A semiparametric triangular systems approach shows how multicointegrating linkages occur naturally in an cointegrated regression model when the long run error variance matrix in the system is singular. Under such singularity, cointegrated systems embody a multicointegrated structure that makes them useful in many empirical settings. Earlier work shows that such systems may be analyzed and estimated without appealing to the associated system but with suboptimal convergence rates and potential asymptotic bias. The present paper develops a robust approach to estimation and inference of such systems using high dimensional IV methods that have appealing asymptotic properties like those known to apply in the optimal estimation of cointegrated systems (Phillips, 1991). The approach uses an extended version of high-dimensional trend IV (Phillips, 2006, 2014) estimation with deterministic orthonormal instruments. The methods and derivations involve new results on high-dimensional IV techniques and matrix normalization in the limit theory that are of independent interest. Wald tests of general linear restrictions are constructed using a fixed- long run variance estimator that leads to robust pivotal HAR inference in both cointegrated and multicointegrated cases. Simulations show good properties of the estimation and inferential procedures in finite samples. An empirical illustration to housing stocks, starts and completions is provided.
Semiparametric efficient estimation of various multi-valued causal effects, including quantiletreatment effects, is important in economic, biomedical, and other social sciences. Under the unconfoundedness condition, adjustment for confounders requires estimating the nuisance functions relating outcome or treatment to confounders nonparametrically. This paper considers a generalized optimization framework for efficient estimation of general treatment effects using artificial neural networks (ANNs) to approximate the unknown nuisance function of growing-dimensional confounders. We establish a new approximation error bound for the ANNs to the nuisance function belonging to a mixed smoothness class without a known sparsity structure. We show that the ANNs can alleviate the “curse of dimensionality” under this circumstance. We establish the root- consistency and asymptotic normality of the proposed general treatment effects estimators, and apply a weighted bootstrap procedure for conducting inference. The proposed methods are illustrated via simulation studies and a real data application.
This paper develops estimates of TFP growth adjusted for movements in unobserved factor utilization for a panel of 29 countries and up to 37 years. When factor utilization changes are unobserved, the commonly used Solow residual mismeasures actual changes in TFP. We use a general equilibrium dynamic multi-country multi-sector model to derive a production function estimating equation that corrects for unobserved factor usage. We compare the properties of utilization-adjusted TFP series to the standard Solow residual, and quantify the roles of both TFP and utilization for international business cycle comovement. Utilization-adjusted TFP is virtually uncorrelated across countries, and does not generate much GDP comovement through its propagation. Shocks to factor utilization can more successfully account for international comovement.
Climate policies vary widely across countries, with some countries imposing stringent emissions policies and others doing very little. When climate policies vary across countries, energy-intensive industries have an incentive to relocate to places with few or no emissions restrictions, an effect known as leakage. Relocated industries would continue to pollute but would be operating in a less desirable location. We consider solutions to the leakage problem in a simple setting where one region of the world imposes a climate policy and the rest of the world is passive. We solve the model analytically and also calibrate and simulate the model. Our model and analysis imply: (1) optimal climate policies tax both the supply of fossil fuels and the demand for fossil fuels; (2) on the demand side, absent administrative costs, optimal policies would tax both the use of fossil fuels in domestic production and the domestic consumption of goods created with fossil fuels, but with the tax rate on production lower due to leakage; (3) taxing only production (on the demand side), however, would be substantially simpler and almost as effective as taxing both production and consumption, because it would avoid the need for border adjustments on imports of goods; and (4) the effectiveness of the latter strategy depends on a low foreign elasticity of energy supply, which means that forming a taxing coalition to ensure a low foreign elasticity of energy supply can act as a substitute for border adjustments on goods.
We analyze sorting in a frictional labor market when workers and jobs have multidimensional characteristics. We say that matching is positive assortative in dimension (j, k) if workers with higher endowment in skill k are matched to a job distribution with higher values of attribute j in the first-order stochastic dominance sense. Crucial for sorting is a single-crossing property of technology. Sorting is positive between worker-job attributes with strong complementarities but negative in other dimensions. Finally, sorting is based on comparative advantage: workers sort into jobs that suit their skill mix rather than their overall skill level.
Which information structures are more effective at eliminating first- and higher-order uncertainty and hence at facilitating efficient play in coordination games? We consider a learning setting where players observe many private signals about the state. First, we characterize multiagent learning efficiency, that is, the rate at which players approximate common knowledge. We find that this coincides with the rate at which first-order uncertainty disappears, as higher-order uncertainty vanishes faster than first-order uncertainty. Second, we show that with enough signal draws, information structures with higher learning efficiency induce higher equilibrium welfare. We highlight information design implications for games in data-rich environments.
Conditional cash transfer (CCT) programs aim to reduce poverty or advance social goals by encouraging desirable behavior that recipients under-invest in. An unintended consequence of conditionality may be the distortion of recipients’ behavior in ways that lower welfare. We first illustrate a range of potential distortions arising from CCT programs around the world. We then show that in the simple case where a CCT causes low return participants to select into a behavior, and social returns and private perceived returns are aligned, transfer size plays an important role: the larger the transfer, the stronger the distortion becomes, implying that (i) there is an optimal transfer size for such CCTs, and (ii) unconditional cash transfers (UCTs) may be better than CCTs when the transfer amount is large. We provide empirical evidence consistent with these claims by studying a cash transfer program conditional on seasonal labor migration in rural Indonesia. In line with theory, we show that when the transfer size exceeds the amount required for travel expenses, distortionary effects dominate and migration earnings decrease.
Reliable testing data for new infectious diseases like COVID-19 is scarce in developing countries making it difficult to rapidly diagnose spatial disease transmission and identify at-risk areas. We propose a method that uses readily available data on bi-lateral migration channels combined with COVID-19 cases at respective migrant destinations to construct a spatially oriented risk index. We find significant and consistent association between our measure and various types of outcomes including actual COVID-19 cases and deaths, indices of government policy responses, and community mobility patterns. Results suggest that future pandemic models should incorporate migration-linkages to predict regional socio-economic and health risk exposure.
What is the pathway to development in a world marked by rising economic nationalism and less international integration? This paper answers this question within a framework that emphasizes the role of demand-side constraints on national development, which is identified with sustained poverty reduction. In this framework, development is linked to the adoption of an increasing returns to scale technology by imperfectly competitive firms that need to pay the fixed setup cost of switching to that technology. Sustained poverty reduction is measured as a continuous decline in the share of the population living below $1.90/day purchasing power parity in 2011 U.S. dollars over a five-year period. This outcome is affected in a statistically significant and economically meaningful way by domestic market size, which is measured as a function of the income distribution, and international market size, which is measured as a function of legally-binding provisions to international trade agreements, including the General Agreement on Tariffs and Trade, the World Trade Organization, and 279 preferential trade agreements. Counterfactual estimates suggest that, in the absence of international integration, the average resident of a low- or lower-middle-income country does not live in a market large enough to experience sustained poverty reduction. Domestic redistribution targeted towards generating a larger middle class can partially compensate for the lack of an international market.
We reconsider trend-based predictability by employing flexible learning methods to identify price patterns that are highly predictive of returns, as opposed to testing predefined patterns like momentum or reversal. Our predictor data are stock-level price charts, allowing us to extract the most predictive price patterns using machine learning image analysis techniques. These patterns differ significantly from commonly analyzed trend signals, yield more accurate return predictions, enable more profitable investment strategies, and demonstrate robustness across specifications. Remarkably, they exhibit context independence, as short-term patterns perform well on longer time scales, and patterns learned from U.S. stocks prove effective in international markets.