M.Com. (Economics) / In this study the use of distributed lags in the estimation of econometric equations is discussed with special reference to Shirley Almon's model of polinomically distributed lags. In chapter 2 of this study possible reasons for the existence of distributed lags as well as a number of distributed lag models are discussed. In chapter 3 the estimation of Almon lag models with and without the existence of end restrictions is discussed with special mention of the practical problems associated with such estimations. In chapter 4 the estimation of multi-variable Almon lags and the benefit of computer programs in the estimation thereof are discussed. In chapter 5 a procedure is given for the estimation of Almon lag models with examples of the estimation of two fuctions: Investment: Private: Non-Agriculture (IPNL) and Exports Excluding Gold (XSG).

This paper explores execution rates among states where the death penalty is legal. Following the Supreme Court ruling in 1972 (Furman v. Georgia) which categorized the death penalty as cruel and unusual punishment due to arbitrary sentencing, this paper looks at whether or not executions are arbitrarily conducted by states. By taking into account race of the defendant, race of the victim, heinousness of crime, quality of defense, and public support for the death penalty, this paper seeks answers to the varying rates of executions across the United States. It was however, unable to find causal reasons for differences in execution rates. It did find though, that in some states, a black defendant had a higher likelihood of being executed during 2008-2012. This finding is important because it shows that race matters in some states if you are a defendant on a capital case.

This thesis explores effects of uncertainty on firm investment that are described in estimates of firm level investment specifications which include proxies for uncertainty over expected future firm profitability. A panel data set of UK firms covering the period 1987-2000 is used to estimate firm level investment specifications. Within year volatility in stock returns - a common proxy for firm specific uncertainty in previous literature - is compared with covariance measures between stock returns and market returns representing un-diversifiable risk from the CAPM; and with alternative uncertainty proxies based on volatility in I/B/E/S securities analysts' forecasts of earnings per share. Within estimates of firm level investment specifications, the thesis investigates the sensitivity of coefficients on uncertainty terms to the choice of underlying investment specification: error correction model between the natural logarithms of capital and sales; or the Hayashi (1982) Q model of investment. Coefficients on stock return volatility measures of uncertainty terms are found to vary significantly between estimates of error correction and average q specifications. Differences between coefficients estimated on uncertainty terms across estimates of these two investment specifications are supported with simulated data. Uncertainty measures based on volatility in I/B/E/S securities analysts' forecasts of earnings per share are found to be much more informative of investment behaviour than within year stock return volatility in estimates of both error correction and average q specifications. Coefficients on I/B/E/S uncertainty proxies imply more consistent investment-uncertainty relationships across estimates of error correction and average q specifications for the UK panel data set.

Understanding of climate change and policy responses thereto rely on accurate measurements as well as models of both socio-economic and physical processes. However, data to assess impacts and establish historical climate records are non-stationary: distributions shift over time due to shocks, measurement changes, and stochastic trends - all of which invalidate standard statistical inference. This thesis establishes econometric methods to model non-stationary climate data consistent with known physical laws, enabling joint estimation and testing, develops techniques for the automatic detection of structural breaks, and evaluates socio-economic scenarios used in long-run climate projections. Econometric cointegration analysis can be used to overcome inferential difficulties stemming from stochastic trends in time series, however, cointegration has been criticised in climate research for lacking a physical justification for its use. I show that physical two-component energy balance models of global mean climate can be mapped to a cointegrated system, making them directly testable, and thereby provide a physical justification for econometric methods in climate research. Automatic model selection with more variables than observations is introduced in modelling concentrations of atmospheric CO₂, while controlling for outliers and breaks at any point in the sample using impulse indicator saturation. Without imposing the inclusion of variables a-priori, model selection results find that vegetation, temperature and other natural factors alone cannot explain the trend or the variation in CO₂ growth. Industrial production components, driven by business cycles and economic shocks, are highly significant contributors. Generalizing the principle of indicator saturation, I present a methodology to detect structural breaks at any point in a time series using designed functions. Selecting over these break functions at every point in time using a general-to-specific algorithm, yields unbiased estimates of the break date and magnitude. Analytical derivations for the split-sample approach are provided under the null of no breaks and the alternative of one or more breaks. The methodology is demonstrated by detecting volcanic eruptions in a time series of Northern Hemisphere mean temperature derived from a coupled climate simulation spanning close to 1200 years. All climate models require socio-economic projections to make statements about future climate change. The large span of projected temperature changes then originates predominantly from the wide range of scenarios, rather than uncertainty in climate models themselves. For the first time, observations over two decades are available against which the first sets of socio-economic scenarios used in the Intergovernmental Panel on Climate Change reports can be assessed. The results show that the growth rate in fossil fuel CO₂ emission intensity (fossil fuel CO2 emissions per GDP) over the 2000s exceeds all main scenario values, with the discrepancy being driven by underprediction of high growth rates in Asia. This underestimation of emission intensity raises concerns about achieving a world of economic prosperity in an environmentally sustainable fashion.

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Relationships between insurance variables representing the cash flows of the Czech insurance market can be effectively modeled using a dynamic system of linear simultaneous equations. The source of the underlying data to build such a model can be publicly available annual reports of the Czech Insurance Association. The resulting model can find its use mainly to predict the future development of financial flows based on historical observations and analysis of possible scenarios. It is this analysis of potential projections and their consequences which provides insight into how e.g. a future decrease of new insurance policies would affect the expected amount of claims costs and the volume of written premiums.

Doctor of Philosophy / Department of Economics / Steven P. Cassou / This dissertation consists of three essays relating to asymmetric preferences in optimal monetary policy models. Optimal monetary policy models are theoretical optimal control problems that seek to identify how the monetary authority makes decisions and ultimately formulate decision rules for monetary policy actions. These models are important to policy makers because they help to define expectations of policy responses by the central bank. By identifying how researchers perceive the central bank’s actions over time, the monetary authority can identify how to manage those expectations better and formulate effective policy measures. In chapter 1, using a model of an optimizing monetary authority which has preferences that weigh inflation and unemployment, Ruge-Murcia (2003a; 2004) finds empirical evidence that the monetary authority has asymmetric preferences for unemployment. We extend this model to weigh inflation and output and show that the empirical evidence using these series also supports an asymmetric preference hypothesis, only in our case, preferences are asymmetricforoutput. Wealsofindevidencethatthemonetaryauthoritytargetspotential output rather than some higher output level as would be the case in an extended Barro and Gordon (1983) model. Chapter 2 extends the asymmetric monetary policy problem of Surico (2007) by relaxing the assumption that inflation and interest rate targets are constant using a time varying parameter approach. By estimating a system of equations using iterative maximum likeli- hood, all of the monetary planner’s structural parameters are identified. Evidence indicates that the inflation and interest rate targets are not constant over time for all models esti- mated. Results also indicate that the Federal Reserve does exhibit asymmetric preferences toward inflationary and output gap movements for the full data sample. The results are robust when accounting for changing monetary policy targeting behavior in an extended model. The asymmetry for both inflation and output gaps disappears over the post-Volcker subsample, as in Surico (2007). In chapter 3, Walsh (2003b)’s speed limit objective function is generalized to allow for asymmetry of policy response. A structural model is estimated using unobserved compo- nents to account for core inflation and measure the output gap as in Harvey, Trimbur and Van Dijk (2007) and Harvey (2011). Full sample estimates provide evidence for asymmetry in changes in inflation over time, but reject asymmetry for the traditional speed limit for the output gap. Post-Volcker subsample estimates see asymmetry disappear as in a more traditional asymmetric preferences model like Surico (2007).

M.A. / The purpose of this study is to document and build an econometric model of the demand in the South African Clothing industry. It is important to study the clothing industry because it is labour intensive and thus its growth and development could contribute positively toward eradicating the unemployment problem in South Africa. With globalization of world economies and South Africa being a signatory to the GATT/WTO, the implications for this industry are manifold. The opening chapter lists the problem statement, identifies the method of research utilised and the relevance of the study. Chapter two looks at demand theory, particularly with regard to the quantitative techniques involved in its estimation. It focusses on regression theory and the evaluation of results generated. The third chapter gives a background to the South African clothing industry, and touches on amongst others aspects of current importance such as trade reform, international best practice and the key issues the industry has to deal with. Chapter four looks at the econometrics aspects of the study. A near perfect forecast was obtained, which attests to the stability and superiority of the model which is presented. The main findings of this study are that it is supply considerations such as the wage bill, costs of inputs (eg textile materials) etc which play an important part in the survival and prosperity of the industry. It is also reveals the fact that low productivity levels could be easily and quickly rectified through the introduction of new organizational practices and human resource development, development of quick response relationships and training to support new organizational practices. The study further and finally asserts that, while trade reform could necessitate painful adjustments the industry could actually come out a stronger world player

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

M.Com. (Econometrics) / The main purpose of this study is the combining of forecasts with special reference to major macroeconomic series of South Africa. The study is based on econometric principles and makes use of three macro-economic variables, forecasted with four forecasting techniques. The macroeconomic variables which have been selected are the consumer price index, consumer expenditure on durable and semi-durable products and real M3 money supply. Forecasts of these variables have been generated by applying the Box-Jenkins ARIMA technique, Holt's two parameter exponential smoothing, the regression approach and mUltiplicative decomposition. Subsequently, the results of each individual forecast are combined in order to determine if forecasting errors can be minimized. Traditionally, forecasting involves the identification and application of the best forecasting model. However, in the search for this unique model, it often happens that some important independent information contained in one of the other models, is discarded. To prevent this from happening, researchers have investigated the idea of combining forecasts. A number of researchers used the results from different techniques as inputs into the combination of forecasts. In spite of the differences in their conclusions, three basic principles have been identified in the combination of forecasts, namely: i The considered forecasts should represent the widest range of forecasting techniques possible. Inferior forecasts should be identified. Predictable errors should be modelled and incorporated into a new forecast series. Finally, a method of combining the selected forecasts needs to be chosen. The best way of selecting a m ethod is probably by experimenting to find the best fit over the historical data. Having generated individual forecasts, these are combined by considering the specifications of the three combination methods. The first combination method is the combination of forecasts via weighted averages. The use of weighted averages to combine forecasts allows consideration of the relative accuracy of the individual methods and of the covariances of forecast errors among the methods. Secondly, the combination of exponential smoothing and Box-Jenkins is considered. Past errors of each of the original forecasts are used to determine the weights to attach to the two original forecasts in forming the combined forecasts. Finally, the regression approach is used to combine individual forecasts. Granger en Ramanathan (1984) have shown that weights can be obtained by regressing actual values of the variables of interest on the individual forecasts, without including a constant and with the restriction that weights add up to one. The performance of combination relative to the individual forecasts have been tested, given that the efficiency criterion is the minimization of the mean square errors. The results of both the individual and the combined forecasting methods are acceptable. Although some of the methods prove to be more accurate than others, the conclusion can be made that reliable forecasts are generated by individual and combined forecasting methods. It is up to the researcher to decide whether he wants to use an individual or combined method since the difference, if any, in the root mean square percentage errors (RMSPE) are insignificantly small.