to replicate the observed persistence of the forecasts.

A Locally Optimal Test for No Unit Root in Cross-sectionally Dependent Panel Data

This paper develops a simple test for the null hypothesis of no unit root for panel data with cross-sectional dependence in the form of a common factor in the disturbance. We do not estimate the common factor but mop-up its effect by employing the same method as the one proposed in Pesaran (2007) in the unit root testing context. We show that our test is asymptotically locally optimal, although the optimality is not guaranteed under a wide range of the alternative.KPSS test, unit root, cross-sectional dependence, LM test, locally best test