This thesis is comprised of three independent essays. One essay is in the field of macroeconomics and the other two are in time-series econometrics. The first essay, "Productivity and Business Investment over the Business Cycle", is co-authored with my co-supervisor Hashmat Khan. This essay documents a new stylized fact: the correlation between labour productivity and real business investment in the U.S. data switching from 0.54 to -0.1 in 1990. With the assistance of a bivariate VAR, we find that the response of investment to identified technology shocks has changed signs from positive to negative across two sub-periods: ranging from the time of the post-WWII era to the end of 1980s and from 1990 onwards, whereas the response to non-technology shocks has remained relatively unchanged. Also, the volatility of technology shocks declined less relative to the non-technology shocks. This raises the question of whether relatively more volatile technology shocks and the negative response of investment can together account for the decreased correlation. To answer this question, we consider a canonical DSGE model and simulate data under a variety of assumptions about the parameters representing structural features and volatility of shocks. The second and third essays are in time series econometrics and solely authored by myself. The second essay, however, focuses on the impact of ignoring structural breaks in the conditional volatility parameters on time-varying volatility parameters. The focal point of the third essay is on empirical relevance of structural breaks in time-varying volatility models and the forecasting gains of accommodating structural breaks in the unconditional variance. There are several ways in modeling time-varying volatility. One way is to use the autoregressive conditional heteroskedasticity (ARCH)/generalized ARCH (GARCH) class first introduced by Engle (1982) and Bollerslev (1986). One prominent model is Bollerslev (1986) GARCH model in which the conditional volatility is updated by its own residuals and its lags. This class of models is popular amongst practitioners in finance because they are able to capture stylized facts about asset returns such as fat tails and volatility clustering (Engle and Patton, 2001; Zivot, 2009) and require maximum likelihood methods for estimation. They also perform well in forecasting volatility. For example, Hansen and Lunde (2005) find that it is difficult to beat a simple GARCH(1,1) model in forecasting exchange rate volatility. Another way of modeling time-varying volatility is to use the class of stochastic volatility (SV) models including Taylor's (1986) autoregressive stochastic volatility (ARSV) model. With SV models, the conditional volatility is updated only by its own lags and increasingly used in macroeconomic modeling (i.e.Justiniano and Primiceri (2010)). Fernandez-Villaverde and Rubio-Ramirez (2010) claim that the stochastic volatility model fits better than the GARCH model and is easier to incorporate into DSGE models. However, Creal et al. (2013) recently introduced a new class of models called the generalized autoregressive score (GAS) models. With the GAS volatility framework, the conditional variance is updated by the scaled score of the model's density function instead of the squared residuals. According to Creal et al. (2013), GAS models are advantageous to use because updating the conditional variance using the score of the log-density instead of the second moments can improve a model's fit to data. They are also found to be less sensitive to other forms of misspecification such as outliers. As mentioned by Maddala and Kim (1998), structural breaks are considered to be one form of outliers. This raises the question about whether GAS volatility models are less sensitive to parameter non-constancy. This issue of ignoring structural breaks in the volatility parameters is important because neglecting breaks can cause the conditional variance to exhibit unit root behaviour in which the unconditional variance is undefined, implying that any shock to the variance will not gradually decline (Lamoureux and Lastrapes, 1990). The impact of ignoring parameter non-constancy is found in GARCH literature (see Lamoureux and Lastrapes, 1990; Hillebrand, 2005) and in SV literature (Psaradakis and Tzavalis, 1999; Kramer and Messow, 2012) in which the estimated persistence parameter overestimates its true value and approaches one. However, it has never been addressed in GAS literature until now. The second essay uses a simple Monte-Carlo simulation study to examine the impact of neglecting parameter non-constancy on the estimated persistence parameter of several GAS and non-GAS models of volatility. Five different volatility models are examined. Of these models, three --the GARCH(1,1), t-GAS(1,1), and Beta-t-EGARCH(1,1) models -- are GAS models, while the other two -- the t-GARCH(1,1) and EGARCH(1,1) models -- are not. Following Hillebrand (2005) who studied only the GARCH model, this essay examines the extent of how biased the estimated persistence parameter are by assessing impact of ignoring breaks on the mean value of the estimated persistence parameter. The impact of neglecting parameter non-constancy on the empirical sampling distributions and coverage probabilities for the estimated persistence parameters are also studied in this essay. For the latter, studying the effect on the coverage probabilities is important because a decrease in coverage probabilities is associated with an increase in Type I error. This study has implications for forecasting. If the size of an ignored break in parameters is small, then there may not be any gains in using forecast methods that accommodate breaks. Empirical evidence suggests that structural breaks are present in data on macro-financial variables such as oil prices and exchange rates. The potentially serious consequences of ignoring a break in GARCH parameters motivated Rapach and Strauss (2008) and Arouri et al. (2012) to study the empirical relevance of structural breaks in the context of GARCH models. However, the literature does not address the empirical relevance of structural breaks in the context of GAS models. The third and final essay contributes to this literature by extending Rapach and Strauss (2008) to include the t-GAS model and by comparing its performance to that of two non-GAS models, the t-GARCH and SV models. The empirical relevance of structural breaks in the models of volatility is assessed using a formal test by Dufour and Torres (1998) to determine how much the estimated parameters change over sub-periods. The in-sample performance of all the models is analyzed using both the weekly USD trade-weighted index between January 1973 and October 2016 and spot oil prices based on West Texas Intermediate between January 1986 and October 2016. The full sample is split into smaller subsamples by break dates chosen based on historical events and policy changes rather than formal tests. This is because commonly-used tests such as CUSUM suffer from low power (Smith, 2008; Xu, 2013). For each sub-period, all models are estimated using either oil or USD returns. The confidence intervals are constructed for the constant of the conditional parameter and the score parameter (or ARCH parameter in GARCH and t-GARCH models). Then Dufour and Torres's union-intersection test is applied to these confidence intervals to determine how much the estimated parameter change over sub-periods. If there is a set of values that intersects the confidence intervals of all sub-periods, then one can conclude that the parameters do not change that much. The out-of-sample performance of all time-varying volatility models are also assessed in the ability to forecast the mean and variance of oil and USD returns. Through this analysis, this essay also addresses whether using models that accommodate structural breaks in the unconditional variance of both GAS and non-GAS models will improve forecasts.

Dynamic stochastic general equilibrium (DSGE) models are the common workhorse of modern macroeconomic theory. Whereas story-telling and policy analysis were in the forefront of applications since its inception, the forecasting perspective of DSGE models is only recently topical. In this study, we perform a post-mortem analysis of the predictive power of DSGE models in the case of Austria's Great Recession in 2009. For this purpose, eight DSGE models with different characteristics (small and large models; closed and open economy models; one and two-country models) were used. The initial hypothesis was that DSGE models are inferior in ex-ante forecasting a crisis. Surprisingly however, it turned out that not all but those models which implemented features of the causes of the global financial crisis (like financial frictions or interbank credit flows) could not only detect the turning point of the Austrian business cycle early in 2008 but they also succeeded in forecasting the following severe recession in 2009. In comparison, non-DSGE methods like the ex-ante forecast with the Global Economic (Macro) Model of Oxford Economics and WIFO's expert forecasts performed comparable or better than most DSGE models in the crisis.

Práce se zabývá malým DSGE modelem (model čtvrté generace) v prostředí české a slovenské ekonomiky. Jádro analytické části tvoří srovnání odhadnutého a optimálního pravidla a zároveň jejich porovnání s reálnými výstupy. Cílem je poukázat na efektivnost centrální banky při stabilizaci variability inflace a výstupu ekonomiky. Teoretická část poskytuje znalosti usnadňující pochopení souvislostí tohoto modelu a zároveň nás obeznámí s modely, které v dnešní době využívá Česká národní banka a Národná banka Slovenska.

Essay 1: Estimation of DSGE Models with Uninformative Priors DSGE models are typically estimated using Bayesian methods, but because prior information may be lacking, a number of papers have developed methods for estimation with less informative priors (diffuse priors). This paper takes this development one step further and suggests a method that allows full information maximum likelihood (FIML) estimation of a medium-sized DSGE model. FIML estimation is equivalent to placing uninformative priors on all parameters. Inference is performed using stochastic simulation techniques. The results reveal that all parameters are identifiable and several parameter estimates differ from previous estimates that were based on more informative priors. These differences are analyzed. Essay 2: A DSGE Model with Labor Hoarding Applied to the US Labor Market In the US, some relatively stable patterns can be observed with respect to employment, production and productivity. An increase in production is followed by an increase in employment with lags of one or two quarters. Productivity leads both production and employment, especially employment. I show that it is possible to replicate this empirical pattern in a model with only one demand-side shock and labor hoarding. I assume that firms have organizational capital that depreciates if workers are utilized to a high degree in current production. When demand increases, firms can increase utilization, but over time, they have to hire more workers and reduce utilization to restore organizational capital. The risk shock turns out to be very dominant and explains virtually all of the dynamics. Essay 3: Demand Shocks and Labor Hoarding: Matching Micro Data In Swedish firm-level data, output is more volatile than employment, and in response to demand shocks, employment follows output with a one- to two-year lag. To explain these observations, we use a model with labor hoarding in which firms can change production by changing the utilization rate of their employees. Matching the impulse response functions, we find that labor hoarding in combination with increasing returns to scale in production and a very high price stickiness can explain the empirical pattern very well. Increasing returns to scale implies a larger percentage change in output than in employment. Price stickiness amplifies volatility in output because the price has a dampening effect on demand changes. Both of these explain the delayed reaction in employment in response to output changes.

This thesis analyzes the incomplete exchange rate pass-through effect on Mongolian economy and its implication on monetary policy under foreign and domestic shocks. The analysis is carried out in a small open economy New Keynesian DSGE model proposed by Monacelli (2005), where incomplete exchange rate pass-through is introduced via nominal rigidities on import prices. In order to accomplish the goal, we firstly derive the solutions of the model, calibrate the parameters, and finally simulate the impulse responses. Moreover, SVAR estimation is achieved to estimate the pass-through. Four main results are obtained. First, the exchange rate pass-through into import price and inflation is 0.69% and 0.49% respectively in short run, implying incomplete pass-through in Mongolia. Second, the exchange rate acts as a shock absorber for domestic productivity and foreign demand shock, but as a shock amplifier for domestic demand shock. Third, in case of incomplete pass-through the central bank of Mongolia is required to adjust the nominal interest rate more under the productivity shock, but less for the domestic and foreign demand shock. Finally, deviations from the law of one price contributes considerably to the variability of the output gap under the low pass-through. Therefore, considering incomplete pass-through in...

The objective of this thesis is to develop alternative forms of Dynamic Stochastic General Equilibrium (DSGE) models for forecasting the South African economy and, in turn, compare them with the forecasts generated by the Classical and Bayesian variants of the Vector Autoregression Models (VARs). Such a comparative analysis is aimed at developing a small-scale micro-founded framework that will help in forecasting the key macroeconomic variables of the economy. The thesis consists of three independent papers. The first paper develops a small-scale DSGE model based on Hansen's (1985) indivisible labor Real Business Cycle (RBC) model. The results suggest that, compared to the VARs and the Bayesian VARs, the DSGE model produces large out-of-sample forecast errors. In the basic RBC framework, business cycle fluctuations are purely driven by real technology shocks. This one-shock assumption makes the RBC models stochastically singular. In order to overcome the singularity problem in the RBC model developed in the first paper, the second paper develops a hybrid model (DSGE-VAR), in which the theoretical model is augmented with unobservable errors having a VAR representation. The model is estimated via maximum likelihood technique. The results suggest DSGE-VAR model outperforms the Classical VAR, but not the Bayesian VARs. However, it does indicate that the forecast accuracy can be improved alarmingly by using the estimated version of the DSGE model. The third paper develops a micro-founded New-Keynesian DSGE (NKDSGE) model. The model consists of three equations, an expectational IS curve, a forward-looking version of the Phillips curve, and a Taylor-type monetary policy rule. The results indicate that, besides the usual usage for policy analysis, a small-scale NKDSGE model has a future for forecasting. The NKDSGE model outperforms both the Classical and Bayesian variants of the VARs in forecasting inflation, but not for output growth and the nominal short-term interest rate. However, the differences of the forecast errors are minor. The indicated success of the NKDSGE model for predicting inflation is important, especially in the context of South Africa - an economy targeting inflation. / Thesis (PhD (Economics))--University of Pretoria, 2008. / Economics / unrestricted

This dissertation consists of three chapters related to macroeconomic implications of financial frictions, along with an application of macro-finance models to the Chinese economy. The first two chapters focus on government guarantees on business loans to state-owned enterprises (SOEs), a typical practice of the Chinese government. Chapter 1 embeds partial loan guarantees into the loan contracting problem, built upon the costly state verification framework. A larger degree of guarantees dampens the sensitivity of the loan rate to a change in leverage, which incentivizes entrepreneurs to lever up. Also, greater guarantees reduce entrepreneurs' exposures to credit risks, hence altering their choices of investment and leverage in response to an exogenous risk shock. Chapter 2 proceeds to develop a New Keynesian dynamic stochastic general equilibrium (DSGE) model and investigates the effect of government guarantees on capital misallocation and business cycle fluctuations in China. On one hand, government guarantees mitigate the influence of the financial accelerator mechanism on investment and production of both SOEs and private-owned enterprises (POEs). On the other hand, by inducing a time-varying dispersion in returns on capital across SOEs and POEs, government guarantees exert a negative impact on the allocative efficiency of resources and thus cause further losses on total factor productivity (TFP) and output during recessions. Quantitative analyses show that partial loan guarantees to SOEs are counterproductive in moderating the reaction of GDP to both risk and technology shocks. Chapter 3 develops a DSGE model with financial constraints on entrepreneurs and banks, featuring a risk-based bank capital requirement, and discusses the role of Basel II in reinforcing procyclical tendencies of the credit market and the real economy. I study impulse responses of the calibrated model to various shocks. Quantitative results show that the direction and magnitude of cyclical effects arising from Basel II strongly depend on the nature of macroeconomic shocks that hit the economy: only a risk shock can generate noticeable procyclical effect, while the procyclicality under a TFP shock and the countercyclicality under a shock to the marginal efficiency of investment (MEI) are quantitatively insignificant.

In this thesis, I take a theoretical dynamic stochastic general equilibrium (DSGE) approach to investigate optimal aggregate dividend policy. I make the following contribution: 1. I extend the standard DSGE model to incorporate a residual dividend policy, external financing and default and find that simulated optimal aggregate payouts are much more volatile than the observed data when other variables are close to the values observed in the data. 2. I examine the sensitivity of optimal aggregate dividend policy to the level of the representative agent¿s habit motive. My results show that, when the habit motive gets stronger, the volatility of optimal aggregate payouts increases while the volatility of aggregate consumption decreases. This is consistent with the hypothesis that investors use cash payouts from well diversified portfolios to help smooth consumption. 3. I demonstrate that the variability of optimal aggregate payouts is sensitive to capital adjustment costs. My simulated results show that costly frictions from changing the capital base of the firm cause optimal aggregate dividends and real investments to be smooth and share prices to be volatile. This finding is consistent with prior empirical observations. 4. I run simulations that support the hypothesis that optimal aggregate dividend policy is similar when the representative firm is risk averse to when it has capital adjustment costs. In both cases, optimal aggregate dividends volatility is very low. 5. In all calibrated DSGE models, apart from case 4, optimal aggregate payouts are found to be countercyclical. This supports the hypothesis that corporations prefer to hold more free cash flows for potential investment opportunities instead of paying dividends when the economy is booming, but is inconsistent with observed data. Keywords: Dynamic Stochastic General Equilibrium (DSGE), real business cycle, utility function, habits, dividends

This thesis examines the transmission of monetary policy and the effects of persistent cost-push shocks in the presence of high household indebtedness (DTI) and frictions in fixed-rate mortgage (FRM) interest rates. A dynamic stochastic general equilibrium (DSGE) model incorporating housing, household debt, and long-term FRMs is estimated to accomplish this. The key findings can be summarized as follows: (i) A higher DTI leads to a stronger transmission of monetary policy, although this effect is dampened by the degree of interest rate fixation periods. (ii) Cost-push shocks propagates more strongly to inflation when the interest rate fixation periods is longer, resulting in delayed and slightly muted effects on output and consumption compared to adjustable-rate mortgages (ARM). (iii) While stronger responses to inflation help mitigate the cost-push shock, this comes at the expense of a larger output gap but with a slightly faster stabilization of the economy with a somewhat steeper recovery.

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