How important are financing constraints in explaining the cyclical behaviour of investment. How do they affect the investment responses to macroeconomic shocks. This thesis answers these questions by developing a structural model of investment with financing and irreversibility constraints and by analysing its implications both theoretically and empirically. After briefly reviewing the recent advancements of the investment literature in chapter 1, in chapter 2 we present a preliminary empirical analysis of the links between financial structure and firm dynamics. In chapter 3 we develop a basic structural model which analyses optimal investment and saving choices of entrepreneurs in the presence of uncertainty as well as of financing constraints. We show that future expected financing constraints generate a precautionary saving behaviour which affects the optimal allocation between risky investment and saving. In chapter 4 we extend the basic model to include both fixed and variable capital as well as financing constraints and irreversibility of fixed capital. We show that the interactions between financing and irreversibility constraints amplify the effects of financing constraints on the cyclical fluctuations of investment and production. This interaction together with the precautionary saving behaviour is essential in explaining a number of stylised facts about investment dynamics: i) aggregate inventory investment is very volatile and procyclical, especially in recessions; ii) it leads the business cycle, while fixed capital investment lags it; iii) fixed and especially inventory investment are sensitive to net worth; iv) output and inventories are more volatile and procyclical for small firms than for large ones. In chapter 5 we verify empirically the theoretical results derived in chapter 4. We use our panel of balance sheet data on Italian manufacturing firms to test and not reject the financing constraints hypothesis. This hypothesis is also strongly supported by the direct qualitative information about the problems faced by the entrepreneurs in financing new investment projects.

This study aims to estimate the ratio of non-performing loans to total loans (NPL ratio), its determinants and its response to different macroeconomic shocks. As the last financial crises had negative impact on the economy of many countries of the world, we have to strive for preventive measures that would help us to fully or at least partly avoid future crises. It should be achieved by sound risk management practices of all financial institutions. Important part of these risk management practices shall be - among others - stress tests that would test the health of the institution under severe conditions and negative shocks. For this study the vector autoregression model (VAR methodology) is used to see the response of credit risk (in terms of NPL ratio) to macroeconomic shocks in the Czech Republic. The variables used for this study are quarterly time series data of the period from 2002 to 2011 (GDP, inflation rate, unemployment rate, koruna exchange rate (CZK/USD), and interest rate). For each of these variables the impulse response function was created, to show the impact of macroeconomic shocks and the speed of adjustment of NPL ratio to these shocks. Keywords: Financial Crises, Credit Risk Management, Non-performing loans, Macroeconomic Shocks, Czech Republic, VARs

Yes / This paper examines real effective exchange rate (REER) responses to shocks in exchange rate determinants for the West African Monetary Zone (WAMZ) over the period 1980–2015. The analysis is based on a country-by-country VECM, and oil price, supply and demand shocks are identified using long run restrictions in a structural VAR model. We report significant differences in the response of REER to real oil price, productivity (supply) and demand preference shocks across these economies. In addition the relative contribution of these shocks to REER movements in the short and long run appears to be different across economies. Our findings suggest that the WAMZ countries are structurally different, and asymmetric shocks with inadequate adjustment mechanisms imply that a monetary union would be costly.

Doctor of Philosophy / Department of Economics / Lance J. Bachmeier / This dissertation consists of three essays in nonlinear macroeconomic modeling and econometrics. In the first essay, we decompose oil price movements into oil demand (stock market) shocks and oil supply (oil-market) shocks, and examine the response of the stock market to these shocks. We find that when oil prices are “net-increasing”, a stock market shock that causes the S&P 500 to rise by one percentage point will cause the price of oil to rise approximately 0.2 percentage points, with a statistically significant positive effect one day after the stock market shock. On the other hand, the response of the stock market to an oil market shock is a decline of 6.8 percent when the price of oil doubles. For other days, the initial response of the oil market to a stock market shock is the same as in the net oil price increase case (by construction). We then analyze the response of monetary policy to the identified stock market and oil market shocks and find that short-term interest rates respond to the stock market shocks but not the oil market shocks. Finally, we evaluate the predictive power of the decomposed stock market and oil shocks relative to the change in the price of oil. We find statistically significant gains in both the in-sample fit and out-of-sample forecast accuracy when using the identified stock market and oil market shocks rather than the change in the price of oil. The second essay revisits the statistical specification of near-multicollinearity in the logistic regression model using the Probabilistic Reduction approach. We argue that the ceteris paribus clause invoked with near-multicollinearity is rather misleading. This assumption states that one can assess the impact of near-multicollinearity by holding the parameters of the logistic regression model constant, while examining the impact on their standard errors and t-ratios as the correlation (\rho) between the regressors increases. Using the Probabilistic Reduction approach, we derive the parameters (and related statisitics) of the logistic regression model and show that they are functions of \rho , indicating the ceteris paribus clause in the traditional account of near multicollinearity is unattainable. Monte carlo simulations in the paper confirm these findings. We also show that traditional near-multicollinearity diagnostics, such as the variance inflation factor and condition number can fail to detect near-multicollinearity. Overall, the paper finds that near-multicollinearity in the logistic model is highly variable and may not lead to the problems indicated by the traditional account. Therefore, unexpected, unreliable or unstable estimates and inferences should not be blamed on near-multicollinearity. Rather the modeler should return to economic theory or statistical respecification of their model to address these problems. The third essay examines the correlations between income inequality and economic growth using a panel of income distribution data for 3,109 counties of the U.S. We examine the non-spatial dynamic correlations between county inequality and growth using a System GMM approach, and find significant negative relationships between changes in inequality in one period and growth in the subsequent period. We show that this finding is robust across different sample sizes. We further argue that because the space-specific time-invariant variables that affect economic growth and inequality can differ significantly across counties, failure to incorporate spatial effects into a model of growth and inequality may lead to biased results.We assume that dependence among counties only arises from the disturbance process, hence the estimation of a spatial error model. Our results indicate that the bias in the parameter for inequality amounts to about 2.66 percent, while that for initial income amounts to about 21.51 percent.

Housing is both a durable good and an investment vehicle, which makes it importantin people’s daily life aswell as for a nation’s economy. This thesis innovatively applies the Sharpe ratio on evaluating the performance of the US residentialhousing market within the time period from 2005:Q1 to 2019:Q3, andinvestigates how this performance would react upon macroeconomic shocks,including sudden changes in GDP growth rate and personal income growthrate, by establishing a vector auto-regression model with the lag order of four.The main results are that: (1)in the long run, direct residential investments are not significantly more profitable than treasury bills but not disappointing compared to the market portfolio of Dow Jones Industrial Average; (2)the performance of residential investments seem to slightly and positively co-move withGDP and personal income growth rate; (3)the long-term impacts that sudden GDP and personal income growths have on the performance seem inconspicuous and tend to mitigate within about three years and (4) limited evidence supports the hypothesis that current housing market performance can help predictfuture GDP growth rate. Based on housing’s two purpose of consumption andinvestment and the empirical results showing that direct investments on residentialproperties have similar risk-adjusted return level to short-term treasurybills, I suggest that financially feasible households purchase their own houseinstead of renting for a long time, and that speculative investors avoid puttingmoney in residential properties unless they have access to inside information. / Bostäder kan betraktas både som en hållbar vara och som ett investeringsinstrument.De är essentiella för människors vardag och har en viktig roll förett lands ekonomi. Denna avhandling använder innovativt Sharpe-förhållandet för att utvärdera hur den amerikanska bostadsmarknaden presterade under perioden2005: kvartal 1 till 2019: kvartal 3. Den försöker även undersöka om denna prestation påverkas av makroekonomiska chocker inklusive plötsligaförändringar i BNP-tillväxttakt och personliga inkomsttillväxthastighet. Detta görs genom att upprätta en vektor autoregression modell med en fördröjningsordningför fyra. De viktigaste resultaten är att: (1) på långsikt är direktabostadsinvesteringar inte betydligt mer lönsamma än statsskuldväxlar dock är det hellre inte en besvikelse jämfört med en marknadsportföljen av Dow JonesIndustrial Average; (2) Prestationen av bostadsinvesteringar verkar vara svagt och samverkar positivit både med BNP och tillväxttakten för personinkomst.(3) De långsiktiga effekterna av plötsliga tillväxter av BNP och personliga inkomster har på utvecklingen verkar vara vaga och tenderar att mildra inomcirka tre år och (4) begränsade bevis stöder hypotesen om att nuvarande bostadsmarknadsresultat kan bidra till att förutsäga framtida BNP-tillväxttakten.Baserat på bostädernas två syften inom konsumtion och investeringar, visar deempiriska resultaten att direkta investeringar i bostadsfastigheter har en liknande riskjusterad avkastningsnivå som kortfristiga statsskuldväxla. Därför föreslår jag att ekonomisk stabila hushåll borde köpa ett eget hus istället för att hyraunder en lång tid, och att spekulativa investerare borde undvika att satsa pengar inom bostadsfastigheter såvida de inte har tillgång till insider-information.