When we published this article, the Assumption 1 on page 4 contained an error.

The median accumulated investment balance for investors with 10 to 15 years to retirement falls drastically short of what is needed with some studies suggesting more than half the population in this age group have virtually zero savings. Individuals who find themselves in this predicament and intend to make near certain future contributions should consider the present value of these future contributions as a risk-less income stream into their retirement account. With this in mind, early contributions should generally be directed towards 100% equity or similar riskreturn asset classes. Using a simplified 50/50 stock-bond example, adjusting contributions to account for this unrealized stream of "risk-free" cash into the retirement account will increase expected terminal wealth after 15 years by approximately 10% with minimal increases in end of horizon risk, although within-horizon risk is magnified. For those with significant balances, consideration of future contributions is not as critical.

This study compares the equity allocation model relative to the more popular PE, Shiller CAPE, yield spread, Fed Model, and Buffet's Ratio (Market Cap/GDP) to predict long-term stock market returns. Although all the variables are related to long-run stock returns, only equity allocation and yield spread have root mean square errors consistently lower than a simple moving average. A simple trading rule transferring wealth between equity and 10-year T-bonds demonstrates equity allocation performs best with a 1.3% annual outperformance relative to buy-and-hold from 1990 to 2018. However, the predictive ability of the ratio was not identified until 2013 and since then, the trading strategy has underperformed by 1.5% annually. Thus, despite equity allocation's initial glamour, its long-term predictive ability does not appear to be easily transformed into profitable trading.

Under the 2017 tax law, carryforward rules for net operating losses (NOLs) allow corporations to apply these losses forward for up to twenty years of taxable income. Startup corporations primarily engaged in basic research or that require rapid growth to be sustainable can go a number years with no positive earnings and thus accumulate net operating losses. These losses can be used to reduce tax obligations in the future. This paper estimates the value of NOLs across firms in a specific subindustry: small cap biotechnology. A valuation method based on ARIMA estimates of future income is used to calculate the value of the NOLs that can be carried forward. The results indicate that even for a subindustry which typically has net operating losses for many years in a startup phase, the expected value of the future benefit of a reduction in taxes is actually fairly modest.

The aim of this thesis is the development and the application of econometric models with time-varying parameters in a policy environment. The popularity of these methods has run in parallel with advances in computing power, which has made feasible estimation methods that until the late '90s would have been unfeasible. Bayesian methods, in particular, benefitted from these technological advances, as sampling from complicated posterior distributions of the model parameters became less and less time-consuming. Building on the seminal work by Carter and Kohn (1994) and Jacquier, Polson, and Rossi (1994), bayesian algorithms for estimating Vector Autoregressions (VARs) with drifting coefficients and volatility were independently derived by Cogley and Sargent (2005) and Primiceri (2005). Despite their increased popularity, bayesian methods still suffer from some limitations, from both a theoretical and a practical viewpoint. First, they typically assume that parameters evolve as independent driftless random walks. It is therefore unclear whether the output that one obtains from these estimators is accurate when the model parameters are generated by a different stochastic process. Second, some computational limitations remain as only a limited number of time series can be jointly modeled in this environment. These shortcomings have prompted a new line of research that uses non-parametric methods to estimate random time-varying coefficients models. Giraitis, Kapetanios, and Yates (2014) develop kernel estimators for autoregressive models with random time-varying coefficients and derive the conditions under which such estimators consistently recover the true path of the model coefficients. The method has been suitably adapted by Giraitis, Kapetanios, and Yates (2012) to a multivariate context. In this thesis I make use of both bayesian and non-parametric methods, adapting them (and in some cases extending them) to answer some of the research questions that, as a Central Bank economist, I have been tackling in the past five years. The variety of empirical exercises proposed throughout the work testifies the wide range of applicability of these models, be it in the area of macroeconomic forecasting (both at short and long horizons) or in the investigation of structural change in the relationship among macroeconomic variables. The first chapter develops a mixed frequency dynamic factor model in which the disturbances of both the latent common factor and of the idiosyncratic components have time varying stochastic volatility. The model is used to investigate business cycle dynamics in the euro area, and to perform point and density forecast. The main result is that introducing stochastic volatility in the model contributes to an improvement in both point and density forecast accuracy. Chapter 2 introduces a nonparametric estimation method for a large Vector Autoregression (VAR) with time-varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs (FAVAR). When applied to the problem of forecasting key macroeconomic variables, the method outperforms constant parameter benchmarks and large Bayesian VARs with time-varying parameters. The tool is also used for structural analysis to study the time-varying effects of oil price innovations on sectorial U.S. industrial output. Chapter 3 uses a bayesian VAR to provide novel evidence on changes in the relationship between the real price of oil and real exports in the euro area. By combining robust predictions on the sign of the impulse responses obtained from a theoretical model with restrictions on the slope of the oil demand and oil supply curves, oil supply and foreign productivity shocks are identified. The main finding is that from the 1980s onwards the relationship between oil prices and euro area exports has become less negative conditional on oil supply shortfalls and more positive conditional on foreign productivity shocks. A general equilibrium model is used to shed some light on the plausible reasons for these changes. Chapter 4 investigates the failure of conventional constant parameter models in anticipating the sharp fall in inflation in the euro area in 2013- 2014. This forecasting failure can be partly attributed to a break in the elasticity of inflation to the output gap. Using structural break tests and non-parametric time varying parameter models this study shows that this elasticity has indeed increased substantially after 2013. Two structural interpretations of this finding are offered. The first is that the increase in the cyclicality of inflation has stemmed from lower nominal rigidities or weaker strategic complementarity in price setting. A second possibility is that real time output gap estimates are understating the amount of spare capacity in the economy. I estimate that, in order to reconcile the observed fall in inflation with the historical correlation between consumer prices and the business cycle, the output gap should be wider by around one third.

This thesis focuses on three different empirical questions related to the economic incentives to the formation of identity. The first chapter examines the dynamics of occupational segregation of self-identified homosexual workers, specifically accounting for the fact that part of their observed distribution may come from selective disclosure of sexual identity. We present a simple labour supply model where individuals choose both an occupation and their revealed sexual identity. Using confidential data from the UK, we show that selfreported homosexuals are concentrated in opposite gender typical occupations. As tolerance increases, a greater fraction of homosexuals reports their homosexual identity, particularly in marginal homosexual occupations, hence occupational segregation falls. The finding suggests that part of the observed segregation of homosexuals in opposite sex occupations is due to selective disclosure rather tastes or comparative advantage. The second chapter uses an original dataset covering the universe of local elections in England spanning over 40 years to investigate whether the electoral success of women and ethnic minorities leads to increases in these groups' representation as political candidates in subsequent elections. Using a regression discontinuity approach, we find that both groups enjoy a personal incumbency advantage. One direct consequence is an increase in the fraction of women and ethnic minority candidates contesting a seat previously held by someone from the same group. In the case of women, this increase is also driven by an inflow of new women candidates. The third chapter focuses on the impact of television on religious identity. We use detailed survey data on individuals' self-reported religious sentiment, behaviours and attitudes from Indonesia. We use the variation in signal reception due to geographic topography at the sub-district level to estimate the causal effect of media exposure. Individuals exposed to a higher number of television channels are less likely to report being religious and following religious practices. Furthermore, they also display lower interfaith hostility. At the village level, higher exposure to television increases the supply of religiously forbidden activities. However, higher exposure to television seems to have no effect on political preferences.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (site viewed May 23, 2007). PDF text: vi, 149 p. : col. ill., col. maps ; 1.89Mb. UMI publication number: AAT 3237052. Includes bibliographical references. Also available in microfilm and microfiche formats.

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Iran, as most countries in the Middle East, has a natural competitive advantage in the global tourism industry. However, the potential of this competitive advantage has not been reached. This research is designed to provide Iranian policy makers with a systematic approach toward understanding and narrowing the gap which exists between tourism potential and market realities. The Constant Market Share (CMS) model is used toward that end. The model is used to analyze two sets of actual data related to tourist arrivals. Based on the results of this study, some policy implications are advanced.

The Middle East is the cradle of civilization and the birthplace of the three major religions practiced today. As such, most countries in the Middle East have a natural competitive advantage in the global tourism industry. However, for many of these countries the potential of this competitive advantage has not been reached. This research is designed to provide policy makers with a systematic approach toward restructuring their tourism strategies. Based on the results of this study, some significant policy implications are identified. While the methodology presented in this study is not without some weakness, it can be easily applied to other countries and regions. In this context, four Middle Eastern countries are used to illustrate the approach proposed in this study, J BUSN RES 2000. 48.147-156.