The global clothing production system: commodity chains and business networks

The global organization of the clothing industry has been the prime example of buyer‐driven commodity chains. However, previous empirical studies to substantiate Gereffi's assertion have been highly Western centric and predominantly based on the empirical evidence of American retailers and brand name companies. In this article I demonstrate that any clothing commodity chain has various influential factors, such as the national and global regulatory framework, the societal context of the leading firm, the fashion context of the products, as well as the actual brand ownership. Thus, the organization of clothing firms in the global economy can develop diverse intra‐sectoral commodity chain coordinations. Using a hypothetical clothing commodity chain as an example, I attempt to identify the individual commodity chains within which Indonesian clothing firms are embedded. Furthermore, I outline the ways in which Indonesian clothing firms were affected by the economic crisis of the late 1990s.     

The sample autocorrelation function of I(1) processes

In case of a random walk the theoretical autocorrelations tend to one asymptotically. The sample autocorrelations, however, may decline rather fast even with large samples. We will explain this observation by deriving the asymptotic distribution that turns out to be closely related to the Dickey-Fuller (1979) distribution. Moreover we discuss the behaviour of the sample autocorrelations of integrated MA(1) and AR(1) processes. In order to prove our results we consider more general I(1) processes and apply the functional central limit theorem injected to time series analysis by Phillips (1987). We obtain unit root tests that are based on autocorrelation estimators of higher lags. We discuss their finite sample behaviour experimentally.     

The periodogram regression:correction and comments

Ratios of periodogram and spectral density at different harmonic frequencies are independent if the frequency zero is approached slowly enough. This is an asymptotically relevant condition for the periodogram regression to work with fractionally integrated series. In finite samples, however, this theoretical condition should be ignored as we illustrate experimentally.     

Ratio tests under limiting normality

We propose a class of ratio tests that is applicable whenever a cumulation (of transformed) data is asymptotically normal upon appropriate normalization. The Karhunen–Loève theorem is employed to compute weighted averages. The test statistics are ratios of quadratic forms of these averages and hence scale-invariant, also called self-normalizing: The scaling parameter cancels asymptotically. Limiting distributions are obtained. Critical values and asymptotic local power functions can be calculated by standard numerical means. The ratio tests are directed against local alternatives and turn out to be almost as powerful as optimal competitors, without being plagued by nuisance parameters at the same time. Also in finite samples they perform well relative to self-normalizing competitors.     

Asymptotic normal tests for integration in panels with cross-dependent units

The asymptotically normal, regression-based LM integration test is adapted for panels with correlated units. The N different units may be integrated of different (fractional) orders under the null hypothesis. The paper first reviews conditions under which the test statistic is asymptotically (as T→∞) normal in a single unit. Then we adopt the framework of seemingly unrelated regression [SUR] for cross-correlated panels, and discuss a panel test statistic based on the feasible generalized least squares [GLS] estimator, which follows a χ ²(N) distribution. Third, a more powerful statistic is obtained by working under the assumption of equal deviations from the respective null in all units. Fourth, feasible GLS requires inversion of sample covariance matrices typically imposing T>N; in addition we discuss alternative covariance matrix estimators for T<N. The usefulness of our results is assessed in Monte Carlo experimentation.     

Unit root tests: the autoregressive approach in comparison with the periodogram regression

In Monte Carlo sudies we investigate unit root tests in line with Dickey/Fuller (1979). In case of positively autocorrelated MA(1) residuals their experimental power is extremely poor. Next we compare different versions of periodogram regression suggested in the literature. Their experimental behaviour is investigated with fractionally integrated processes. It is demonstrated how unit root tests may be based on periodogram regression. There is simulation evidence that those tests may do better in terms of power than the autoregressive tests, especially when testing ARMA(1,1) series against a linear time trend.     

Sample autocorrelations of nonsationary fractionally integrated series

We derive the asymptotic distribution of the sample autocor-relations of nonstationary fractionally integrated processes of order d. If d≥1, the sample autocorrelations approach their probability limit one with a rate equal to the sample size. If d<1, the rate is slower and depends on d. These findings carry over to the case of detrended series. Monte Carlo evidence and an empirical example illustrate the theoretical results.     

Detecting changes from short to long memory

This paper studies well-known tests by Kim et?al. (J Econom 109:389?C392, 2002) and Busetti and Taylor (J Econom 123:33?C66, 2004) for the null hypothesis of short memory against a change to nonstationarity, I (1). The potential break point is not assumed to be known but estimated from the data. First, we show that the tests are also applicable for a change from I (0) to a fractional order of integration I (d) with d?>?0 (long memory) in that the tests are consistent. The rates of divergence of the test statistics are derived as functions of the sample size T and d. Second, we compare their finite sample power experimentally. Third, we consider break point estimation for a change from I (0) to I (d) for finite samples in computer simulations. It turns out that the estimators proposed for the integer case (d?=?1) are practically reliable only if d is close enough to 1.     

Optimizing the conditional value-at-risk in revenue management

Many service industries use revenue management to balance demand and capacity. The assumption of risk-neutrality lies at the heart of the classical approaches, which aim at maximizing expected revenue. In this paper, we give a comprehensive overview of the existing approaches, most of which were only recently developed, and discuss the need to take risk-averse decision makers into account. We then present a heuristic that maximizes conditional value-at-risk (CVaR). Although CVaR has become increasingly popular in finance and actuarial science due to its beneficial properties, this risk measure has not yet been considered in the context of revenue management. We are able to efficiently solve the optimization problem inherent in CVaR by taking advantage of specific structural properties that allow us to reformulate this optimization problem as a continuous knapsack problem. In order to demonstrate the applicability and robustness of our approach, we conduct a simulation study that shows that the new approach can significantly improve the risk profile in various scenarios.