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This paper surveys the treatment of expectations in estimated Dynamic Stochastic General Equilibrium (DSGE) macroeconomic models.

A recent notable development in the empirical macroeconomics literature has been the rapid growth of papers that build structural models, which include a number of frictions and shocks, and which are confronted with the data using sophisticated full-information econometric approaches, often using Bayesian methods.

A widespread assumption in these estimated models, as in most of the macroeconomic literature in general, is that economic agents' expectations are formed according to the Rational Expectations Hypothesis (REH). Various alternative ways to model the formation of expectations have, however, emerged: some are simple refinements that maintain the REH, but change the information structure along different dimensions, while others imply more significant departures from rational expectations.

I review here the modeling of the expectation formation process and discuss related econometric issues in current structural macroeconomic models. The discussion includes benchmark models assuming rational expectations, extensions based on allowing for sunspots, news, sticky information, as well as models that abandon the REH to use learning, heuristics, or subjective expectations.

In this article, we survey experiments that are directly related to monetary policy and central banking. We argue that experiments can also be used as a tool for central bankers for bench testing policy measures or rules. We distinguish experiments that analyze the reasons for non-neutrality of monetary policy, experiments in which subjects play the role of central bankers, experiments that analyze the role of central bank communication and its implications, experiments on the optimal implementation of monetary policy, and experiments relevant for monetary policy responses to financial crises. Finally, we mention open issues and raise new avenues for future research.

The purpose of this paper is to integrate the global value chain (GVC) perspective with firms' innovation in emerging economies (EEs) and explain why EE firms can improve their innovation capabilities more from their domestic markets by focusing on R&D and marketing than from original equipment manufacturer/original design manufacturer (OEM/ODM) modes in the GVC and how they contribute to the national innovation system (NIS).

The literature on GVC is reviewed and then several propositions are developed using the example of the underground mobile phone developers in China by integrating the GVC perspective.

The paper proposes that EE firms, especially firms that have a large underdeveloped domestic market such as China, should focus on R&D and marketing instead of on OEM/ODM in GVC to increase their competitiveness and strengthen their NIS. Also implications are drawn from their success in the underground markets to advance knowledge on NIS.

The paper shows that EE firms can build and strengthen their innovation capabilities through intense interaction and learning in domestic markets, which, subsequently, moves them upwards in their GVC. Industrial policy needs to change in order to facilitate such technological entrepreneurship in NIS, whether it is informal or underground.

In today's internationalized world, value creation consists of knowledge and work integration involving workers from around the world. Members of these globally distributed work teams (GDWT) encounter organizational behavior issues (identity, cultural differences, and leadership) and organization design issues (dependencies, information processing, media use, and teamwork structures). While most research on GDWT focuses on the first set of issues, this chapter is among the few to systematically explore the second set. We propose and elaborate on strategies for either reducing the intensity of collaboration, or enabling teams to collaborate intensely on a global scale. Implications for research and practice are explored.

The provision of high-quality e-Government services requires efficient and collaborative sharing of data across varied types of government agencies. However, interagency government data sharing (IDS) is not always spontaneous, active and unconditional. Adopting a stickiness theory, this paper reports on a research study, which explores the causes of data stickiness in IDS.

This study employed an inductive case study approach. Twenty-three officials from the government of City M in Hubei Province, Central China, were approached and interviewed using a semi-structured question script.

The analysis of the interview data pointed to 27 causes of data stickiness in five main themes: data sharing willingness; data sharing ability; data articulatability; data residence; and data absorptive capacity. The analysis revealed that interagency tensions and lack of preparedness of individual agencies are the main causes of data stickiness in IDS.

The case setting is based on China's Government, but the findings offer useful insights and indications that can be shared across international borders.

The authors model heterogeneity of inflation expectations across Indian households using the Inflation Expectations Survey of Households data set. Using Carroll-type epidemiological models and pooled cross sectional analyses, the authors find that women, homemakers, older people and Tier 2 and 3 city dwellers tend to have higher inflation expectations compared to their counterparts. In the epidemiological model-based analysis, these very cohorts display higher speed of adjustment to news. Overall higher relative adjustment speeds point to the significance of central bank communications.

This paper addresses, from a knowledge management perspective, the following question: How are companies choosing the information that is considered more important for the management of the manufacturing process? We analyze empirically, from a strategic approach, how different types and sources of information may influence information characteristics like accuracy, access, and timing. In addition, we also evaluate how information is related to manufacturing competencies and performance. The results indicate that customer‐focused information and information technologies (IT) are the most important aspects that influence information characteristics.

The purpose of this paper is to develop growth models that depart from the conventional framework, in the sense that consumption decisions take into account previous periods' expectations about output fluctuations. Households will raise their propensity to consume in periods of expected expansion and they will lower it in phases of predictable recession. Such a framework allows discussion of how growth trends may be disturbed over time as the result of changes in consumer sentiment.

Endogenous growth models are generally designed to address long‐term trends of growth. They explain how the economy converges with or diverges from a balanced growth path and they characterize aggregate behavior, given the optimization problem faced by a representative agent that maximizes consumption utility. In such frameworks, only potential output matters and all decisions, by firms and households, are taken on the assumption that any expectations on the value of the output gap do not interfere with the agents' behavior. Introducing consumer sentiment, a conventional growth model is modified in order to understand how effective output eventually deviates from the balanced growth path.

The proposed framework allows one to introduce nonlinear dynamics into the model, making it feasible to obtain, for reasonable parameter values, endogenous fluctuations. These are triggered by a Neimark‐Sacker bifurcation.

By introducing consumer confidence or consumer sentiment, it is possible to integrate the evaluation of growth and cycles into a unified framework. It is possible to explain business cycles as the result of the consumers' reaction to the expected performance of the economic system.

The Inflation Expectations Survey of Households, conducted by the Reserve Bank of India (RBI), indicates that there is considerable disparity in inflation expectations across cities in India. The purpose of this paper is to investigate why different cities exhibit heterogeneous inflation expectations despite coming under a central monetary policy umbrella.

First, the correspondence between city-level inflation expectations and city-specific economic characteristics is mapped. Second, how the disagreement in inflation expectations across cities, measured by dispersion, behaves over the business cycle is investigated. Finally, using seemingly unrelated regression technique, the economic factors that play a role in explaining inflation expectations heterogeneity across cities are estimated.

Cities with higher economic activity and cost of living have higher inflation expectations. Disagreement across cities regarding inflation expectations rise with an increase in output gap and inflation. Information friction plays an important role in explaining the disparity in inflation expectations across cities, and the effects of macro-level factors vary across cities, thereby accentuating expectations dispersion.

Monetary policy-related communication by the RBI (toward the general public) should increase in order to address information friction, which, in turn, would temper down the extent of inflation expectations heterogeneity across cities in India.

This is a novel application of the data from the monetary policy perspective. Heterogeneity in inflation expectations across cities or regions is an unexplored area. The use of nightlights as a proxy for city-level economic activity in India (in absence of data on city-level income) is another original contribution.