Thesis (Ph. D.)--University of Wisconsin--Madison, 1974. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains v, 95 p. : ill. (some col.). Includes abstract. Includes bibliographical references.

This thesis analyzes estimation methods and testing procedures for handling large data series. The first chapter introduces the use of the adaptive elastic net, and the penalized regression methods nested within it, for estimating sparse vector autoregressions. That chapter shows that under suitable conditions on the data generating process this estimation method satisfies an oracle property. Furthermore, it is shown that the bootstrap can be used to accurately conduct inference on the estimated parameters. These properties are used to show that structural VAR analysis can also be validly conducted, allowing for accurate measures of policy response. The strength of these estimation methods is demonstrated in a numerical study and on U.S. macroeconomic data. The second chapter continues in a similar vein, using the elastic net to estimate sparse vector autoregressions of realized variances to construct volatility forecasts. It is shown that the use of volatility spillovers estimated by the elastic net delivers substantial improvements in forecast ability, and can be used to indicate systemic risk among a group of assets. The model is estimated on realized variances of equities of U.S. financial institutions, where it is shown that the estimated parameters translate into two novel indicators of systemic risk. The third chapter discusses the use of the bootstrap as an alternative to asymptotic Wald-type tests. It is shown that the bootstrap is particularly useful in situations with many restrictions, such as tests of equal conditional predictive ability that make use of many orthogonal variables, or `test functions'. The testing procedure is analyzed in a Monte Carlo study and is used to test the relevance of real variables in forecasting U.S. inflation.

The basic objective of this thesis is to identify some of the basic trends tending to shape the Canadian economy. The procedure followed was to examine economic theory and previous forecasting studies to determine methodological principles and apply these principles to estimate the possible future course of the Canadian economy between 1965 and 1980. No comprehensive economic theory appears to be presently developed to explain and therefore to form a complete basis for predicting the economic growth of a nation. In an effort to make economic theory manageable, many variables affecting economic growth and development such as those of sociology tend to be ignored in quantitative terms. Together with these unquantifiable variables, it is not known how many non-economic factors affect economic growth. It would seem to be these many unknown factors that tend to cause errors in the results of long range economic forecasts. Economic growth, defined as the expansion of a nation's capacity to produce, in an already advanced industrial economy, is heavily dependent on the quantity and quality of the nation's labour force, natural resources, real capital, and the technological level in the society. These basic determinants are tempered by the sociological, institutional, and consumption trends or factors within the economy. Although many articles have been written on various aspects of economic growth, the present state of knowledge does not appear to be appreciably past the theorizing stage. As no complete theory of economic growth and development appears to exist, the long range economic forecaster may gain some insights from economic theory but depend very much on his own resources to make various forecasts. The most common method to determine output appears to necessitate a population forecast from which a labour force estimate is made and then with assumptions regarding per-man productivity, an estimate for total output can be made. Sophisticated population and labour force forecasts tend to divide the population into age and sex specific cohorts and then analyze the trends within each of these cohorts. The methodology used in this thesis was based on broad estimates for various trends per thousand population. Due mainly to an expected high birth rate in Canada, the population is anticipated to increase at about 3.8 percent per year to about 25,800,000 by 1980. Of this figure, about 10,000,000 are expected to make up the labour force. The two significant trends expected in the labour force are a large influx of young people and a greater participation of women in the labour force. In this thesis, the total output was separated into agriculture, government and public administration, and commercial non-agricultural sectors. This enabled the analysis of the trends in the work force, productivity, and output in each sector to be examined. The significant trends in output expected are an increase in per-man productivity, but a declining labour force in agriculture, a rather constant productivity per man, but an increase in the total labour force in the government and public administration sector, and an increase in both the labour force and productivity per-man in the commercial non-agricultural sector. The real increase in output of the combined sectors is estimated to approximate 4.6 percent per year between 1965 and 1980 for the Canadian economy. With the total output estimated, an estimate was made as to the division of the output between capital accumulation, government expenditures, consumer expenditures, imports and exports. It was found that the division of the output between these broad sectors tended to be rather stable in relation to the gross national product. Because of this stability, future estimates for the broad categorical spending were based mainly on simple trend projections. From the historical spending patterns, it would appear difficult to justify any drastic changes in the basic spending patterns. / Business, Sauder School of / Graduate

This paper develops a series of Early Warning System models for debt crises. This paper uses a Debt Pressure index to define crisis periods and then demonstrates how one can go about trying to forecast these periods using Logit and Markov-switching Models. An alternative approach, whereby ordinary least squares (OLS) is used to create Early Warning System models, is introduced. A graphical analysis is also conducted. Three useful Early Warning System models emerge from this study.

This paper investigates the Baltic Dry Index; an often misunderstood index, which tracks the cost of shipping dry bulk cargo globally. The research is based on the hypothesis that movements in the Baltic Dry Index price are driven largely by changes in the underlying demand for goods which are consumed globally. Accordingly, this paper aims to investigate whether changes in the Baltic Dry Index price may be used to predict future economic movements in a South African context. In this regard, the paper first conducts a thorough synthesis of the available literature, in order to formulate the conclusion that the Baltic Dry Index price is driven by a multitude of variables, including the global demand for goods, the global supply of ships, the laycan period, bunker prices, global piracy, global winter severity, as well as the inclusion of a cyclical component. The global demand for goods is concluded to be chief among these. Based on these findings, the paper then conducts empirical testing on the usefulness of the BDI in a South African context, and concludes that the Baltic Dry Index is useful when used as a leading economic indicator in South African, especially when used in order to predict long-term economic movements, across a period of 3 – 4.5 years. Finally, strong evidence is found to support the existence of a relationship between the BDI and the Johannesburg Stock Exchange Mining Index, although further investigation is required in order to form a definitive conclusion in this regard.

In the first chapter of this dissertation, I explore how forecaster attention, or the degree to which new information is incorporated into forecasts, is reflected at the lower-dimensional factor representation of multivariate forecast data. When information is costly to acquire, forecasters may pay more attention to some sources of information and ignore others. How much attention they pay will determine the strength of the forecast correlation (factor) structure. Using a factor model representation, I show that a forecast made by a rationally inattentive agent will include an extra shrinkage and thresholding "attention matrix" relative to a full information benchmark, and propose an econometric procedure to estimate it. Differences in the degree of forecaster attentiveness can explain observed differences in empirical shrinkage in professional macroeconomic forecasts relative to a consensus benchmark. Forecasters share the same reduced-form model, but differ in their measured attention. Better-performing forecasters have higher measured attention (lower shrinkage) than their poorly-performing peers. Measured forecaster attention to multiple dimensions of the information space can largely be captured by a single scalar cost parameter. I propose a new class of information cost functions for the classic multivariate linear-quadratic Gaussian tracking problem called separable spectral cost functions. The proposed measure of attention and mapping from theoretical model of attention allocation to factor structure in the first chapter is valid for this set of cost functions. These functions are defined over the eigenvalues of prior and posterior variance matrices. Separable spectral cost functions both nest known cost functions and are consistent with the definition of Uniformly Posterior Separable cost functions, which have desirable theoretical properties. The third chapter is coauthored work with Professor Serena Ng. We estimate higher frequency values of monthly macroeconomic data using different factor based imputation methods. Monthly and weekly economic indicators are often taken to be the largest common factor estimated from high and low frequency data, either separately or jointly. To incorporate mixed frequency information without directly modeling them, we target a low frequency diffusion index that is already available, and treat high frequency values as missing. We impute these values using multiple factors estimated from the high frequency data. In the empirical examples considered, static matrix completion that does not account for serial correlation in the idiosyncratic errors yields imprecise estimates of the missing values irrespective of how the factors are estimated. Single equation and systems-based dynamic procedures that account for serial correlation yield imputed values that are closer to the observed low frequency ones. This is the case in the counterfactual exercise that imputes the monthly values of consumer sentiment series before 1978 when the data was released only on a quarterly basis. This is also the case for a weekly version of the CFNAI index of economic activity that is imputed using seasonally unadjusted data. The imputed series reveals episodes of increased variability of weekly economic information that are masked by the monthly data, notably around the 2014-15 collapse in oil prices.

M.Com. (Economics) / Please refer to full text to view abstract

A dissertation submitted to the Wits Business School, Faculty of Commerce, Law and Management, in partial fulfillment of the requirements of the candidacy of the Masters of Management in Finance and Investments University of Witwatersrand April 2016 / The aim of this paper is to investigate financial contagion in African financial markets from the global financial crisis. Interest in this subject has grown exponentially in the recent past in light of expanding globalization. The empirical analysis is based on daily stock price indices of a sample of African countries in order to compute the stock returns and find the impact of correlations between them and the US market. The empirical evidence is based on correlation tests by Forbes& Rigobon (2002). The analysis suggests that the larger markets by market capitalization and number of traded stocks exhibit co-movement, whereas the smaller markets experience financial contagion. The results have implications for financial investment process and risk management in terms of globalization and the unfolding of financial liberalization in Africa. / GR2018

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