Teses / dissertações: "Economic forecasting"

1

Odendahl, Florens. "Essays in economic forecasting". Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/664016.

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This thesis consists of three chapters on forecasting techniques in economics. In chapter 1, I use copulas to estimate multivariate density forecasts based on univariate densities from survey data. Survey-based predictions are often competitive to time series models in their forecasting performance but have a univariate focus and my estimation strategy exploits the information in the surveys’ marginal densities. I subsequently demonstrate the importance of the multivariate aspect for forecasters. In chapter 2, we propose novel tests for forecast rationality, which are robust under the presence of Markov switching. Existing tests focus on constant out-of-sample performances or use non-parametric techniques; consequently, they may lack power against the alternative of discrete switches. Investigating the Blue Chip Fi-nancial Forecasts, we find evidence against forecast unbiasedness during periods of monetary easing. Chapter 3 provides an empirical investigation of the real-time forecasting performance of quantile regressions for predicting diferent vintages of real US GDP growth. My results indicate that quantile regressions are competitive to current benchmark models and that the insample estimation strategy matters for the performance concerning difrent data vintages.
Esta tesis consta de tres capítulos sobre métodos predictivos en economía. El primer capítulo propone el uso de cópulas para la elaboración de previsiones de distribuciones multivariantes utilizando datos de encuestas sobre distribuciones univariantes. Las previsiones basadas en sondeos son, a menudo, equiparables a las obtenidas por modelos de series temporales, pero sólo hay datos disponibles para distribuciones univariantes. La estrategia de estimación propuesta utiliza la información de las distribuciones univariantes de los sondeos. Posteriormente queda demostrada la importancia de la perspectiva multivariante en la elaboración de previsiones. El segundo capítulo propone nuevos tests para evaluar la racionalidad de las previsiones, los cuales, resultan sólidos bajo la presencia de Markov switching. En comparación, los tests existentes se centran en probar la prueba entera o usan técnicas no-paramétricas y tienen menos poder contra la alternativa de cambios discretos. Mediante la investigación empírica de la racionalidad del las previsiones del Blue Chip Financial Forecasts, se encuentra evidencia a favor de la hipótesis de un sesgo con Markov switching durante los periodos de relajación monetaria. El tercer capítulo es una investigación empírica de la eficacia del modelo de regresión de cuantiles para prever en tiempo real el crecimiento del PIB estadounidense. Los resultados obtenidos indican que dicho modelo es comparable a los modelos de referencia actuales y que la estrategia de estimación aplicada con diferentes muestras de datos influye los resultados.

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Souza, André B. M. "Essays in economic forecasting". Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/672997.

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This dissertation consists of two independent chapters on economic and financial forecasting. The first chapter introduces a nonlinear forecasting framework that combines forecasts of the sign and absolute value of a time series into conditional mean forecasts. In contrast to linear models, the proposed framework allows different predictors to separately impact the sign and absolute value of the target series. An empirical application using the FRED-MD dataset shows that forecasts from the proposed model substantially outperform linear forecasts for series that exhibit persistent volatility dynamics, such as output and interest rates. The second chapter, coauthored with Christian Brownlees, provides an extensive comparison of methods to forecast downside risks to GDP growth for a panel of 24 OECD economies. We consider forecasts constructed from standard quantile regressions as well as from conditional volatility models. Our evidence suggests that standard volatility models such as the GARCH(1,1) are at least as accurate as quantile regressions.
Aquesta dissertació consta de dos capítols independents sobre previsió econòmica i financera. El primer capítol introdueix un modelo de predicció no lineal que combina les previsions del signe i del valor absolut d’una sèrie temporal en previsions mitjanes condicionals. A diferència dels models lineals, el modelo proposat permet que diferents variables afectin per separat el signe i el valor absolut de la sèrie d’interés. Una aplicació empírica que utilitza el conjunt de dades FRED-MD mostra que les previsions basades en el modelo proposat superen substancialment les previsions lineals per a sèries que presenten dinàmiques de volatilitat persistents, com la producció industrial i els tipus d’interès. El segon capítol, coautorado con Christian Brownlees, proporciona una àmplia comparació de mètodes per predir els riscos negatius per al creixement del PIB per a un grup de 24 economies de l’OCDE. Considerem les previsions construïdes a partir de regressions quàntils estàndard, així com a partir de models de volatilitat condicional. La nostra evidència suggereix que els models de volatilitat, com el GARCH (1,1), són almenys tan precisos com les regressions quantils.

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Acar, Emmanuel. "Economic evaluation of financial forecasting". Thesis, City University London, 1993. http://openaccess.city.ac.uk/8256/.

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This thesis examines the economic evaluation of forecasting strategies based on past prices, bringing together academics and practitioners techniques Forecasting methods based on past prices are convex and path-dependent dynamic strategies Therefore, they must be able to profitably exploit positive serial dependences in financial prices The most important measure of financial forecasting ability is the rate of return achieved by the predictor The expected return of forecasting strategies is first investigated by applying stochastic modelling Then, the presence of serial dependences in financial prices is tested by comparing expected and observed rates of returns of forecasting strategies According to the academic literature, the expected return of investment strategies is best established by applying stochastic modelling That is done analytically for linear forecasters, assuming that the underlying process of asset returns is not only a random walk with drift but any Gaussian processes The rate of return from financial strategies is zero under the assumption of a random walk without drift, and non-zero in all the other cases Then, it is shown that many forecasting techniques used by market participants are in fact linear forecasters and consequently fall in the scope of this study. Minimising the mean squared error is a sufficient but not necessary condition to maximise returns Under the random walk without dnft assumption, error measures and profits arenegatively correlated but very few in absolute value Only the directional accuracy exhibits high degree of linear association with profits When the true Gaussian process is not known, there are cases for which a decrease in mean squared error does not imply an increase in returns Therefore the mean squared error criterion is of poor use to maximise returns when the true model is not known The directional accuracy is of no further help Market timing ability tests based on the percentage of correct forecasts have very low power in presence of low positive autocorrelations. It is why a test of the random walk hypothesis based on the joint profitability of trading rules is investigated It happens to be powerful against a broad range of linear alternatives Its ruee feature is to exhibit a power almost equal to the best of its components unknown when the true model is unknown It constitutes as well a tool to separate mean from variance non-hnear models Simple tests of adequacy of Gaussian processes are subsequently proposed from the joint profitability of trading rules Applying previous tests, the random walk hypothesis is rejected for daily exchange rates against Dollar, over the period 1982-1992 The hypothesis of normal underlying returns is very weak compared to the independence assumption Among a few Gaussian processes, the price-trend model along with some technical models appear to be the best alternatives to explain observed trading rule returns Statistical forecasters based either on ARMA(1,1) or fractional Gaussian processes do not outperform simple technical rules Taking Into account transaction costs reduce profits to zero for individual but not for institutional Investors who might have to act on strategies that assume the foreign exchange markets exhibit positive dependencies, if not inefficiencies.

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Bezsmertna, Julia. "Modern methods of economic forecasting". Thesis, Київський національний університет технологій та дизайну, 2019. https://er.knutd.edu.ua/handle/123456789/14350.

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Sippl-Swezey, Nicolas. "Heterogeneous gain forecasting using historic asset information". Oberlin College Honors Theses / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1354304083.

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Marsilli, Clément. "Mixed-Frequency Modeling and Economic Forecasting". Thesis, Besançon, 2014. http://www.theses.fr/2014BESA2023/document.

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La prévision macroéconomique à court terme est un exercice aussi complexe qu’essentiel pour la définition de la politique économique et monétaire. Les crises financières récentes ainsi que les récessions qu’ont endurées et qu’endurent aujourd’hui encore, en ce début d’année 2014, nombre de pays parmi les plus riches, témoignent de la difficulté d’anticiper les fluctuations économiques, même à des horizons proches. Les recherches effectuées dans le cadre de la thèse de doctorat qui est présentée dans ce manuscrit se sont attachées à étudier, analyser et développer des modélisations pour la prévision de croissance économique. L’ensemble d’informations à partir duquel construire une méthodologie prédictive est vaste mais également hétérogène. Celle-ci doit en effet concilier le mélange des fréquences d’échantillonnage des données et la parcimonie nécessaire à son estimation. Nous évoquons à cet effet dans un premier chapitre les éléments économétriques fondamentaux de la modélisation multi-fréquentielle. Le deuxième chapitre illustre l’apport prédictif macroéconomique que constitue l’utilisation de la volatilité des variables financières en période de retournement conjoncturel. Le troisième chapitre s’étend ensuite sur l’inférence bayésienne et nous présentons par ce biais un travail empirique issu de l’adjonction d’une volatilité stochastique à notre modèle. Enfin, le quatrième chapitre propose une étude des techniques de sélection de variables à fréquence multiple dans l’optique d’améliorer la capacité prédictive de nos modélisations. Diverses méthodologies sont à cet égard développées, leurs aptitudes empiriques sont comparées, et certains faits stylisés sont esquissés
Economic downturn and recession that many countries experienced in the wake of the global financial crisis demonstrate how important but difficult it is to forecast macroeconomic fluctuations, especially within a short time horizon. The doctoral dissertation studies, analyses and develops models for economic growth forecasting. The set of information coming from economic activity is vast and disparate. In fact, time series coming from real and financial economy do not have the same characteristics, both in terms of sampling frequency and predictive power. Therefore short-term forecasting models should both allow the use of mixed-frequency data and parsimony. The first chapter is dedicated to time series econometrics within a mixed-frequency framework. The second chapter contains two empirical works that sheds light on macro-financial linkages by assessing the leading role of the daily financial volatility in macroeconomic prediction during the Great Recession. The third chapter extends mixed-frequency model into a Bayesian framework and presents an empirical study using a stochastic volatility augmented mixed data sampling model. The fourth chapter focuses on variable selection techniques in mixed-frequency models for short-term forecasting. We address the selection issue by developing mixed-frequency-based dimension reduction techniques in a cross-validation procedure that allows automatic in-sample selection based on recent forecasting performances. Our model succeeds in constructing an objective variable selection with broad applicability

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Franklin, Jesse C. "Forecasting the Inland Empire's Economic Recovery". Scholarship @ Claremont, 2010. http://scholarship.claremont.edu/cmc_theses/42.

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The Inland Empire -Riverside and San Bernardino Counties - was one of the hardest hit areas in all of the United States during the Great Recession. Home prices have declined over 50%, significantly more than the 25% decline in the surrounding Los Angeles County, and housing starts have declined to over 90% from 2005. The Inland Empire has one of the highest unemployment rates in the US at 14.8%. This paper attempts to forecast the recovery for the Inland Empire. Employing univariate forecasts along with VAR(12) forecasts, focusing on housing starts and unemployment rates as the underlying variables, we find that there is little hope for a recovery over the next 3 years. The model predicts unemployment to either rise even more or, at best, remain stagnant. Housing starts are predicted to remain constant over the next three years.

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Thomas, M. C. "Techno-economic forecasting for packaging materials". Thesis, Swansea University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639223.

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Forecasting packaging material demand is crucial to effective future planning by capital intensive material manufacturers. Yet several considerations introduce great uncertainty over the future packaging mix. Foremost is a highly heterogeneous and dynamic end-use marketplace subject to multitudinous technological, economic, consumer and legislative change-forces. These act at all levels upon a diverse and complex supply chain that suffers data paucity and, hence, opacity of cause and effect. A wide range of future-oriented decision technologies was examined to meet these challenges. None promise competitive advantage over commissioned forecasts of aggregate demand. At the sector level, the petfood market is relatively homogeneous and simple, but nonetheless significant. Neural network analysis of its causal relationships led to rich results and a simple, workable causal-forecasting model. Data paucity inspired three key development paths. First, a weakness in current implementations of genetic algorithm model input selection was exposed - result variability due to training data set division. Novel software invoked genetic algorithm input selection over exhaustive permutations of training cases to generate a result distribution, thereby partially automating model specification for wider application. Second, the neural networks were implemented in a scenario-planning spreadsheet, to isolate the more certain and less certain factors in scenario forecasting. Third, several unprecedented factors change past relationships and can undermine even the most accurately specific model. Accordingly, a Delphi survey was conducted to develop scenarios of the potential impact of remote retailing upon packaging demand. Consequently, although the five-year outlook for tinplate petfood packaging is open to interpretation, the most likely scenario is stable demand. Petfood and human food cans exhibit clear strengths in the remote-retailing scenario, but high uncertainty is envisaged for the remaining packaging applications. Such unprecedented forces should be continually monitored, and marketing activities should emphasise the strengths of tinplate in the scenarios thereby envisaged.

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Hackworth, J. F. "Forecasting the ownership growth of consumer durables". Thesis, Cranfield University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371830.

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Betz, Gregor Tetens Holm. "Prediction or prophecy? the boundaries of economic foreknowledge and their socio-political consequences /". Wiesbaden : Deutscher Universitäts-Verlag, 2006. http://site.ebrary.com/id/10231757.

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Bryhn, Andreas. "The Forecasting Power of Economic Growth Models". Thesis, Uppsala University, Department of Economics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8053.

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High forecasting power is essential for understanding scientific relationships. In economics, forecasting power may be decisive for the success or failure of a particular policy. The forecasting power of economic growth models is investigated in this study. Regressions from one dataset including the gross domestic product (GDP), GDP growth, trade openness, the quality of public institutions and secondary education generate insufficient forecasting power with respect to growth. Furthermore, the International Monetary Fund's one-year growth forecasts are compared to outcome. Forecasts for 1999-2006 were found to be significantly different from outcome during 7 years out of 8. The forecast error slightly exceeded 1 percentage unit, which is similar to results from earlier studies on forecast error and equal to the forecast/hindcast error from a simple multivariate model constructed from historical growth data. Possible reasons behind poor forecast quality are discussed, including the tradition to build models using assumptions from irrefutable theoretical constructs.

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Chevillon, Guillaume. "Multi-step estimation for forecasting economic processes". Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.416533.

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Toner, Patrick Thomas. "Load forecasting for economic power system operation". Thesis, Queen's University Belfast, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317533.

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Shih, Shou Hsing. "Forecasting models for economic and environmental applications". [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002425.

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Skinner, David. "Forecasting models of activity in industrial and commercial building". Thesis, University of Salford, 1999. http://usir.salford.ac.uk/26916/.

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Despite its importance in national income, the level of activity in the construction sector has received little attention in the economics literature. The lack of studies attempting to forecast construction activity is surprising given that its volatility is often regarded as destabilising to the economy. Here, we model an important and growing component of construction, namely private industrial and commercial building. Construction activity is typically measured by output. To the extent that new construction output represents capital formation, output can be modelled as an investment problem. The theoretical investment literature is disparate and confusing but here, the leading models are presented in a unified framework in which the similarities and differences between them can be easily identified. We then go on to estimate a number of the models empirically. Some are econometric models consistent with traditional theories of investment. Others are based on vector autoregression (VAR) analysis which provides a largely statistical representation of a set of variables with minimum use of a priori restrictions but in which long-run relationships are preserved. The data required for model estimation is considerable and complicated by the effects of investment incentives embodied in the tax system. The forecasting performance of all the models is evaluated against forecasts generated by a benchmark model suggested by the data rather than by economic theory. In terms of forecasting performance, some of the investment models considered here are shown to be superior to the benchmark model.

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Giesecke, James Andrew David. "FEDERAL-F : a multi-regional multi-sectoral dynamic model of the Australian economy /". Title page, appendix, contents and abstract only, 2000. http://web4.library.adelaide.edu.au/theses/09PH/09phg4554.pdf.

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Valente, Giorgio. "Essays in financial forecasting". Thesis, University of Warwick, 2003. http://wrap.warwick.ac.uk/4055/.

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Forecasting is central to economic and financial decision-making. Government institutions and agents in the private sector often base their decisions on forecasts of financial and economic variables. Forecasting has therefore been a primary concern for practitioners and financial econometricians alike, and the relevant literature has witnessed a renaissance in recent years. This thesis contributes to this literature by investigating three topical issues related to financial and economic forecasting. The first chapter finds its rationale in the large literature suggesting that standard exchange rate models cannot outperform a random walk forecast and that the forward rate is not an optimal predictor of the spot rate. However, there is some evidence that the term structure of forward premia contains valuable information for forecasting future spot exchange rates and that exchange rate dynamics display nonlinearities. This chapter proposes a term-structure forecasting model of exchange rates based on a regime-switching vector equilibrium correction model which is novel in this context. Our model significantly outperforms both a random walk and, to a lesser extent, a linear term-structure vector equilibrium correction model for four major dollar exchange rates across a range of horizons. The second chapter proposes a vector equilibrium correction model of stock returns that exploits the information in the futures market, while also allowing for regime-switching behavior and international spillovers across stock market indices. Using data for three major stock market indices since 1989, we find that: (i) in sample, the model outperforms several alternative models on the basis of standard statistical criteria; (ii) in out-of-sample forecasting, the model does not produce significant gains in terms of point forecasts relative to more parsimonious alternative specifications, but it does so both in terms of market timing ability and in density forecasting performance. The importance of these gains is illustrated with a simple application to a risk management problem. The third chapter re-examines a major puzzle in international finance that is the inability of exchange rate models based on monetary fundamentals to produce better out-of-sample forecasts of the nominal exchange rate than a naive random walk. While prior research has generally evaluated exchange rate forecasts using conventional statistical measures of forecast accuracy, this chapter investigates whether there is any economic value to the predictive power of monetary fundamentals for the exchange rate. We estimate, using a framework that allows for parameter uncertainty, the economic and utility gains to an investor who manages her portfolio based on exchange rate forecasts from a monetary fundamentals model. In contrast to much previous research, we find that the economic value of the exchange rate forecasts implied by monetary fundamentals can be substantially greater than the economic value of forecasts obtained using a random walk across a range of horizons. In sum this thesis adds to the relevant literature on forecasting financial variables by providing insights and evidence to researchers and indicating potential avenues for futures research.

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Fortune, Christopher Joseph. "Factors affecting the selection of building project price forecasting tools". Thesis, Heriot-Watt University, 1999. http://hdl.handle.net/10399/1271.

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This thesis contributes to what is known about the investigation and formulation phases of the building project price forecasting advice process. The research has developed a greater understanding of what general factors affect the selection of non-traditional types of building project price forecasting models. The thesis adopted a two-phased combined research approach. The first phase required a population mailed survey to be executed with over two thousand three hundred quantity surveying organisations located across England in 1997. The second phase required thirty-one in-depth interviews to be executed, with informed practitioners, in five rounds of data collection. Consequently, this research firstly, established the types of building project price forecasting models or tools in-use in England. The study found that the called for paradigm shift away from the traditional types of models, had not yet been generally achieved. The study provided evidence that some types of quantity surveying organisations were moving towards the adoption of the non-traditional models, for use as additional tools. The study then, secondly, identified a number of general factors that were found to affect the selection of non-traditional types of building project price forecasting models. The thesis concluded by generating a grounded constraints-based theory of factors found to affect the selection of non-traditional types of building project price forecasting models. The emergent theory identified the parameters needed to enable all types of quantity surveying organisations to become involved with the selection of non-traditional models or tools.

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Brooks, Christopher. "Testing for and forecasting nonlinearities in daily sterling exchange rates". Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318624.

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Wang, Wei-Hsin. "Comparative analysis of approaches to short-term foreign exchange rates forecasting". Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313480.

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Orr, Allison McLean. "The determination of industrial property rental values : theory, evidence and forecasting". Thesis, University of the West of Scotland, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296190.

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Yuan, Hang. "Overlapping regression and forecasting : essays on economic cycles". Thesis, Lancaster University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.657624.

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This thesis documents research and findings of three essays in the area of prediction and forecast of economic cycles. Each essay in this thesis is dedicated to address one particular aspect of the research. The thesis also contributes to the existing research by providing additional empirical evidence on the predictability of the real economic activity and recessions in the US. The first essay (Chapter 2) focuses on the long horizon inference methods, and examines the predictability of real GDP growth rate in the US using several well known predictors. A battery of specifically designed inference methods are employed in the analysis to address statistical complications introduced by overlapping long horizon dependent variable. The recursive moving block Jack-knife method is used to correct the biased estimated coefficients. The second essay (Chapter 3) puts emphasize on the out-of-sample forecast evaluation between nested and non-nested model. The forecast performances of various forecasting models are evaluated against two naive benchmark models, namely the random walk model and the autoregressive model. For the nested model, the asymptotically valid critical values for the forecast evaluation are derived from bootstrap simulations. The robustness of the test results are examined by Rossi and Inoue (2011 )'s robustness tests. The third essay (Chapter 4) utilizes the probit model to examine the predictability of recessions in the US. We evaluate the predictive power of several non-linear transformed predictors against that of the yield spread, and we also introduce a similar approach as in Rossi and !noue (2011) to examine the average and peak forecast ability of the predictors. The forecast performances of the predictors under various model specifications are carefully investigated in this chapter as well

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Mansur, Mohaimen. "Essays on forecasting financial and economic time series". Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8576.

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This thesis comprises three main chapters focusing on a number of issues related to forecasting economic and nancial time series. Chapter 2 contains a detailed empirical study comparing forecast perfor- mance of a number of popular term structure models in predicting the UK yield curve. Several questions are addressed and investigated, such as whether macroeconomic information helps in forecasting yields and whether predict- ing performance of models change over time. We nd evidence of signi cant time-variation in forecast accuracy of competing models, particularly during the recent nancial crisis period. Chapter 3 explores density forecasts of the yield curve which, unlike the point forecasts, provide a full account of possible uncertainties surrounding the forecasts. We contribute by evaluating predictive performance of the recently developed stochastic-volatility arbitrage-free Nelson-Siegel models of Chris- tensen et al. (2010). The one-month-ahead predictive densities of the models appear to be inferior compared to those of their constant-volatility counter- parts. The advantage of modelling time-varying volatilities becomes evident only when forecasting interest rates at longer horizons. Chapter 3 deals with a more general problem of forecasting time series under structural change and long memory noise. Presence of long memory in the data is often easily confused with structural change. Wrongly account- ing for one when the other is present may lead to serious forecast failure. In our search for a forecast method that can perform reliably in presence of both features we extend the recent work of Giraitis et al. (2013). A forecast strategy with data-dependent discounting is adopted and typical robust-to- structural-change methods such as rolling window regression, forecast averag- ing and exponentially weighted moving average methods are exploited. We provide detailed theoretical analyses of forecast optimality by considering cer- tain types of structural changes and various degrees of long range dependence in noise. An extensive Monte Carlo study and empirical application to many UK time series ensure usefulness of adaptive forecast methods.

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Reed, Larry Donnell. "Forecasting economic impacts of the Third Harbor Tunnel". Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/77341.

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Mapp, Claudette Melissa. "Forecasting economic impacts of the Boston Harbor cleanup". Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/76018.

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Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1989.
Title as it appeared in M.I.T. Graduate List, June 1989: Forecasting economic benefits of the Boston Harbor cleanup.
Includes bibliographical references (leaves 97-98).
by Claudette Melissa Mapp.
M.C.P.

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Furman, Yoel Avraham. "Forecasting with large datasets". Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:69f2833b-cc53-457a-8426-37c06df85bc2.

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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.

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Spagnolo, Nicola. "Nonlinearity testing, model selection and forecasting in the prescence of Markov regime switching". Thesis, Birkbeck (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368914.

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Banavas, Georgios Nikolaos. "Prognosis : historical pattern matching for economic forecasting and trading". Thesis, University of Plymouth, 2000. http://hdl.handle.net/10026.1/1642.

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In recent years financial markets have become complex environments that continuously change and they change quickly. The strong link between the continuous change in the markets and the danger of losing money when trading in them, has made financial studies a domain that concentrates increasing scientific and business attention. In this context, the development of computational techniques that can monitor recent financial events can process them according to their similarity with historical data recordings, and can support financial decision making, is a challenging problem. In this work, the principal idea for tackling this problem is the integration of 'current' market information as derived from the market's recent past and historical information. A robust technique which is based on flexible pattern matching, segmented data representations, time warping, and time series embedding dimension measures is proposed. Complementary time series derived features, concerning trend structures, temporal considerations and statistical measures are systematically combined in this technique. All these components have been integrated into a software package, which I called PROGNOSIS, that can selectively monitor its application and allows systematic evaluation in terms of financial forecasting and trading performance. In addition, two other topics are discussed in this thesis. Firstly, in chapter 3, a neural network, that is known as the Growing Neural Gas network, is employed for financial forecasting and trading. To my knowledge, this network has never been applied before to financial problems. Based on this a neural network forecasting and trading benchmark was constructed for comparison purposes. Secondly, a novel method of approaching the well established co-integraton theory is proposed in the last chapter of the thesis. This method enhances the co-integration theory by integrating into it local time relations between two time series. These local time dependencies are identified using dynamic time warping. The hypothesis that is tested is that local time shifts, delays, shrinks or stretches, if identified, may help to reveal co-integrating movement between the two time series. I called this type of co-integration time-warped co-integration. To this end, the time-warped co-integration framework is presented as an error correction model and it is tested on arbitrage trading opportunities within PROGNOSIS.

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VASCONCELOS, GABRIEL FILIPE RODRIGUES. "FORECASTING IN HIGH-DIMENSION: INFLATION AND OTHER ECONOMIC VARIABLES". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35237@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE EXCELENCIA ACADEMICA
Esta tese é composta de quatro artigos e um pacote de R. Todos os artigos têm como foco previsão de variáveis econômicas em alta dimensão. O primeiro artigo mostra que modelos LASSO são muito precisos para prever a inflação brasileira em horizontes curtos de previsão. O segundo artigo utiliza vários métodos de Machine Learning para prever um grupo de variáveis macroeconomicas americanas. Os resultados mostram que uma adaptação no LASSO melhora as previsões com um alto custo computacional. O terceiro artigo também trata da previsão da inflação brasileira, mas em tempo real. Os principais resultados mostram que uma combinação de modelos de Machine Learning é mais precisa do que a previsão do especialista (FOCUS). Finalmente, o último artigo trata da previsão da inflação americana utilizando um grande conjunto de modelos. O modelo vencedor é o Random Forest, que levanta a questão da não-linearidade na inflação americana. Os resultados mostram que tanto a não-linearidade quanto a seleção de variáveis são importantes para os bons resultados do Random Forest.
This thesis is made of four articles and an R package. The articles are all focused on forecasting economic variables on high-dimension. The first article shows that LASSO models are very accurate to forecast the Brazilian inflation in small horizons. The second article uses several Machine Learning models to forecast a set o US macroeconomic variables. The results show that a small adaptation in the LASSO improves the forecasts but with high computational costs. The third article is also on forecasting the Brazilian inflation, but in real-time. The main results show that a combination of Machine Learning models is more accurate than the FOCUS specialist forecasts. Finally, the last article is about forecasting the US inflation using a very large set of models. The winning model is the Random Forest, which opens the discussion of nonlinearity in the US inflation. The results show that both nonlinearity and variable selection are important features for the Random Forest performance.

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Jeon, Yongil. "Four essays on forecasting evaluation and econometric estimation /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1999. http://wwwlib.umi.com/cr/ucsd/fullcit?p9949690.

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Liu, Guangling. "Forecasting with DSGE models the case of South Africa /". Pretoria : [s.n.], 2007. http://upetd.up.ac.za/thesis/available/etd-06102008-094841/.

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Lazim, Mohamad Alias. "Econometric forecasting models and model evaluation : a case study of air passenger traffic flow". Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296880.

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Dam, Robert A. "Economic limits to corporate growth in America". Thesis, Monterey, California. Naval Postgraduate School, 2006. http://hdl.handle.net/10945/2514.

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This work explores the relationship between corporate and economic growth within the United States since 1929. The corporate share of GDP climbed from 52.5 percent in 1929 to 59.7 percent in 2005. Depending upon the years included and the method of estimating respective growth rates, this increasing share of GDP accounts for up to 14 percent of real domestic corporate growth. However, the domestic corporate share of GDP can never exceed 100 percent. Subject to numerous assumptions, the models presented here estimate that this source of corporate growth could be exhausted as early as the year 2032. Given the lack of discussion of this issue in the relevant literature, it is unlikely that current stock valuations account for the eventual loss of this source of growth. The actual effect on stock prices of such a slowdown of domestic corporate growth will depend not only on how far into the future such an event occurs, but also on how successful these corporations are at finding new growth opportunities overseas. More research is needed to better model

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Marriott, Richard Keyworth. "Estimating and forecasting a demand chain for food using cross-section and time-series data". Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266903.

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Julia, Draeb. "Reexamining the Expectations Hypothesis of the Term Structure of Interest Rates: an Out-of-Sample Forecasting Perspective". Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1623251442890825.

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Harrington, Robert P. "Forecasting corporate performance". Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/54515.

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For the past twenty years, the usefulness of accounting information has been emphasized. In 1966 the American Accounting Association in its State of Basic Accounting Theory asserted that usefulness is the primary purpose of external financial reports. In 1978 the State of Financial Accounting Concepts, No. 1 affirmed the usefulness criterion. "Financial reporting should provide information that is useful to present and potential investors and creditors and other users..." Information is useful if it facilitates decision making. Moreover, all decisions are future-oriented; they are based on a prognosis of future events. The objective of this research, therefore, is to examine some factors that affect the decision maker's ability to use financial information to make good predictions and thereby good decisions. There are two major purposes of the study. The first is to gain insight into the amount of increase in prediction accuracy that is expected to be achieved when a model replaces the human decision-maker in the selection of cues. The second major purpose is to examine the information overload phenomenon to provide research evidence to determine the point at which additional information may contaminate prediction accuracy. The research methodology is based on the lens model developed by Eyon Brunswick in 1952. Multiple linear regression equations are used to capture the participants’ models, and correlation statistics are used to measure prediction accuracy.
Ph. D.

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Васильєва, Тетяна Анатоліївна, Татьяна Анатольевна Васильева, Tetiana Anatoliivna Vasylieva, Сергій Вячеславович Лєонов, Сергей Вячеславович Леонов, Serhii Viacheslavovych Lieonov, Наталія Євгенівна Летуновська, Наталия Евгеньевна Летуновская e Nataliia Yevhenivna Letunovska. "The economic impact of COVID-19: forecasting for ukrainian regions". Thesis, Sumy State University, 2020. https://essuir.sumdu.edu.ua/handle/123456789/80904.

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The world scientific community has developed many economic and mathematical models for predicting and analysing the multidirectional impact of COVID-19 on various aspects of society. Modelling the economic consequences of a new dangerous virus for different countries has a vital role today. There is a lack of such research in Ukraine. Foreign papers, particularly from the highly-rated databases Scopus and Web of Science, show an increase in research on this topic and their citations. It is appropriate to mention specific quotes from the report of the consulting company One Philosophy Insights, which states that quarantine has affected the economy of Ukraine in different ways.

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Васильєва, Тетяна Анатоліївна, Татьяна Анатольевна Васильева, Tetiana Anatoliivna Vasylieva, Сергій Вячеславович Лєонов, Сергей Вячеславович Леонов, Serhii Viacheslavovych Lieonov, Наталія Євгенівна Летуновська, Наталия Евгеньевна Летуновская e Nataliia Yevhenivna Letunovska. "The economic impact of covid-19: forecasting for Ukrainian regions". Thesis, Sumy State University, 2020. https://essuir.sumdu.edu.ua/handle/123456789/80956.

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У тезах наведені економічні показники в Україні, що показують прямий вплив пандемії COVID-19 на соціально-економічне становище ряду вітчизняних галузей та регіонів. Обгрунтована доцільність використання економіко-математичних моделей для прогнозування розвитку подій під час такого роду епідемій.
В тезисах приведены экономические показатели в Украине, которые показывают прямое влияние пандемии COVID-19 на социально-экономическое положение ряда отечественных отраслей и регионов. Обоснована целесообразность использования экономико-математических моделей для прогнозирования развития событий во время такого рода эпидемий.
The abstracts present economic indicators in Ukraine that show the direct impact of the COVID-19 pandemic on the socio-economic situation of a number of domestic industries and regions. The expediency of using economic and mathematical models to predict the development of events during such epidemics is substantiated.

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Boshoff, Willem Hendrik. "The properties of cycles in South African financial variables and their relation to the business cycle". Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019.1/1733.

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Thesis (MComm (Economics)--University of Stellenbosch, 2006.
The goal of this thesis is twofold: it aims, firstly, at a description of cycles in South African financial variables and, secondly, at the evaluation of the relationship between cycles in financial variables and the South African business cycle. The study is based on the original business cycle framework of Arthur Burns and Wesley Mitchell, but incorporates recent contributions by Australian economists Don Harding and Adrian Pagan, as well as the work of the Economic Cycle Research Institute in New York. Part I of the thesis is concerned with the characteristics of cycles in financial variables within the South African context. The first chapter presents a taxonomy of the concepts of classical, deviation and growth rate cycles in order to establish a simple reference framework for cycle concepts. At this point the concept of a ‘turning point cycle’ is introduced, with particular focus on the non-parametric method of turning point identification, following Harding and Pagan’s recent translation of the original work of Burns and Mitchell into a modern version with a sound statistical basis. With the turning points identified the dissertation proceeds to an exposition of descriptive measures of expansion and contraction phases. The second chapter entails an empirical report on descriptive results for amplitude and duration characteristics of cycle phases in the different financial variables, with separate reports for classical cycles and growth rate cycles. Chapter two concludes with a series of tables in which the behaviour of cycle phases are compared for different financial variables. Part II considers financial variables as potential leading indicators of the business cycle in South Africa. Chapter 3 introduces the concept ‘leading indicator’ to this end and distinguishes the original concept from modern, econometric versions. The chapter then introduces a framework for evaluating potential leading indicators, which emphasises two requirements: firstly, broad co-movement between cycles in the proposed leading indicator and the business cycle and, secondly, stability in the number of months between turning points in cycles of the proposed indicator and business cycle turning points. The capacity of potential indicators to meet these criteria is measured via the concordance statistic and the ‘lead profile’ respectively. Chapter four provides the statistical basis for the concordance statistic, after which the empirical results (presented separately for classical and growth rate cycles) are presented. The fifth chapter presents the statistical test for the stability of the interval by which cyclical turning points in the potential indicator lead turning points in the business cycle. Empirical results are presented in both tabular form (the ‘lead profile’) and graphical form (the ‘lead profile chart’). As far as can be determined, this analysis represents the first application of the ‘lead profile’ evaluation to financial variables. Chapter six concludes by presenting a summary of the results and a brief comparison with findings from an econometric study of leading indicators for South Africa.

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Yang, Yibai. "Economic growth under endogenous technological change and time preference : empirical evidence from selected OECD countries". Thesis, The University of Sydney, 2012. https://hdl.handle.net/2123/28824.

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Technological change and time preference are two important factors affecting the mechanics of the process of economic growth, and the endogeneity of these factors receives increasing attention in recent studies. This thesis provides analyses of the roles of endogenous technological change and endogenous time preference in the growth process, With particular interests in long—run growth7 the organization and direction of innovation, equilibrium dynamics, and improvements of household welfare. The thesis consists of the following chapters. Chapter 1 introduces the research. Chapter 2 reviews the related literature and the modelling foundations that are extended in the thesis. Chapter 3 extends the basic Schumpeterian growth model to investigate the relation between the cooperative R&D decision by firms and the aggregate technological Change (productivity growth). In a duopolistic intermediate—good market, duopolistic firms are concerned With their individual R&D cost and profits in noncooperative R&D, Which yields a constant successful probability of innovation; whereas in cooperative R&D, the learning ability and the probability of successful innovation for the duopohsts increase as the level of technology grows, but the R&D cost and profits are shared equally. We show that the duopolists prefer to cooperate in R&D as the economy converges Closer toward the frontier, Which is consistent With our empirical evidence. Moreover, we analyse the dynamics of the convergence paths induced by both R&D cooperation options, implying that if the learning ability of the duopolists is sufficiently high, the economy converges toward a high—technology steady state near the frontier; however, the economy’s technology relative to the frontier may stop growing in a nonconvergence trap if the cost of imitation is relatively low. Chapter 4 focuses on the direction of technological change and its effect on the growth process and individual welfare. This chapter proposes a directed technological change model where managerial skills become complementary to the production skills in intermediate—good production, and it provides the solutions of high— relative to low—skilled technologies and long-run growth rates. We derive several results from this framework. First, weak and strong equilibrium biases of technological change still hold for the management sectors such that an increase in the relative managerial skills raises the wage inequality between high— and low-skilled managers, which explains the empirical evidence in the US, Australian and British labour markets. Second, the transitional dynamics of the economy—wide technology implies that a sectoral management shock causes temporary growth in both the aggregate total factor productivity and the aggregate output, which is higher than the balanced growth path level. Third, we show that education but not on—the—job training can be a feasible scheme to acquire managerial skills if individuals are heterogeneous in their ability. Chapter 5 investigates the determinants of time preference and their effects on equilibrium dynamics in the canonical neoclassical growth model, the AK model, and the real business cycle (RBC) model. Two types of marginal impatience endogenize the representative household’s discount function to alter its time preference: increasing (Koopmans—Uzawa type) and decreasing (Becker-Mulligan type), which are induced by current consumption and future—oriented capital, respectively. In the canonical neo—classical growth model, we derive a set of sufficient conditions for a unique steady state equilibrium, in which local stability still holds when marginal return to capital decreases more slowly than marginal impatience. Moreover, based on functional forms and assumptions, this framework can be extended to the endogenous version of neo—classical growth: the AK model, which sustains long—run growth. In an application of the uncertainty version—the RBC model in a small open economy—the equilibrium level of future—oriented capital is obtained in a reduced form, which simultaneously overcomes the nonstationarity problem. The positive relation (procyclicality) between the turnover of future—oriented and current consumption is also consistent with the empirical evidence from Australia. Chapter 6 summarises the research results and points out directions of future research. This thesis has implications for improving a household’s welfare. In Chapter 3, we find that the representative household in a country that converges towards the frontier along the cooperative R&D path can have higher welfare than the household in a country along the noncooperative R&D convergence path. Moreover, we claim that the government could contribute a lump—sum subsidy as a growth maximization policy for the economy to ameliorate the under contribution to learning ability of duopolistic firms resulted from the decentralized equilibrium, which could lead to growth and welfare maximization, simultaneously. Chapter 4 shows that subsidies to encourage education can increase the supply of one type of skill. If an individual belongs to this type of skilled group, the between-group wage inequality will increase this individual’s welfare if the bias of technological change is strong enough to offset the time cost of education. Finally, Chapter 5 demonstrates that expenditures by the household on particular goods reduce the remoteness of future pleasures. If the effect of these expenditures on the rate of time preference exceeds their counterpart in current utility, the household’s welfare will also be improved.

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Moore, Ronald K. (Ronald Kenneth). "Prediction of Bankruptcy Using Financial Ratios, Information Measures, National Economic Data and Texas Economic Data". Thesis, North Texas State University, 1987. https://digital.library.unt.edu/ark:/67531/metadc331133/.

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The main purpose of this study is to develop a bankruptcy prediction model for the small business firm. Data was collected from the Dallas Small Business Administration (SBA), making this study specific to its decision makers. Existing research has produced models which predominately use financial ratios and information measures either independently or combined, and a few research models have used economic trends. This study varies from past studies in that it includes regional economic variables from the states of Texas. A sample of three-year data for 138 firms included fifteen bankrupt firms. This proportion of bankrupt/nonbankrupt firms approximates the proportion of repayed/defaulted loans in the SBA. Stepwise regression, set at the .15 level of significance, reduced a total of fifty-three variables to nine. These nine variables were then used to test twelve predictive models. All twelve models tested improved the SBA repayment rate and only two of the twelve would have caused the SBA to deny loans to applicants who eventually repaid. The study determined the model that included financial ratios, information measures, and Texas economic variables as best. It was also demonstrated that some of the variables used in this model could be eliminated without decreasing the predictive power of the model. The best of twelve models improved the SBA default rate by 40 percent without denying a loan to any applicant that eventually repaid.

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Chen, Yuang-Sung Al. "Financial analyst forecast dispersion : determinants and usefulness as an ex-ante measure of risk". Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/29391.

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Liu, Yu. "Essays on analyst growth forecasts and stock market valuations /". View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ACCT%202008%20LIU.

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Betz, Gregor. "Prediction or prophecy? : the boundaries of economic foreknowledge and their socio-political consequences /". Wiesbaden : Dt. Univ.-Verl, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014606920&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA.

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Giyose, Dorrington. "Possible scenarios for Africa's economic futures towards 2055". Thesis, Nelson Mandela Metropolitan University, 2014. http://hdl.handle.net/10948/d1021188.

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This academic discourse is a research treatise that is submitted in fulfilment of the requirements for the Masters in Business Administration (MBA) degree at the Nelson Mandela Metropolitan University (NMMU). Purpose – The purpose of this treatise is to develop four possible scenarios for Africa’s economic futures over the next 40 years, i.e. towards 2055. This study will expose the possible, probable, plausible, and preferable (desirable) scenarios for Africa towards 2055. Design/Methodology/Approach – This study employs a Futures Studies methodology that is known as scenario planning. The key variables of the scenarios are clustered as follows: Good governance and good economic growth; Good governance and bad economic growth; Bad governance and bad economic growth; as well as bad governance and good economic growth. Each of these scenarios begin with the current state of affairs in Africa. As such, the four scenarios in this study are informed by the current affairs in African countries as is internationally observed by scientists, researchers, as well as global views and opinions. Practical implications – This academic discourse provides useful insight into the causality relationship between the political, economic, sociological, technological, ecological, as well as legal factors (PESTEL factors) on the continent and the possible scenarios for Africa’s economic futures towards 2055. The aforementioned causality relationship between the abovementioned variables allows for insight into the drivers for change for Africa as well as in what way to anticipate these changes in accordance with scenario planning. Originality/Value: This treatise looks at the economic futures of Africa over the next 40 years from the point of view of African planners and African decision-makers.

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Fan, Yat-chau, e 范一舟. "Modelling and forecasting Hong Kong construction demands". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45547269.

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Kang, Sungjun. "Forecasting inflation with probit and regression models /". free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9946268.

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Liebermann, Joëlle. "Essays in real-time forecasting". Doctoral thesis, Universite Libre de Bruxelles, 2012. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209644.

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This thesis contains three essays in the field of real-time econometrics, and more particularly

forecasting.

The issue of using data as available in real-time to forecasters, policymakers or financial

markets is an important one which has only recently been taken on board in the empirical

literature. Data available and used in real-time are preliminary and differ from ex-post

revised data, and given that data revisions may be quite substantial, the use of latest

available instead of real-time can substantially affect empirical findings (see, among others,

Croushore’s (2011) survey). Furthermore, as variables are released on different dates

and with varying degrees of publication lags, in order not to disregard timely information,

datasets are characterized by the so-called “ragged-edge”structure problem. Hence, special

econometric frameworks, such as developed by Giannone, Reichlin and Small (2008) must

be used.

The first Chapter, “The impact of macroeconomic news on bond yields: (in)stabilities over

time and relative importance”, studies the reaction of U.S. Treasury bond yields to real-time

market-based news in the daily flow of macroeconomic releases which provide most of the

relevant information on their fundamentals, i.e. the state of the economy and inflation. We

find that yields react systematically to a set of news consisting of the soft data, which have

very short publication lags, and the most timely hard data, with the employment report

being the most important release. However, sub-samples evidence reveals that parameter

instability in terms of absolute and relative size of yields response to news, as well as

significance, is present. Especially, the often cited dominance to markets of the employment

report has been evolving over time, as the size of the yields reaction to it was steadily

increasing. Moreover, over the recent crisis period there has been an overall switch in the

relative importance of soft and hard data compared to the pre-crisis period, with the latter

becoming more important even if less timely, and the scope of hard data to which markets

react has increased and is more balanced as less concentrated on the employment report.

Markets have become more reactive to news over the recent crisis period, particularly to

hard data. This is a consequence of the fact that in periods of high uncertainty (bad state),

markets starve for information and attach a higher value to the marginal information content

of these news releases.

The second and third Chapters focus on the real-time ability of models to now-and-forecast

in a data-rich environment. It uses an econometric framework, that can deal with large

panels that have a “ragged-edge”structure, and to evaluate the models in real-time, we

constructed a database of vintages for US variables reproducing the exact information that

was available to a real-time forecaster.

The second Chapter, “Real-time nowcasting of GDP: a factor model versus professional

forecasters”, performs a fully real-time nowcasting (forecasting) exercise of US real GDP

growth using Giannone, Reichlin and Smalls (2008), henceforth (GRS), dynamic factor

model (DFM) framework which enables to handle large unbalanced datasets as available

in real-time. We track the daily evolution throughout the current and next quarter of the

model nowcasting performance. Similarly to GRS’s pseudo real-time results, we find that

the precision of the nowcasts increases with information releases. Moreover, the Survey of

Professional Forecasters does not carry additional information with respect to the model,

suggesting that the often cited superiority of the former, attributable to judgment, is weak

over our sample. As one moves forward along the real-time data flow, the continuous

updating of the model provides a more precise estimate of current quarter GDP growth and

the Survey of Professional Forecasters becomes stale. These results are robust to the recent

recession period.

The last Chapter, “Real-time forecasting in a data-rich environment”, evaluates the ability

of different models, to forecast key real and nominal U.S. monthly macroeconomic variables

in a data-rich environment and from the perspective of a real-time forecaster. Among

the approaches used to forecast in a data-rich environment, we use pooling of bi-variate

forecasts which is an indirect way to exploit large cross-section and the directly pooling of

information using a high-dimensional model (DFM and Bayesian VAR). Furthermore forecasts

combination schemes are used, to overcome the choice of model specification faced by

the practitioner (e.g. which criteria to use to select the parametrization of the model), as

we seek for evidence regarding the performance of a model that is robust across specifications/

combination schemes. Our findings show that predictability of the real variables is

confined over the recent recession/crisis period. This in line with the findings of D’Agostino

and Giannone (2012) over an earlier period, that gains in relative performance of models

using large datasets over univariate models are driven by downturn periods which are characterized

by higher comovements. These results are robust to the combination schemes

or models used. A point worth mentioning is that for nowcasting GDP exploiting crosssectional

information along the real-time data flow also helps over the end of the great moderation period. Since this is a quarterly aggregate proxying the state of the economy,

monthly variables carry information content for GDP. But similarly to the findings for the

monthly variables, predictability, as measured by the gains relative to the naive random

walk model, is higher during crisis/recession period than during tranquil times. Regarding

inflation, results are stable across time, but predictability is mainly found at nowcasting

and forecasting one-month ahead, with the BVAR standing out at nowcasting. The results

show that the forecasting gains at these short horizons stem mainly from exploiting timely

information. The results also show that direct pooling of information using a high dimensional

model (DFM or BVAR) which takes into account the cross-correlation between the

variables and efficiently deals with the “ragged-edge”structure of the dataset, yields more

accurate forecasts than the indirect pooling of bi-variate forecasts/models.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

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Al-Teraiki, Ahmed B. M. "A macroeconometric model of Saudi Arabia for economic stabilisation and forecasting". Thesis, Loughborough University, 1999. https://dspace.lboro.ac.uk/2134/7286.

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The purpose of this study is to construct a macroeconometric model for the Saudi Arabian economy in order to assess the effects of external shocks through such variables as the price of (oil) exports, real (oil) exports, and the price of imports. This model follows the methodology of the aggregate demand and supply. Due to the absence of interest rates, the formulation of the aggregate demand, following the monetary approach to the income determination, is done by combining the equations from the monetary sector in addition to the government and foreign sectors of the economy. The aggregate supply side of the economy is formulated by combining the equations from the oil and non-oil production sectors. The model determines the behaviours of such important endogenous variables as the real absorptive capacity, real oil and non-oil GDP, real imports, velocity of money, money supply, balance of payments, government oil and non-oil revenues, government expenditure, government deficit, and non-oil GDP and general price inflation rates. The estimated model satisfactorily simulates the reality of the economy for the estimation period of 1971-1994. This, therefore, justifies the use of the model for both multiplier and scenario analyses. The multiplier analysis evaluates the cffects of a 10% change in the price of (oil) exports, real (oil) exports, and the price of imports on the endogenous variables. The scenario analysis, however, examines the behaviours of the endogenous variables for 1999-2005 based on several scenarios on the price of (oil) exports, real (oil) exports, and the price of imports. Concentrating on three sets of scenarios corresponding to low, moderate, and high level of oil prices, our study concludes that a sound economy into the next century requires more aggressive privatisation policies. That is, the government policies should drastically limit the government expenditure and, instead, encourage the private sector to invest and participate more aggressively in the economic development projects.

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Fuentes, Antonio. "An Analysis of Sensitivity in Economic Forecasting for Pavement Management Systems". DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4279.

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The research presented in this thesis investigates the effect the data collection process has on the results of the economic analysis in pavement management systems. The incorporation of pavement management systems into software packages has enabled local governments to easily implement and maintain an asset management plan. However a general standard has yet to be set, enabling local governments to select from several methods of data collection. In this research, two pavement management system software packages with different data collection methods are analyzed on the common estimated recommended M&R cost provided by their respective economic analysis. The Transportation Asset Management Software (TAMS) software package developed by the Utah LTAP Center at Utah State University consists of a data collection process composed of nine asphalt pavement distress observations. The Micro PAVERTM software package developed by the Army Corps of Engineers consists of a data collection process composed of 20 asphalt pavement distress observations. A Latin-hypercube sample set was input into each software package, as well as actual local government pavement condition data for the City of Smithfield, Utah and the City of Tremonton, Utah. This resulted in six total data sets for analysis, three entered and analyzed in TAMS and three entered and analyzed in Micro PAVERTM. These sample sets were then statistically modeled to determine the effect each distress variable had on the response produced by the economic analysis of estimated recommended M&R costs. Due to the different methodologies of pavement condition data collection, two different statistical approaches were utilized during the sensitivity analysis. The TAMS data sets consisted of a general linear regression model, while the Micro PAVERTM data sets consisted of an analysis of covariance model. It was determined that each data set had varying results in terms of sensitive pavement distresses; however the common sensitive distress in all of the data sets was that of alligator cracking/fatigue. This research also investigates the possibility of utilizing statistically produced models as a direct cost estimator given pavement condition data.

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