Intertemporal Substitution in Macroeconomics

Modern neoclassical theories of the business cycle posit that aggregate fluctuations in consumption and employment are the consequence of dynamic optimizing behavior by economic agents who face no quantity constraint. In this paper, we estimate an explicit model :f this type. In particular, we assume that the observed fluctuations correspond to the decisions of an optimizing representative individual. This individual has a stable utility function which is additively separable over time but not necessarily additively separable in consumption and leisure. We estimate three first order conditions which represent three margins on which the individual is optimizing. He can trade off present consumption for future consumption, present leisure for future leisure and present consumption for present leisure. Our results show that the aggregate U.S. data are extremely reluctant to be characterized by a model of this type. Not only are the overidentifying restrictions statistically rejected but, in addition, the estimated utility function is often not concave. Even when it is concave the estimates imply that either consumption or leisure is an inferior good.

Essays on Panel and Network Modeling

This dissertation studies identification and estimation in panel and network models. Panel models have long been a workhorse in empirical research. In the first two chapters, we analyze random coefficient linear panel model and panel multinomial choice model, respectively, where we incorporate features such as time-varying endogeneity and unobserved heterogeneity that are prevalent in real life into the models. We present new identification results and provide consistent estimators based on the identification strategy. Then, we apply the estimation procedures to panel data and obtain economically convincing results. The study of networks is a fast-growing area of economic research thanks to the increasing availability of network data and computing power. In the third chapter, we study network formation problems under non-transferrable utilities (NTU). We show how to identify the parameters of interest without additive separability based on “logical differencing” and provide consistent estimators. In chapter 1, we propose a random coefficient linear panel model where the regressors can depend on the time-varying random coefficients in each period, a critical feature in many economic applications including production function estimation. The random coefficients are modeled as unknown functions of a fixed effect of arbitrary dimension and a random shock. The regressors may depend on the random coefficients due to agent\u27s optimization behavior such as profit maximization, utility maximization, among others. We use a sufficiency argument to control for the fixed effect, which enables us to construct a feasible control function for the random shock and subsequently identify the moments of the random coefficients via a sequential argument. Based on the multi-step identification argument, we propose a series estimator and prove a new inference result. Monte Carlo simulations show that the proposed method can capture the distributional properties of the random coefficients. We then apply the procedure to panel data for Chinese manufacturing firms and find significant variation in the output elasticities both across firms and through time. In chapter 2, we propose a simple yet robust method for semiparametric identification and estimation of panel multinomial choice models, where we allow infinite-dimensional fixed effects to enter consumer utilities in an additively nonseparable way, thus incorporating rich forms of unobserved heterogeneity. Such heterogeneity may take the form of, for example, brand loyalty or responsiveness to subtle flavor and packaging designs, which are hard to quantify but affect consumer choices in complex ways. Our identification strategy exploits the standard notion of multivariate monotonicity in its contrapositive form, which provides leverage for converting observable events into identifying restrictions on unknown parameters of interest. Based on our identification result, we construct consistent set (or point) estimators, together with a computational algorithm that adopts a machine learning algorithm and a new minimization procedure on the spherical-coordinate space. We demonstrate the practical advantages of our method with simulations and an empirical example using the Nielsen data. We find that special in-store displays boost sales not only through a direct promotion effect but also through the attenuation of consumers’ price sensitivity. In chapter 3, we consider a semiparametric model of dyadic network formation under NTU. NTU frequently arises in social interactions that require bilateral consent, such as Facebook friendship networks or informal risk-sharing networks in developing countries. However, NTU inherently induces additive non-separability, which makes identification challenging. Based on multivariate monotonicity, we identify structural parameters by constructing events involving the intersection of two mutually exclusive restrictions on the unobserved individual fixed effects to cancel them out. The constructive identification argument leads to a consistent estimator. We analyze the finite-sample performance of the estimator via a simulation study. Then, we apply the method to the Nyakatoke risk-sharing network data. The results show that our approach can capture the essence of the network formation process. For instance, we find that the greater the difference in wealth between two households, the lower is the probability they are connected

Global sensitivity analysis of the climate–vegetation system to astronomical forcing: an emulator-based approach

A global sensitivity analysis is performed to describe the effects of astronomical forcing on the climate–vegetation system simulated by the model of intermediate complexity LOVECLIM in interglacial conditions. The methodology relies on the estimation of sensitivity measures, using a Gaussian process emulator as a fast surrogate of the climate model, calibrated on a set of well-chosen experiments. The outputs considered are the annual mean temperature and precipitation and the growing degree days (GDD). The experiments were run on two distinct land surface schemes to estimate the importance of vegetation feedbacks on climate variance. This analysis provides a spatial description of the variance due to the factors and their combinations, in the form of "fingerprints" obtained from the covariance indices. The results are broadly consistent with the current under-standing of Earth's climate response to the astronomical forcing. In particular, precession and obliquity are found to contribute in LOVECLIM equally to GDD in the Northern Hemisphere, and the effect of obliquity on the response of Southern Hemisphere temperature dominates precession effects. Precession dominates precipitation changes in subtropical areas. Compared to standard approaches based on a small number of simulations, the methodology presented here allows us to identify more systematically regions susceptible to experiencing rapid climate change in response to the smooth astronomical forcing change. In particular, we find that using interactive vegetation significantly enhances the expected rates of climate change, specifically in the Sahel (up to 50% precipitation change in 1000 years) and in the Canadian Arctic region (up to 3° in 1000 years). None of the tested astronomical configurations were found to induce multiple steady states, but, at low obliquity, we observed the development of an oscillatory pattern that has already been reported in LOVECLIM. Although the mathematics of the analysis are fairly straightforward, the emulation approach still requires considerable care in its implementation. We discuss the effect of the choice of length scales and the type of emulator, and estimate uncertainties associated with specific computational aspects, to conclude that the principal component emulator is a good option for this kind of application

On a reduction for a class of resource allocation problems

In the resource allocation problem (RAP), the goal is to divide a given amount of resource over a set of activities while minimizing the cost of this allocation and possibly satisfying constraints on allocations to subsets of the activities. Most solution approaches for the RAP and its extensions allow each activity to have its own cost function. However, in many applications, often the structure of the objective function is the same for each activity and the difference between the cost functions lies in different parameter choices such as, e.g., the multiplicative factors. In this article, we introduce a new class of objective functions that captures the majority of the objectives occurring in studied applications. These objectives are characterized by a shared structure of the cost function depending on two input parameters. We show that, given the two input parameters, there exists a solution to the RAP that is optimal for any choice of the shared structure. As a consequence, this problem reduces to the quadratic RAP, making available the vast amount of solution approaches and algorithms for the latter problem. We show the impact of our reduction result on several applications and, in particular, we improve the best known worst-case complexity bound of two important problems in vessel routing and processor scheduling from $O(n^2)$ to $O(n \log n)$

Essays on State Dependence in the Government Spending Multiplier

This dissertation is comprises three essays. The first attempts to answer the following question. Is fiscal policy more effective, as measured by the government spending multiplier, when the economy is “weak” relative to when it is “strong?” Results in the empirical literature have been mixed on this question. I use local projection techniques to estimate the impulse response functions of real output and real government spending to a shock to military spending. In addition, I attempt to endogenously estimate the level of the unemployment rate that distinguishes between states of the economy. I find that fiscal multipliers are near two at horizons of two to four years when unemployment is relatively high, compared to below 1 when unemployment is low. The second paper seeks to understand why disagreement in the emprical literature is so pervasive and if there are certain modeling choices that systematically lead to particular findings on the state dependence of the government spending multiplier. I identify eight dimensions along which many of the studies in the literature vary and determine if choices along these dimensions have a systematic impact on the results. I conclude that estimation of a state-dependent multiplier is, in general, not robust to various plausible specification assumptions. Finally, I estimate the effect of government spending at the county level using a previously little studied spending program, the Vinson-Trammell Act of 1934. Stimulated by fears about Japanese military expansion, this act aimed to build up the United States Navy to treaty allowances. I am able to identify local areas in the United States that hosted shipyards in 1934, and I estimate the effects of government spending on these areas. I find that manufacturing output, employment, and earnings all rise faster over the course of the 1930s in counties hosting shipyards at the time of the bill’s passage. Also, I see significantly faster growth in county level retail sales and a positive effect on household consumption. Attempting to scale these results to an aggregate government spending multiplier, however, leads to a wide range of estimates for the effect on overall output.Economics, Department o

Generation of nonclassical biphoton states through cascaded quantum walks on a nonlinear chip

We demonstrate a nonlinear optical chip that generates photons with reconfigurable nonclassical spatial correlations. We employ a quadratic nonlinear waveguide array, where photon pairs are generated through spontaneous parametric down-conversion and simultaneously spread through quantum walks between the waveguides. Because of the quantum interference of these cascaded quantum walks, the emerging photons can become entangled over multiple waveguide positions. We experimentally observe highly nonclassical photon-pair correlations, confirming the high fidelity of on-chip quantum interference. Furthermore, we demonstrate biphoton-state tunability by spatial shaping and frequency tuning of the classical pump beam