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In this chapter we demonstrate the construction of inverse test confidence intervals for the turning-points in estimated nonlinear relationships by the use of the marginal or first derivative function. First, we outline the inverse test confidence interval approach. Then we examine the relationship between the traditional confidence intervals based on the Wald test for the turning-points for a cubic, a quartic, and fractional polynomials estimated via regression analysis and the inverse test intervals. We show that the confidence interval plots of the marginal function can be used to estimate confidence intervals for the turning-points that are equivalent to the inverse test. We also provide a method for the interpretation of the confidence intervals for the second derivative function to draw inferences for the characteristics of the turning-point.

This method is applied to the examination of the turning-points found when estimating a quartic and a fractional polynomial from data used for the estimation of an Environmental Kuznets Curve. The Stata do files used to generate these examples are listed in Appendix A along with the data.

The collection of chapters in this 30th volume of Advances in Econometrics provides a well-deserved tribute to Thomas B. Fomby and R. Carter Hill, who have served as editors of the Advances in Econometrics series for 25 and 21 years, respectively. Volume 30 contains a more varied collection of chapters than previous volumes, in essence mirroring the wide variety of econometric topics covered by the series over 30 years. Volume 30 starts with a chapter discussing the history of this series over the last 30 years. The next five chapters can be broadly categorized as focusing on model specification and testing. Following this section are three contributions that examine instrumental variables models in quite different settings. The next four chapters focus on applied macroeconomics topics. The final chapter offers a practical guide to conducting Monte Carlo simulations.

Recent evidence on the impact of the crisis on developed countries shows that changes in income inequality and poverty have been relatively small in spite of the macroeconomic heterogeneity of the recession across different economies. However, when evaluating individual perceptions linked to the crisis any changes in the chances to scale up or lose ground in the income ladder are also crucial. Our aim in this paper is to analyze to what extent the recession has had an impact on individual equivalent incomes and, in particular, on the prevalence of downward mobility in two developed countries where job losses have been large. We find that income losses have increased, particularly in Spain, and while age and education are key determinants of the probability of experiencing an income loss in both countries, the presence of children only increases the probability of an income loss in Spain.

Field experiments have raised important issues of interpretation of bargaining behavior. There is evidence that bargaining behavior appears to vary across groups of populations, such as nationality, ethnicity and sex. Differences have been observed with respect to initial behavior and with respect to the adjustment pattern over time. Often, such behavioral differences are referred to as cultural, although the delineation of the cultural group has been confined to one or other observable characteristic in isolation. We show that this way of characterizing cultural differences is overly simplistic: at best, it leads to unreliable claims; at worst, it leads to erroneous conclusions. We reconsider the evidence provided by previous experiments using ultimatum game rules, and undertake new experiments that expand the controls for demographics. The lesson from our demonstration is that the task of designing experiments for the field offers many challenges if one wants to define and control for cultural impacts, but that field experiments also offer potential for providing new insights into these issues.

The sharp increase in interest in social networks among marketing scholars and practitioners has coincided with the rapid proliferation of social networks among broader populations. Considering the substantial body of research that has emerged, it is an opportune time to reflect on the state of social network research (SNR) in marketing. Therefore, this chapter reviews recent marketing research, organized according to substantive areas of interest, followed by a discussion of critical dimensions of SNR for researchers, including network actor characteristics, modes, boundaries, impacts, and mechanisms, as well as the relevant level of analysis. By documenting how SNR can inform marketing decisions and influence marketing outcomes, this study also establishes recommendations for research to advance the state of SNR in marketing. A 2 × 2 classification schema reveals four categories that might guide scholars' choices of research designs, theories, constructs, and measures for SNR.

The discrete Fourier transform (dft) of a fractional process is studied. An exact representation of the dft is given in terms of the component data, leading to the frequency domain form of the model for a fractional process. This representation is particularly useful in analyzing the asymptotic behavior of the dft and periodogram in the nonstationary case when the memory parameter d≥12. Various asymptotic approximations are established including some new hypergeometric function representations that are of independent interest. It is shown that smoothed periodogram spectral estimates remain consistent for frequencies away from the origin in the nonstationary case provided the memory parameter d < 1. When d = 1, the spectral estimates are inconsistent and converge weakly to random variates. Applications of the theory to log periodogram regression and local Whittle estimation of the memory parameter are discussed and some modified versions of these procedures are suggested for nonstationary cases.

This study employs a new time series representation of persistence in conditional mean and variance to test for the existence of the long memory property in the currency futures market. Empirical results indicate that there exists a fractional exponent in the differencing process for foreign currency futures prices. The series of returns for these currencies displays long-term positive dependence. A hedging strategy for long memory in volatility is also discussed in this article to help the investors hedge for the exchange rate risk by using currency futures.

Apart from the well-known, high persistence of daily financial volatility data, there is also a short correlation structure that reverts to the mean in less than a month. We find this short correlation time scale in six different daily financial time series and use it to improve the short-term forecasts from generalized auto-regressive conditional heteroskedasticity (GARCH) models. We study different generalizations of GARCH that allow for several time scales. On our holding sample, none of the considered models can fully exploit the information contained in the short scale. Wavelet analysis shows a correlation between fluctuations on long and on short scales. Models accounting for this correlation as well as long-memory models for absolute returns appear to be promising.

Since the seminal works by Granger and Joyeux (1980) and Hosking (1981), estimations of long-memory time series models have been receiving considerable attention and a number of parameter estimation procedures have been proposed. This paper gives an overview of this plethora of methodologies with special focus on likelihood-based techniques. Broadly speaking, likelihood-based techniques can be classified into the following categories: the exact maximum likelihood (ML) estimation (Sowell, 1992; Dahlhaus, 1989), ML estimates based on autoregressive approximations (Granger & Joyeux, 1980; Li & McLeod, 1986), Whittle estimates (Fox & Taqqu, 1986; Giraitis & Surgailis, 1990), Whittle estimates with autoregressive truncation (Beran, 1994a), approximate estimates based on the Durbin–Levinson algorithm (Haslett & Raftery, 1989), state-space-based maximum likelihood estimates for ARFIMA models (Chan & Palma, 1998), and estimation of stochastic volatility models (Ghysels, Harvey, & Renault, 1996; Breidt, Crato, & de Lima, 1998; Chan & Petris, 2000) among others. Given the diversified applications of these techniques in different areas, this review aims at providing a succinct survey of these methodologies as well as an overview of important related problems such as the ML estimation with missing data (Palma & Chan, 1997), influence of subsets of observations on estimates and the estimation of seasonal long-memory models (Palma & Chan, 2005). Performances and asymptotic properties of these techniques are compared and examined. Inter-connections and finite sample performances among these procedures are studied. Finally, applications to financial time series of these methodologies are discussed.

Recent attempts to measure altruism toward other players or charities suffer from a potential confound: the act of giving is typically correlated with the size of the pie left on the experimenter's table. Altruistic acts could thus be more generous if subjects prefer that monies go toward other players, or charities, than be left on the table. On the other hand, revealed altruism could be lower if subjects are more altruistic toward the residual claimant than they are toward the agent to whom they are being asked to give. We demonstrate this point with simple laboratory experiments that derive from popular recent designs. We find a significant effect from the hypothesized confound, with revealed altruism dependent upon who is specified as the residual claimant.