This thesis investigates the significant factors affecting municipal solid waste (MSW) generation in Turkey. For this purpose, both spatial and non-spatial tech&not / niques are utilized. Non-spatial technique is ordinary least squares (OLS) regression while spatial techniques employed are simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR). The independent variables include socio-economic, demographic and climatic indicators. The results show that nearer provinces tend to have similar solid waste generation rate. Moreover, it is shown that the effects of independent variables vary among provinces. It is demonstrated that educational status and unemployment are significant factors of waste generation in Turkey.

This dissertation examines several issues regarding the congestion on the Upper Mississippi River and Illinois Waterway. Chapter II identifies and measures the impact of lock congestion on grain barge rates on these waterways. Results indicate grain barge rates on both rivers are not affected by lagged lock congestion. In present time, however, lock congestion in the lower reaches of the upper Mississippi and Illinois Rivers are found to increase barge rates that link the north central United States to the lower Mississippi Gulf port area. The findings suggest the impact of lock congestion on grain barge rates is moderate. Chapter III explores the interaction between grain prices in export and domestic markets and transportation rates linking these markets over time. Three model frameworks were evaluated and some consistent results are observed. In general, shocks in transportation rates (barge, rail, and ocean) explain a great proportion of the variation in corn and soybean market prices in the long run, suggesting the importance of transportation in grain price determination. The volatile ocean freight rates are the mostimportant transportation rates contributing to the variation in grain prices, while shocks in barge rates on the Upper Mississippi River and Illinois Waterway generally explain less than 15 percent of the variation in grain prices. The dynamic interrelationships among the six evaluated transportation rates are also found. In addition, the north central corn markets likely have the most influence over other markets while soybean export price dominates the soybean market in the long run. Chapter IV estimates the structural demand for grain barge transportation on both the upper Mississippi and Illinois Rivers. Results suggest foreign grain demand is the most influential force affecting grain barge demand on both rivers. Also, results indicate an inelastic demand for grain barge transportation on the Upper Mississippi in the short run; demand is price elastic in the long run. The price elasticity for grain barge demand on the Illinois River is consistently inelastic. Additionally, the winter season and floods affect demand on the Upper Mississippi negatively, while barge demand increases on the Illinois River in winter.

This article considers the evolution of international business cycle interdependencies among 27 developed and developing countries since the beginning of 1870s, utilising the generalized vector autoregressive (VAR)-based spillover index of Diebold and Yilmaz (2012), which allows the construction of a time-varying measure of business cycle spillovers. We find that, on average, 65% of the forecast error variance of the 27 countries' business cycle shocks is due to international spillovers. However, the magnitude of international business cycle spillovers varies considerably over time. There is a clear increasing trend since the end of World War II and until the middle 1980s. After that, international business cycle interdependencies declined during the period that was dubbed the Great Moderation, and stabilized around the beginning of the twenty-first century. During the Great Recession of 2008-2009, international business cycle spillovers increased to unprecedented levels. Finally, developed countries are consistently ranked as net transmitters of cyclical shocks to developing counties throughout the sample. (authors' abstract)

Macroeconomic stability is one of the most important national objectives in any country. However, economies are often subjected to a number of shocks (internal and external), which can be destabilising, produce volatility and make it difficult to achieve and maintain economic stability. Consequently, various policies are used to help deal with the various shocks that may affect the economy. Of all the available policies, monetary policy appears to have been ever more at the centre of macroeconomic policymaking. Meanwhile, for monetary policy to be effective, there is a need for a better understanding of the transmission mechanism, i.e., the process through which monetary policy decisions are transmitted into changes in real output and inflation. Whereas extensive research on the transmission mechanism has been conducted in developed countries, such work in developing countries, especially in Africa is lacking. This could be due to the fact that it was not long time ago, around the 1990s that countries in Africa started adopting the more modem central bank operations in a market-based economic and financial system characterised by indirect monetary policy. Such operations require an understanding of the transmission mechanism. Lack of empirical analysis of the monetary transmission mechanism in Botswana and developing countries of Africa in general, is the main motivating factor behind this thesis. The main objective of this thesis is, therefore, to estimate the transmission mechanism of monetary policy in Botswana. Three different, but complementary techniques (the Narrative Approach, Vector Autoregression (VAR) analysis and the Structural Approach involving the estimation of a small structural model for Botswana economy) are used. Results from these methods tell a consistent story and indicate that monetary policy in Botswana affects real output and inflation through the interest rate channel, while the exchange rate channel is not operational. The credit channel is also active but not strong. The structural approach also indicates that devaluation is contractionary in Botswana, but more research is necessary before firmer conclusions could be made.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 79-81). Also available on the Internet.

In the field of empirical macroeconomics factor-augmented vector autoregressive (FAVAR) models have become a popular tool in explaining how economic variables interact over time. FAVAR is based upon a data-reduction step using factor estimation, which are then employed in a vector autoregressive model. This paper aims to study alternative methods regarding factor estimation. More precisely, we compare the generally used principal component method with the uncomplicated common correlated effect estimation. Results show low divergence between the two factor estimation methods employed, indicating interchangeability between the two estimation approaches.

This study examines the linkages between output growth and output volatility in the G7 countries over the period 1958M2-2013M8. Using the VAR-based spillover index approach by Diebold and Yilmaz (2012) we find that: i) output growth and volatility are highly intertwined; ii) spillovers have reached unprecedented levels during the global financial crisis; and iii) the US has been the largest transmitter of growth and volatility shocks. Generalized impulse response analyses suggest moderate growth spillovers and sizable volatility spillovers across countries. Cross-variable effects indicate that volatility shocks lead to lower growth, while growth shocks reduce output volatility.

Southern California is the largest U.S. metropolitan area geographically, and demonstrates complex spatial relationships between county labor markets. This paper is interested in investigating the employment dependencies between the core city of Los Angeles its respective commuting sheds, such as San Bernardino and Riverside counties. Using time series data that includes labor demand shocks from the Great Recession, this analysis implements a vector autoregressive model to dissect the relationship between urban and suburban employment changes. The work finds a strong lagging-leading relationship between counties that varies by business cycle phase, and provides policy implications from this relationship.

Riksbanken har under senaste åren blivit kritiserade för deras bristande prognoser av svenska valutakurser. I denna uppsats undersöks det om slumpvandring (RW) är den mest framgångsrika prognosmodellen eller om alternativa ekonometriska prognosmodeller (AR, VAR och VECM) kan estimera framtida växelkurser mer korrekt på kort sikt, ett kvartal fram, och medellång sikt, fyra kvartal fram. I dessa prognosmodeller behandlas fem Svenska makroekonomiska variabler som endogena; KPI, BNP, arbetslöshet, 3 månaders statsobligationer (T-bonds), samt en exogen variabel, Amerikansk-BNP. Den data som används är kvartalsdata från första kvartalet 1993 till andra kvartalet 2020 för respektive variabel. Resultaten från studie visar på att RW är mer ackurat än de multivariata modellerna (VAR och VECM) på både kort sikt och medellång sikt. Residualerna utvärderas genom att kolla på rotmedelkvadratfel (RMSE) från respektive prognos. / In recent years, the Riksbank has been criticized for their underperforming forecasts of Swedish exchange rates. This thesis examines whether the random walk (RW) is the most successful forecasting model when forecasting the exchange rate (SEK / USD) or whether alternative economic forecasting models (AR, VAR and VECM) can estimate future exchange rates more accurately. Both in the short and medium term, one respectively four quarters ahead. In these forecast models, five Swedish macroeconomic variables are treated as endogenous; CPI, GDP, unemployment, three-month Treasury-bonds (T-Bonds), and an exogenous variable, US GDP. The data used is quarterly data from the first quarter of 1993 to the second quarter of 2020 for each variable. Results from the study show that RW is more accurate than the multivariate models (VAR and VECM) in both the short and medium term. The residuals are evaluated by looking at root mean square error (RMSE) from the respective forecast.

In this paper we conduct an out-of-sample forecasting exercise for monthly Swedish air traveler volumes. The models considered are multiplicative seasonal ARIMA, Neural network autoregression, Exponential smoothing, the Prophet model and a Random Walk as a benchmark model. We divide the out-of-sample data into three different evaluation periods: Pre-COVID-19, during COVID-19 and Post-COVID-19 for which we calculate the MAE, MAPE and RMSE for each model in each of these evaluation periods. The results show that for the Pre-COVID-19 period all models produce accurate forecasts, in comparison to the Random Walk model. For the period during COVID-19, no model outperforms the Random Walk, with only Exponential smoothing performing as well as the Random Walk. For the period Post-COVID-19, the best performing models are Random Walk, SARIMA and Exponential smoothing, with all aforementioned models having similar performance.