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This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

Transient climate sensitivity relates total climate forcings from anthropogenic and other sources to surface temperature. Global transient climate sensitivity is well studied, as are the related concepts of equilibrium climate sensitivity (ECS) and transient climate response (TCR), but spatially disaggregated local climate sensitivity (LCS) is less so. An energy balance model (EBM) and an easily implemented semiparametric statistical approach are proposed to estimate LCS using the historical record and to assess its contribution to global transient climate sensitivity. Results suggest that areas dominated by ocean tend to import energy, they are relatively more sensitive to forcings, but they warm more slowly than areas dominated by land. Economic implications are discussed.

In this paper chaos is viewed as an alternative approach to modeling complex and random appearing behavior. The spatial (static) characteristics of weekly returns and price levels for eleven International Indices are quantified. We find evidence that all countries exhibit similar static characteristics. Evidence presented supports the examination of price series instead of returns.

The purpose of this paper is to address the issue of optimal management of ecosystems by developing a dynamic model of strategic behavior by users/communities of an ecosystem such as a lake, which is subject to pollution resulting from the users. More specifically, it builds a model of two ecosystems that are spatially connected.

The paper uses the techniques of optimal control theory and game theory.

The paper uncovers sufficient conditions under which the analysis of the dynamic game can be converted to an optimal problem for a pseudo authority. It is shown that if the discount rate on the future is high enough relative to ecological self‐restoration parameters then multiple stable states appear. In this case, if the pollution level is high enough it is too costly in terms of what must be given up today to restore the damaged system. By using computational methods, the paper evaluates the relative strengths of lack of coordination, strength of ecosystem self‐cleaning forces, size of discount rates, etc.

The methodology as well as findings can help to devise an optimal management strategy over time for ecosystems.

Examines the dynamic relationships between stock markets in Japan, Hong Kong, Singapore, Malaysia, Taiwan and Thailand before, during and after the October 1997 crisis. Discusses linear and non‐linear Granger causality tests and applies both to stock market data for three time periods between Jan 1997 and Oct 1998. Presents the results, which suggest that Hong Kong, Singapore and Malaysia led the Asian crisis, Taiwan and Thailand followed but Japan played no part in spreading it. Considers consistency with other research and the implications for these stock exchanges, their regulators and future research.

This paper is focused on two particular issues related to the stochastic structure of stock prices: linear long‐memory and nonlinearity.

We examine the stability of market prices for 35 technology and 35 industrial stocks for the period December 31, 1993 to October 31, 2002. A phase portrait plot of the detrended log prices and de‐meaned returns of the two sectors shows a chaotic pattern in the stock prices indicating the presence of nonlinearity. However, when we compute the Lyapunov exponents, negative values are obtained. This shows that the price fluctuations for the 70 stocks result primarily from diffusion processes rather than from nonlinear dynamics. We evaluate forecast errors from a naïve model, a neural network, and ARMA models and find that the forecast errors are correlated with average changes in closed‐end fund discounts and other sentiment indexes. These results support an investor sentiment explanation for the closed‐end fund puzzle and behavioral theories of investor overreaction.

This chapter estimates heterogeneous productivity growth and spatial spillovers through industrial linkages in the United States and China from 1981 to 2010. The authors employ a spatial Durbin stochastic frontier model and estimates with a spatial weight matrix based on inter-country input–output linkages to describe the spatial interdependencies in technology. The authors estimate productivity growth and spillovers at the industry level using the World KLEMS database. The spillovers of factor inputs and productivity growth are decomposed into domestic and international effects. Most of the spillover effects are found to be significant and the spillovers of productivity growth offered and received provide detailed information reflecting interdependence of the industries in the global value chain (GVC). The authors use this model to evaluate the impact of a US–Sino decoupling of trade links based on simulations of four scenarios of the reductions in bilateral intermediate trade. Their estimation results and their simulations are as mentioned based on date that ends in 2010, as this is the only KLEMS data available for these countries at this level of industrial disaggregation. As the GVC linkages between the United States and China have expanded since the end of their sample period their results can be viewed as informative in their own right for this period as well as possible lower bounds on the extent of the spillovers generated by an expanding GVC.