Wang Xin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 61-63). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction and Literature Review --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- The Objective --- p.2 / Chapter 1.3 --- Literature Review --- p.3 / Chapter Chapter 2 --- The Model --- p.7 / Chapter Chapter 3 --- The Consistent Estimation of Chang-Point --- p.11 / Chapter 3.1 --- The Consistency of ιT --- p.11 / Chapter 3.2 --- The Case of Single Regressor --- p.15 / Chapter Chapter 4 --- The SupWald Test and Sample-Splitting Method --- p.20 / Chapter 4.1 --- The SupWald Test --- p.20 / Chapter 4.2 --- " The Distribution of WT (ι,0,1)" --- p.23 / Chapter 4.3 --- The Sample-Splitting Method --- p.25 / Chapter Chapter 5 --- Experimental Results --- p.27 / Chapter Chapter 6 --- Empirical Example --- p.41 / Chapter Chapter 7 --- Conclusion --- p.44 / Appendix --- p.46 / References --- p.61

This thesis comprehends four essays investigating strategies to fight against poverty. The first essay explores a series of police operations to pacify the slums of Rio de Janeiro to understand the impacts of intrauterine exposure to violence on birth outcomes. One argues that pregnancies starting before, but ending around the pacification dates are ‘quasirandomly’ exposed to exogenous shocks of violence during pregnancy. The results show that each month pregnant women are exposed to pacification increases birth weights by 4 grams and reduces the probability of low birth weight (< 2500 grams) by 1.2 percent compared to pregnancies ending just before pacifications. A second essay uses Brazilian legislative change making it mandatory for private hospitals to publicly disclose information about physicians’ performance. The results show a reduction in scheduled C-sections by 4.8 percent; which two-thirds originating from physicians anticipating to information disclosure. The third essay proposes an empirical strategy to estimate bullying effects on labour and schooling outcomes when "true" bullying is observed inaccurately. The estimates show that high-school bullying decreases University attendance by 3.4 percent and increases the probability of being not in education, employed or in training after high-school by 2.8 percent. Estimations neglecting misreport implicates in impacts two-thirds smaller. And finally, the fourth essay shows that poor households increase their participation in social groups after receiving Bolsa Família. The strategy explores households registered in Cadastro Único, and performs a propensity score difference-in-difference framework to minimize selection bias. Becoming a recipient of Bolsa Família increases .09 standard deviations the number of social affiliation and increase from 6.1 to 8.9 percent the probability of engaging in social groups. Altogether, this thesis implicates that investing in early stages of life harvest significant benefits to disadvantaged children, it also shows that victims of bullying need sustained support after high school, and that conditional cash transfers foster social engagement.

My dissertation consists of three chapters that focus on panel data models in econometrics and under high dimensionality; that is, both the number of individuals and the number of time periods are large. This high dimensionality is widely applicable in practice, as economists increasingly face large dimensional data sets. This dissertation contributes to the methodology and techniques that deal with large data sets. All the models studied in the three chapters contain a factor structure, which provides various ways to extract information from large data sets. Chapter 1 and Chapter 2 use the factor structure to capture the comovement of economic variables, where the factors represent the common shocks and the factor loadings represent the heterogeneous responses to these shocks. Common shocks are widely present in the real world, for example, global financial shocks, macroeconomic shocks and energy price shocks. In applications where common shocks exist, failing to capture these common shocks would lead to biased estimation. Factor models provide a way to capture these common shocks. In contrast to Chapter 1 and Chapter 2, Chapter 3 directly focuses on the factor model with the loadings being constrained, in order to reduce the number of parameters to be estimated. In addition to the common shocks effect, Chapter 1 considers two other effects: spatial effects and simultaneous effects. The spatial effect is present in models where dependent variables are spatially interacted and spatial weights are specified based on location and distance, in a geographic space or in more general economic, social or network spaces. The simultaneous effect comes from the endogeneity of the dependent variables in a simultaneous equations system, and it is important in many structural economic models. A model including all these three effects would be useful in various fields. In estimation, all the three chapters propose quasi-maximum likelihood (QML) based estimation methods and further study the asymptotic properties of these estimators by providing a full inferential theory, which includes consistency, convergence rate and limiting distribution. Moreover, I conduct Monte-Carlo simulations to investigate the finite sample performance of these proposed estimators. Specifically, Chapter 1 considers a simultaneous spatial panel data model with common shocks. Chapter 2 studies a panel data model with heterogenous coefficients and common shocks. Chapter 3 studies a high dimensional constrained factor model. In Chapter 1, I consider a simultaneous spatial panel data model, jointly modeling three effects: simultaneous effects, spatial effects and common shock effects. This joint modeling and consideration of cross-sectional heteroskedasticity result in a large number of incidental parameters. I propose two estimation approaches, a QML method and an iterative generalized principal components (IGPC) method. I develop full inferential theories for the two estimation approaches and study the trade-off between the model specifications and their respective asymptotic properties. I further investigate the finite sample performance of both methods using Monte-Carlo simulations. I find that both methods perform well and that the simulation results corroborate the inferential theories. Some extensions of the model are considered. Finally, I apply the model to analyze the relationship between trade and GDP using a panel data over time and across countries. Chapter 2 investigates efficient estimation of heterogeneous coefficients in panel data models with common shocks, which have been a particular focus of recent theoretical and empirical literature. It proposes a new two-step method to estimate the heterogeneous coefficients. In the first step, a QML method is first conducted to estimate the loadings and idiosyncratic variances. The second step estimates the heterogeneous coefficients by using the structural relations implied by the model and replacing the unknown parameters with their QML estimates. Further, Chapter 2 establishes the asymptotic theory of the estimator, including consistency, asymptotic representation, and limiting distribution. The two-step estimator is asymptotically efficient in the sense that it has the same limiting distribution as the infeasible generalized least squares (GLS) estimator. Intensive Monte-Carlo simulations show that the proposed estimator performs robustly in a variety of data setups. Chapter 3 documents the estimation and inferential theory of high dimensional constrained factor models. Factor models have been widely used in practice. However, an undesirable feature of a high dimensional factor model is that the model has too many parameters. An effective way to address this issue, proposed in Tsai and Tsay (2010), is to decompose the loadings matrix by a high-dimensional known matrix multiplying with a low-dimensional unknown matrix, which Tsai and Tsay (2010) name the constrained factor models. Chapter 3 proposes a QML method to estimate the model and develops the asymptotic properties of its estimators. A new statistic is proposed for testing the null hypothesis of constrained factor models against the alternative of standard factor models. Partially constrained factor models are also investigated. Monte-Carlo simulations confirm the theoretical results and show that the QML estimators and the proposed new statistic perform well in finite samples. Chapter 3 also considers the extension to an approximate constrained factor model where the idiosyncratic errors are allowed to be weakly dependent processes.

This thesis investigates the macroeconomic effects of domestic oil prices and international crude oil prices in Ghana. We investigate the ability of oil prices to influence economic growth in Ghana using annual data from 1971 to 2014. We also examine the possibility of shock spillover and volatility spillover effects from domestic and crude oil prices to the Ghana currency exchange rates and the Ghana stock market index using monthly data from January 1991 to December 2015. To conduct these investigations, this study employed various econometric techniques including; unit root testing, cointegration testing, vector autoregressive model (VAR), structural VAR (SVAR), vector error correction model (VECM), scenario-based dynamic forecasting, the autoregressive distributive lag (ARDL) specification, and the generalized autoregressive conditional heteroscedasticity (GARCH) BEKK model. Overall, this study seeks to address two central issues; i) whether domestic and world oil prices have the same effect on economic activities and financial variables in Ghana, and ii) whether the crude oil price and the macro economy relationship in Ghana is related to the treatment of crude oil prices as exogenous or endogenous. It is important to recognize the exogeneity of crude oil prices in the context of Ghana given the relatively small size of the Ghanaian economy. The findings suggest that the international crude oil price movements have an insignificant effect on output growth in Ghana both in the short run and in the long, regardless of whether the crude oil price is treated as exogenous or endogenous. However, domestic oil prices have a significant effect on the output growth rate only in the long run. The findings also indicate that world crude oil prices have significant spillover effects on the exchange rate, and this result is unaffected by the treatment of world crude oil prices as exogenous or endogenous. However, the relationship between crude oil prices and the Ghana stock market depends on whether the crude oil price is exogenous or endogenous. In addition, domestic oil prices have significant spillover effects on the exchange rate and the stock market. Domestic oil prices are also found to have more influence on the stock market than crude oil prices do. The results of this study have some implications for the government and investors; (i) Increases in crude oil prices do not put a binding constraint on the monetary authorities to loosen monetary policy to offset its effect on output. If inflation is a priority, policy makers could focus on inflation stabilization by tightening monetary policy during oil price rises. (ii) The government?s tax policies on petroleum products should not only be focused on revenue generation, but also on ensuring that such policies do not lead to exorbitant domestic oil prices since higher taxes on petroleum products will increase domestic oil prices which can be detrimental to the economy in the long term. (iii) The government should formulate transport-related policies such as promoting mass transportation or encouraging the use of electrically powered vehicles. The government can also encourage the use of renewable energy such as solar to help reduce the country?s dependence on oil (iv) Internationally diversified portfolio investors in Ghana should use hedging strategies such as currency forwards, futures, and options to protect their investments from exchange rate risk emanating from oil price shocks.

The fundamental objective of the analysis of financial time series is to unveil the random mechanism, i.e. the probability law, underlying financial data. The effort to identify the truth that governs the observations involves proposing and estimating reasonable statistical models that well explain the empirical features of data. This thesis develops some new nonparametric tools that can be exploited in this context; the efficacy and validity of their use are supported by computational advancements and surging availability of large/complex (`big') data sets. Chapter 1 investigates the conditional first moment properties of financial returns. We propose multivariate extensions of the popular Variance Ratio (VR) statistic, aiming to test linear predictability of returns and weak-form market efficiency. We construct asymptotic distribution theories for the statistics and scalar functions thereof under the null hypothesis of no predictability. The imposed assumptions are weaker than those widely adopted in the literature, and in our view more credible with regard to the underlying data generating process we expect for stock returns. It is also shown that the limit theories can be extended to the long horizon and large dimension cases, and also to allow for a time varying risk premium. Our methods are applied to CRSP weekly returns from 1962 to 2013; the joint tests of the multivariate hypothesis reject the null at the 1% level for all horizons considered. Chapter 2 is about nonparametric estimation of conditional moments. We propose a local constant type estimator that operates with an infinite number of conditioning variables; this enables a direct estimation of many objects of econometric interest that have dependence upon the infinite past. We show pointwise and uniform consistency of the estimator and establish its asymptotic nomality in various static and dynamic regressions context. The optimal rate of estimation turns out to be of logarithmic order, and the precise rate depends on the Lambert W function, the smoothness of the regression operator and the dependence of the data in a non-trivial way. The theories are applied to investigate the intertemporal risk-return relation for the aggregate stock market. We report an overall positive risk-return relation on the S&P 500 daily data from 1950-2017, and find evidence of strong time variation and counter-cyclical behaviour in risk aversion. Lastly, Chapter 3 concerns nonparametric volatility estimation with high frequency time series. While data observed at finer time scale than daily provide rich information, their distinctive empirical properties bring new challenges in their analysis. We propose a Fourier domain based estimator for multivariate ex-post volatility that is robust to two major hurdles in high frequency finance: asynchronicity in observations and the presence of microstructure noise. Asymptotic properties are derived under some mild conditions. Simulation studies show our method outperforms time domain estimators when two assets with different liquidity are traded asynchronously.

The BRICS (Brazil, the Russian Federation, India, China and South Africa) economies, as the major emerging markets, continue to influence the transformation of the global structure. Their role within the global governance and global economy is increasing day by day. This study is an analysis of the determinants of foreign direct investment (FDI) in a panel of bilateral FOI data from thirty-two the Organisation for Economic Co-operation and Development (DECO) countries in the BRICS countries for the period of 1992 to 2010. Applying the extended gravity model with the home country push factors and the host country pull factors, the empirical findings of this study confirms that market size, distance, common culture, trade openness, privatisation, relative labor cost, research and development (R&D), relative exchange rate, infrastructure and institutional factors are the key determinants of FDI in the BRICS countries. Furthermore, bilateral investment treaties (BITs) are significant at 10% level for FDI flows. The home country corporate tax rate and bilateral export are inconclusive. In this aspect, there are evidences to accept that the motive of FDI in the BRICS economies is principally are market seeking, natural resource seeking and partly efficiency seeking type of FDI. Employing a standard gravity model, within limited scope this study also attempts to examine the impact of the 2008 global financial crisis on the BRICS countries' exports. The author use a panel data of BRICS countries' bilateral exports to the top ten major destination countries for the period of 2000 to 2011 by including a dummy variable to measure the impact of the global financial crisis. The result confirms that the coefficient estimate for the crisis dummy variable is positive and statistically significant at 1% level for fixed effects (FE) model. The result specifies that during the global financial crisis periods, the BRICS countries' exports growth rate increased relative to the non-financial crisis period. Furthermore, model approves that GOP growth rate and population are an also important factor that affects the value of the BRICS countries' exports.

This paper uses the 2010 Mexican Population and Housing Survey to examine the role of regional return to education on migrant selection. The study uses a standard linear regression model to predict the educational attainment of migrants and compares it to the educational attainment of non-migrants in each Mexican State. It finds evidence of negative selection, that less educated Mexican citizens are more likely to migrate to the United States. It also finds little evidence of the impact of regional return to education on migrant selection. The study offers potential explanations for the lack of impact and suggests avenues for continued study.

This paper attempts to uncover the relationship between violent crime and economic mobility in the United States using cross-sectional time series data and multiple regression analysis.

This dissertation contains five chapters. An introduction and a summary of the research are given in Chapter 1. The other four chapters present theoretical results on the moment structure of GARCH processes. Some chapters also contain empirical examples in order to illustrate applications of the theory. The focus, however, is mainly on statistical theory. Chapter 2 considers the moments of a family of first-order GARCH processes. First, a general condition of the existence of any integer moment of the absolute values of the observations is given. Second, a general expression of this moments as a function of lower-order moments is derived. Third, the kurtosis and the autocorrelation function of the squared and absolute-valued observations are derived. The results apply to a host of different GARCH parameterizations. Finally, the existence, or the lack of it, of the theoretical counterpart to the so-called Taylor effect for some members of this GARCH family is discussed. The asymmetric power ARCH model is a recent addition to time series models that may be used for predicting volatility. Its performance is compared with that of standard models of conditional heteroskedasticity such as GARCH. This has previously been done empirically. In Chapter 3 the same issue is studied theoretically using unconditional fractional moments for the A-PARCH model that are derived for the purpose. The role of the heteroskedasticity parameter of the A-PARCH process is highlighted and compared with corresponding empirical results involving autocorrelation functions of power-transformed absolute-valued return series.In Chapter 4, a necessary and sufficient condition for the existence of the unconditional fourth moment of the GARCH(p,q) process is given as well as an expression for the moment itself. Furthermore, the autocorrelation function of the centred and squared observations of this process is derived. The statistical theory is further illustrated by a few special cases such as the GARCH(2,2) process and the ARCH(q) process.Nonnegativity constraints on the parameters of the GARCH(p,q) model may be relaxed without giving up the requirement of the conditional variance remaining nonnegative with probability one. Chapter 5 looks into the consequences of adopting these less severe constraints in the GARCH(2,2) case and its two second-order special cases, GARCH(2,1) and GARCH(1,2). This is done by comparing the autocorrelation function of squared observations under these two sets of constraints. The less severe constraints allow more flexibility in the shape of the autocorrelation function than the constraints restricting the parameters to be nonnegative. The theory is illustrated by an empirical example. / Revised versions of chapters 2-5 have been published as:He, C. and T. Teräsvirta, "Properties of moments of a amily of GARCH processes" in Journal of Econometrics, Vol. 92, No. 1, 1999, pp173-192.He, C. and T. Teräsvirta, "Statistical Properties of the Asymmetric Power ARCH Process" in R.F. Engle and H. White (eds) Cointegration, causality, and forecasting. Festschrift in honour of Clive W.J. Granger, chapter 19, pp 462-474, Oxford University Press, 1999.He, C. and T. Teräsvirta, "Fourth moment structure of the GARCH(p,q) process" in Econometric Theory, Vol. 15, 1999, pp 824-846.He, C. and T. Teräsvirta, "Properties of the autocorrelation function of squared observations for second order GARCH processes under two sets of parameter constraints" in Journal of Time Series Analysis, Vol. 20, No. 1, January 1999, pp 23-30.

This paper analyzes the effects of the Fukushima Daiichi nuclear disaster on Tokyo Electric Power Company’s (TEPCO) electricity load using alternative event study methodology. The data set includes TEPCO’s published hourly loads from January 1, 2008 to December 31, 2011. Four time series regressions are used to analyze the disaster’s effect on TEPCO’s load curve at an hourly and aggregate level. By examining the hourly impacts of the disaster, this paper provides commentary on the effects of the disaster on the daily load curve, finding transition periods to be the time of day that is most targeted for decreases in electricity consumption. The models control for temperature, population, time of day, week, month, and year, holidays, and trends. The results indicate a significant, negative relationship between the disaster and TEPCO’s electricity load. In addition to examining the effects of the disaster on the daily load curve, four event windows are analyzed, ranging from a week after the March 11, 2011 disaster to the end of the data set (December 31, 2011). These event windows are used to capture the short, medium, and long-term effects of the Fukushima Daiichi disaster on electricity load. These event window results combined with an analysis of the annual and disaster trend variables allow for commentary on the timeline for which TEPCO’s loads will reach pre-disaster levels. Additionally, the results provide insight into both the economic and political implications of the Fukushima Daiichi disaster both in Japan and worldwide.