Introduction: There is growing evidence for increased prevalence of poor adolescent mental health, including depression, in the United States. Increases in adolescent depression beginning around 2008-2010 coincided with the timing of the Great Recession and there are plausible mechanisms through which economic recessions may influence adolescent depression (e.g., caregiver job loss, household economic hardship). More research is needed to understand the potential relationship between the 2007-2009 Great Recession and long-term impacts on mental health by household poverty, as many mechanisms (e.g., cumulative familial stress) can impact adolescent mental health after the peak of a recession passes. The objective of this dissertation is to examine the associations between economic recessions and adolescent depression. This dissertation includes five chapters: first, an introduction; second, a literature review to examine evidence of time trends and birth cohort effects in depressive disorders and symptoms among adolescents in recent years; third, an empirical study to assess changes in adolescent depression and depression treatment, including differences by household poverty occurring at the beginning of the Great Recession; fourth, an empirical study to estimate potential longer-term impacts of the Great Recession by examining whether young adults from birth cohorts who were adolescents at the time of the Great Recession had higher risk of MDE and mental health treatment use as young adults compared with birth cohorts who were adolescents and surveyed prior to the Great Recession with potential differences by household poverty; and fifth, a conclusion to summarize results and discuss implications for future research. Methods: The integrative systematic literature review included 10 studies related to the United States, adolescent populations, birth year and time trends, and depressive symptoms or disorders. The two empirical aims utilized data from the National Survey on Drug Use and Health (NSDUH), a national survey assessing behavioral health among participants aged 12 and older. For the first empirical aim, I analyzed data for adolescents ages 12-17 participating in the 2004-2019 NSDUH (N = 256,572). For the second empirical aim, I included young adults ages 18-29 from the 2005-2019 NSDUH (N = 135,158). For this aim, the main exposure measure was belonging to birth cohorts (1990-1994) who were adolescents during the Great Recession and surveyed in 2008-2019 versus those from birth cohorts (1976-1989) that did not experience the Great Recession and were surveyed prior to the Great Recession in 2005-2007. For both empirical aims, I measured past year DSM-IV and DSM-5 major depressive episodes (MDE) from self-reported symptoms. MDE treatment was assessed among those with past year MDE, excluding those who were already successfully treated for MDE. For the first empirical aim, I tested how MDE and MDE treatment conditioned on MDE changed from pre-Great Recession (2004 to Fall 2007) to post-Great Recession (Winter 2007 to 2019) using interrupted time-series (ITS) segmented regression models accounting for seasonality (January-March, April-June, July-September, October-December) and autocorrelation. For the second empirical aim, regression models assessed the relationships between the birth cohort exposure measure and MDE and mental health treatment utilization adjusting for age, gender, race/ethnicity, educational attainment, and insurance status. Both empirical aims tested effect modification by household poverty. Results: The review of 10 studies found increases in depressive symptoms and disorders in adolescents across recent survey years with increases observed between 1991 and 2020. Of the 3 articles that assessed birth cohort trends, birth cohort trends were less prominent than time period trends. Proposed explanations for increases included social media, economic-related reasons, changes in mental health screening and diagnosis, changes to mental health stigma and treatment and, in more recent years, the COVID-19 pandemic. In the first empirical study, I illustrated that the Great Recession was not associated with an immediate change in MDE prevalence (β: -0.77, 95% CI: -2.23, 0.69). However, following the Great Recession, the increase in MDE prevalence accelerated (β: 0.29, 95% CI: 0.13, 0.44). The Great Recession was not associated with acute changes in adolescent MDE treatment (β: -2.87, 95% CI: -7.79, 2.04) nor longer-term slope effects (β: 0.03, 95% CI: -0.46, 0.51). Evidence of interaction by household poverty was not observed for either the MDE or MDE treatment outcome. In the second empirical aim, interaction between the birth cohort exposure and household poverty was observed for MDE (F=10.17, df=2, p=<0.0001), but not for mental health treatment use. Great Recession exposure effects were stronger among those at higher levels of household income. For example, among young adults who were living in households at two times the poverty threshold, those from birth cohorts who were exposed during adolescence to the Great Recession had higher odds of MDE compared with young adults from birth cohorts who were unexposed during adolescence to the Great Recession (adjusted odds ratio= 1.16, 95% CI= 1.04, 1.29). Conclusions: Multiple cross-sectional surveys and cohort studies documented rising prevalence of depressive symptoms and disorder among adolescents from 1991-2020. The Great Recession coincided with accelerated trends of increasing MDE, but not MDE treatment of these adolescents. Contrary to my hypothesis, the strength of changes in the rate of increase in MDE did not differ by household poverty and adolescents from households living in poverty, who likely experienced a greater financial burden during the recession, did not experience an increase in the rate of MDE. Birth cohort effects by household poverty were observed and exposure to the Great Recession during adolescence was associated with long-term effects on MDE, but not mental health treatment utilization, during young adulthood compared with those not exposed to the Great Recession. Young adults from higher income households who were exposed to the Great Recession had heightened likelihood of MDE. Future research should explore alternative drivers of MDE during the 2010s, as poverty-specific cohort analyses did not show that those living in poverty who likely experienced the greatest burden of a recession financially had increased risk of MDE.

"The problem investigated in this thesis is that of determining the impact of the disestablishment of Camp Edward Gary on the economy of the city of San Marcos, Texas...it is anticipated that this study may contribute two additional outcomes of value: the first is a test of certain ideas in economic theory pertaining to recessions; and the second is an evaluation of the data pertaining to the economy of small communities...the data presented in Chapter II and the summarization of that information in Chapter III lead to the inevitable conclusion that the deactivation of Camp Edward Gary caused a recession in the City of San Marcos, Texas, which was shared in varying degree by virtually every element of the economy...it is further concluded that the impact of the loss of the military community was modified to some degree by the beneficial effects of the growth in its educational institutions and the fact that the loss was shared, although in a lesser degree, by other communities in the general area." --leaves 4, 5, 79

For an introductory macroeconomics course, the discussion of historical relevance helps foster important learning connections. By comparing the Great Depression to the 2008-2009 recession, a macroeconomics instructor can provide students with connections to history. This paper discusses the major causes of each recession, major fiscal policy and monetary policy decisions of both recessions, and the respective relevance in teaching the relationship of each policy to gross domestic product. The teaching points addressed in this paper are directed towards an introductory college-level macroeconomics course, incorporating a variety of theories from historical and economic writers and data from government and central bank sources. A lesson plan is included in an appendix to assist the instructor in implementing the material.

This doctoral thesis is a collection of four papers on the analysis of the term structure of interest rates with a focus at the intersection of macroeconomics and finance. "Risk in Macroeconomic Fundamentals and Bond Return Predictability" documents that factors related to risks underlying the macroeconomy such as expectations, uncertainty and downside (upside) macroeconomic risks are able to explain variation in bond risk premia. The information provided is found to be, to a large extent, unrelated to that contained in forward rates and current macroeconomic conditions. "Out-of-sample bond excess returns predictability" provides evidence that macroeconomic variables, risks in macroeconomic outcomes as well as the combination of these different sources of information are able to generate statistical as well as economic bond excess returns predictability in an out-of-sample setting. Results suggest that this finding is not driven by revisions in macroeconomic data. The term spread (yield curve slope) is largely used as an indicator of future economic activity. "Re-examining the predictive power of the yield curve with quantile regression" provides new evidence on the predictive ability of the term spread by studying the whole conditional distribution of GDP growth. "Modeling and forecasting the yield curve by extended Nelson-Siegel class of models: a quantile regression approach" deals with yield curve prediction. More flexible Nelson-Siegel models are found to provide better fitting to the data, even when penalizing for additional model complexity. For the forecasting exercise, quantile-based models are found to overcome all competitors. / <p>Diss. Stockholm :  Stockholm School of Economics, 2014. Introduction together with 4 papers.</p>

La « Grande Récession » de 2008-2009 ou encore l'aggravation de la crise des dettes souveraines et de la dette publique dans la zone euro à l'été 2011, constituent de récents événements qui ont cristallisé les enjeux de l'analyse conjoncturelle, ceux relatifs notamment à la datation et à la prévision des inflexions cycliques de l'activité réelle. L'objet de cette thèse s'inscrit fondamentalement au sein de ces deux approches complémentaires du cycle économique.Le chapitre 1 dresse un portrait du cycle autour de trois conceptions distinctes de ses points de retournement : le cycle classique, le cycle de croissance et le cycle d'accélération. Nous discutons également de sa mesure eu égard aux diverses représentations possibles de l'activité agrégée d'un pays, ainsi qu'aux deux traditions existantes dans lesquelles s'inscrivent les modèles de datation. Nous mettons par ailleurs en lumière l'influence grandissante de l'environnement financier sur la dynamique cyclique des économies. Le chapitre 2 nous amène à développer deux algorithmes non-paramétriques dans le but de repérer les inflexions propres à chacun des cycles auparavant conceptualisés, mais aussipour en mesurer leurs principales caractéristiques. Le premier (resp. le second) algorithme repose sur une représentation univariée (resp. multivariée) de l'activité économique globale ; in fine, nous les appliquons aux données de la conjoncture française entre 1970 et 2010. Le chapitre 3 tire parti de nos résultats en matière de datation conjoncturelle afin de prévoir les récessions françaises depuis 1974. Au moyen de modèles probits, nous illustrons le rôle de variables financières et monétaires en tant qu'indicateurs avancés des fluctuations du cycle des affaires français. Nous montrons de plus que nos modèles prédictifs assurent uneparfaite détection des récessions pour un horizon égal à deux trimestres.Le chapitre 4 prolonge l'ensemble de l'analyse à plusieurs États membres de la zoneeuro, ces derniers étant observés depuis 1979. Nous construisons d'abord une chronologie de leurs cycles classiques respectifs puis, nous proposons un examen de leurs caractéristiques moyennes et de leur degré de synchronisation. Enfin, en s'appuyant sur des indicateurs financiers et monétaires dans le cadre d'un probit dynamique à effets fixes, nous parvenons à anticiper - jusqu'à un horizon de deux trimestres - les épisodes récessifs survenus dans les économies considérées. / The « Great Recession » of 2008-2009 and the sovereign and public debt crises which strengthened in the euro area in the summer of 2011 are recent events that have crystallized the challenges facing economic analysis, especially those related to dating and predicting cyclical inflections of real activity. The purpose of this thesis is to study these two complementary approaches to the economic cycle. Chapter 1 provides a portrait of the cycle using three distinct conceptions of its turning points: the classical cycle, the growth cycle and the acceleration cycle. We also discuss the measurement of the cycle with respect to various possible representations of aggregate activity of a country, as well as to two existing traditions which encompass dating models. Moreover, we highlight the growing influence of the financial environment over business cycle fluctuations.In chapter 2, we develop two non-parametric algorithms in order to identify theinflections that are particular to each of the previously conceptualized cycles, but also to measure their main characteristics. The first algorithm is based on a univariate representation of overall economic activity, the second on its ultivariate representation; ultimately, we apply the algorithms to the data of the French economy between 1970 and 2010. Chapter 3 builds on our results for cyclical dating to predict French recessions since 1974. Using probit models, we illustrate the role of monetary and financial variables as leading indicators of French business cycle fluctuations. In addition, we show that our models accurately detect recessions for a forecasting lag of two-quarters. Chapter 4 extends the entire analysis to several member states of the euro zone, with observations beginning in 1979. We first construct a chronology of their classical cycles, and then we propose an analysis of their main characteristics and their degree of synchronization.Finally, based on financial and monetary indicators in the context of a dynamic probit with fixed effects, we can anticipate the recessionary episodes which occurred in these economies with a horizon of two quarters.

<h2><br></h2> <p>This dissertation consists of distinct but related essays that delve into the impacts of changing economic conditions and climate mitigation policies on household consumption, health, and welfare outcomes. The first essay examines the effect of variations in economic factors, such as home values, on unhealthy consumption behaviors in the U.S. The second essay examines the distributional effects and possible health advantages of climate mitigation policies in India. The findings in this dissertation have significant implications for preventive health and environmental justice policies, particularly concerning vulnerable populations. </p> <p>The first essay of this dissertation investigates the impact of home value fluctuations on household tobacco and alcohol consumption in the U.S., specifically focusing on consumption based on homeownership status. First, we utilize high-frequency household transaction panel data and ZIP code-level home values to estimate the causal effect of home value fluctuations (or the housing wealth effect) on household tobacco and alcohol consumption for all U.S. households. Second, we predict household homeownership status by supplementing our primary household panel transaction data with a secondary household survey dataset; this allowed us to estimate the housing wealth effect separately for homeowners and renters. Home values are a leading economic indicator and effectively represent variation in housing wealth, whereas prior literature mainly focuses on lagging economic indicators, such as the unemployment rate. Housing wealth is a significant component of household net worth in the U.S. We leverage temporal and geographic fluctuations in household transactions and local home values to show that changes in housing wealth have a causal effect on household tobacco and alcohol consumption. Our findings show that declining home values increase tobacco and alcohol consumption among homeowners, with no effect on renters. Beer and cigarettes mainly drive this effect. Declining home values substantially increase annual consumption of nicotine, tar, carbon monoxide, and alcohol by volume, exacerbating public health concerns. In contrast, unemployment shocks increase tobacco and alcohol consumption among homeowners and decrease it among renters. The housing wealth effect is most pronounced among bubble states households, heavy-use consumers, low-income, and white households. The study emphasizes the importance of targeted policy interventions to mitigate the negative effects of fluctuations in housing wealth on unhealthy consumption, especially amid the current unpredictable economic environment and volatile real estate market. </p> <p>The second essay of this dissertation analyzes the distributional impacts of climate mitigation policies consistent with India’s 2030 Nationally Determined Contribution and 2070 net-zero target, using a dynamic global computable general equilibrium (CGE) model with heterogeneous Indian households. Specifically, we expand the CGE model to incorporate ten rural and ten urban household income deciles. Additionally, we link the CGE model with a global atmospheric source-receptor model to derive health co-benefits from reduced premature mortality due to lower air pollution. Several policy levers are considered in this study, including carbon pricing, enhanced coal consumption tax (or coal cess), and fossil subsidies phaseout. These are further combined with five alternative revenue recycling options. Our results suggest the potential welfare costs of such mitigation policies are rather moderate and do not exceed 0.5% over 2023-2050, not accounting for health and environmental co-benefits and damages avoided by successfully limiting global temperature rise to well below 2°C. However, health co-benefits from lower air pollution can potentially outweigh the mitigation costs. Combining carbon pricing and fossil subsidy removal is more efficient than carbon pricing alone, generating progressive medium-term welfare gains due to reduced market distortions. Raising coal cess rates is the least efficient policy. Inequality and distributional impacts vary significantly based on the chosen revenue recycling approach. Equal transfer of tax revenue across households proves to be the most efficient and equitable, followed by labor tax subsidies, leading to a Gini index and S20/S80 ratio reduction of 0.01%-1.7% and 0.1%-7%, respectively. Recycling revenues to stimulate green energy investments yields the least favorable distributional impacts and worsens inequality. Trade-offs exist between reducing inequality and fostering investment-driven economic growth when choosing revenue recycling options. Policymakers should prioritize policy mixes and revenue-recycling methods based on their objectives to effectively combat climate change while promoting sustainable growth and reducing income inequality in India. </p>

CHAPTER 1:<p>The default g-priors predominant in Bayesian Model Averaging tend to over-concentrate posterior mass on a tiny set of models - a feature we denote as 'supermodel effect'. To address it, we propose a 'hyper-g' prior specification, whose data-dependent shrinkage adapts posterior model distributions to data quality. We demonstrate the asymptotic consistency of the hyper-g prior, and its interpretation as a goodness-of-fit indicator. Moreover, we highlight the similarities between hyper-g and 'Empirical Bayes' priors, and introduce closed-form expressions essential to computationally feasibility. The robustness of the hyper-g prior is demonstrated via simulation analysis, and by comparing four vintages of economic growth data.<p><p>CHAPTER 2:<p>Ciccone and Jarocinski (2010) show that inference in Bayesian Model Averaging (BMA) can be highly sensitive to small data perturbations. In particular they demonstrate that the importance attributed to potential growth determinants varies tremendously over different revisions of international income data. They conclude that 'agnostic' priors appear too sensitive for this strand of growth empirics. In response, we show that the found instability owes much to a specific BMA set-up: First, comparing the same countries over data revisions improves robustness. Second, much of the remaining variation can be reduced by applying an evenly 'agnostic', but flexible prior.<p><p>CHAPTER 3:<p>This chapter explores the link between the leverage of the US financial sector, of households and of non-financial businesses, and real activity. We document that leverage is negatively correlated with the future growth of real activity, and positively linked to the conditional volatility of future real activity and of equity returns. <p>The joint information in sectoral leverage series is more relevant for predicting future real activity than the information contained in any individual leverage series. Using in-sample regressions and out-of sample forecasts, we show that the predictive power of leverage is roughly comparable to that of macro and financial predictors commonly used by forecasters. <p>Leverage information would not have allowed to predict the 'Great Recession' of 2008-2009 any better than conventional macro/financial predictors. <p><p>CHAPTER 4:<p>Model averaging has proven popular for inference with many potential predictors in small samples. However, it is frequently criticized for a lack of robustness with respect to prediction and inference. This chapter explores the reasons for such robustness problems and proposes to address them by transforming the subset of potential 'control' predictors into principal components in suitable datasets. A simulation analysis shows that this approach yields robustness advantages vs. both standard model averaging and principal component-augmented regression. Moreover, we devise a prior framework that extends model averaging to uncertainty over the set of principal components and show that it offers considerable improvements with respect to the robustness of estimates and inference about the importance of covariates. Finally, we empirically benchmark our approach with popular model averaging and PC-based techniques in evaluating financial indicators as alternatives to established macroeconomic predictors of real economic activity. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished