In this paper we study identification and estimation of the causal effect of a small change in an endogenous regressor on a continuously-valued outcome of interest using panel data. We focus on the average partial effect (APE) over the full population distribution of unobserved heterogeneity (e.g., Chamberlain, 1984; Blundell and Powell, 2003; Wooldridge, 2005a). In our basic model the outcome of interest varies linearly with a (scalar) regressor, but with an intercept and slope coefficient that may vary across units and over time in a way which depends on the regressor. This model is a special case of Chamberlain's (1980b, 1982, 1992a) correlated random coefficients (CRC) model, but not does not satisfy the regularity conditions he imposes. Irregularity, while precluding estimation at parametric rates, does not result in a loss of identification under mild smoothness conditions. We show how two measures of the outcome and regressor for each unit are sufficient for identification of the APE as well as aggregate time trends. We identify aggregate trends using units with a zero first difference in the regressor or, in the language of Chamberlain (1980b, 1982), 'stayers' and the average partial effect using units with non-zero first differences or 'movers'. We discuss extensions of our approach to models with multiple regressors and more than two time periods. We use our methods to estimate the average elasticity of calorie consumption with respect to total outlay for a sample of poor Nicaraguan households (cf., Strauss and Thomas, 1995; Subramanian and Deaton, 1996). Our CRC average elasticity estimate declines with total outlay more sharply than its parametric counterpart.

We estimate the average fiscal multiplier, allowing multipliers to be heterogeneous across countries or over time and correlated with the size of government spending. We demonstrate that this form of nonseparable unobserved heterogeneity is empirically relevant and address it by estimating a correlated random coefficient model. Using a panel dataset of 127 countries over the period 1994-2011, we show that not accounting for omitted heterogeneity produces a significant downward bias in conventional multiplier estimates. We rely on both crosssectional and time-series variation in spending shocks, exploiting the differential effects of oil price shocks on fuel subsidies, to identify the average government spending multiplier. Our estimates of the average multiplier range between 1.4 and 1.6.

The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers, and data scientists seeking to perform effective analyses of networks, especially inference problems. It introduces the key results and ideas in an accessible, yet rigorous way. While a multi-contributor reference, the work is tightly focused and disciplined, providing latitude for varied specialties in one authorial voice. - Answers both 'why' and 'how' questions in network analysis, bridging the gap between practice and theory allowing for the easier entry of novices into complex technical literature and computation - Fully describes multiple worked examples from the literature and beyond, allowing empirical researchers and data scientists to quickly access the 'state of the art' versioned for their domain environment, saving them time and money - Disciplined structure provides latitude for multiple sources of expertise while retaining an integrated and pedagogically focused authorial voice, ensuring smooth transition and easy progression for readers - Fully supported by companion site code repository - 40+ diagrams of 'networks in the wild' help visually summarize key points

This book is a collection of articles that present the most recent cutting edge results on specification and estimation of economic models written by a number of the world’s foremost leaders in the fields of theoretical and methodological econometrics. Recent advances in asymptotic approximation theory, including the use of higher order asymptotics for things like estimator bias correction, and the use of various expansion and other theoretical tools for the development of bootstrap techniques designed for implementation when carrying out inference are at the forefront of theoretical development in the field of econometrics. One important feature of these advances in the theory of econometrics is that they are being seamlessly and almost immediately incorporated into the “empirical toolbox” that applied practitioners use when actually constructing models using data, for the purposes of both prediction and policy analysis and the more theoretically targeted chapters in the book will discuss these developments. Turning now to empirical methodology, chapters on prediction methodology will focus on macroeconomic and financial applications, such as the construction of diffusion index models for forecasting with very large numbers of variables, and the construction of data samples that result in optimal predictive accuracy tests when comparing alternative prediction models. Chapters carefully outline how applied practitioners can correctly implement the latest theoretical refinements in model specification in order to “build” the best models using large-scale and traditional datasets, making the book of interest to a broad readership of economists from theoretical econometricians to applied economic practitioners.

Now in its fourth edition, this comprehensive introduction of fundamental panel data methodologies provides insights on what is most essential in panel literature. A capstone to the forty-year career of a pioneer of panel data analysis, this new edition's primary contribution will be the coverage of advancements in panel data analysis, a statistical method widely used to analyze two or higher-dimensional panel data. The topics discussed in early editions have been reorganized and streamlined to comprehensively introduce panel econometric methodologies useful for identifying causal relationships among variables, supported by interdisciplinary examples and case studies. This book, to be featured in Cambridge's Econometric Society Monographs series, has been the leader in the field since the first edition. It is essential reading for researchers, practitioners and graduate students interested in the analysis of microeconomic behavior.

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The post-industrial revolution has changed global economic and environmental conditions. Globalization has helped to boost resource productivity and accelerated individual nations’ economic development and levels of connectedness to one another. The One Belt One Road (OBOR) initiative, for example, is an ambitious economic growth and business venture aimed at increasing the integration of various countries across Africa, Asia, and Europe. The Chinese authorities term it the "Century project," as OBOR stretches around 78 nations globally. The China-Pakistan Economic Corridor (CPEC) is another example of economic integration, emphasizing connections to Africa and West Asia as economic markets. It remains apparent that economic growth and energy sources (mainly fossil fuels) are the primary drivers of climate change. However, only a limited fraction of initiatives have been taken to convert such energy systems to carbon-neutral sources. Economic integration extension without green corridors raises doubts on the effectiveness and productivity of various international conferences and summits in this regard.