The Effects Of Government Policies On (un)employment In A Search Model

This thesis studies the effects of fiscal and labour market policies on the labour market activities. The study is based on a general equilibrium framework. The models considered in this thesis are built on the recent advances of growth models which incorporate labour market search/match activities. This study extends the previous models to address several important empirical issues.;In Chapter 1, an empirical study using multivariate vector autoregression (VAR) models shows that, in postwar U.S., employment and hours worked per worker respond differently to a temporary shock in government consumption. The shock raises hours worked per worker and reduces employment. The existing standard neoclassical growth models have mainly focused on total hours effects of a government spending shock, rather than separate employment and hours effects. In this chapter, we analyze the dynamic behaviour of employment and hours worked per worker in a stochastic general equilibrium model with a matching mechanism between vacancies and unemployed workers. The model is estimated for U.S. using the Generalized Methods of Moments (GMM) estimation technique and the responses of employment and hours worked per worker are qualitatively similar to those in the VAR models. An increase in government spending raises hours worked per worker, and crowds out private consumption due to a negative wealth effect. On the path converging towards the steady state, private consumption is below its long run average and increasing, which implies that the interest rate is above its long run average and declining. The higher interest rates lower the capital value of a hired worker to the firm, causing a reduction of job openings and consequently a decrease in employment.;Chapter 2 involves a study of quantifying the potential causes of the rising unemployment rate in Canada over the past three decades based on a general equilibrium catch model. Several policy variables which are considered to be influential to unemployment are incorporated in the model. They are labour income taxes, capital income taxes, payroll taxes, unemployment benefits, and advance notice and several payment regulations. In order to evaluate the relative importance of changes in government policies, we also consider the impact of changes in productivity growth. We find that fiscal and labour market policies have only a limited effect in the model economy.;Chapter 3 compares the cost-effectiveness of the Investment Tax Credit (ITC) program and the Payroll Tax Credit (PTC) program. The ITC increases employment by increasing marginal labour productivity, while the PTC increases employment by directly reducing labour cost. This chapter compares the consequences of an introduction of the Investment Tax Credit (ITC) program or the Payroll Tax Credit (PTC) program based on a general equilibrium search model. The results show that the ITC program leads to more employment opportunities than the PTC program. This finding is robust according to sensitivity analyses

Market Valuation and Risk Assessment of Canadian Banks

The authors apply the asset-valuation model developed by Rabinovitch (1989) to six publicly traded Canadian banks over the period 1982–2002. The model is an extension of the Merton (1977a) option-pricing model with the incorporation of stochastic interest rates. The authors introduce the Z-score, a measure of distance-to-default, which can be a useful tool for regulators in assessing the risk of bank failures. The Z-scores, overall, suggest that Canadian banks are far from the point of default. The authors also find that both the market valuation of the bank assets and the Z-score of the Canadian banks demonstrate similar regime shifts in the late 1990s, which may be related to regulatory changes during the 1990s.Financial institutions

Unified linear time-invariant model predictive control for strong nonlinear chaotic systems

It is well known that an alone linear controller is difficult to control a chaotic system, because intensive nonlinearities exist in such system. Meanwhile, depending closely on a precise mathematical modeling of the system and high computational complexity, model predictive control has its inherent drawback in controlling nonlinear systems. In this paper, a unified linear time-invariant model predictive control for intensive nonlinear chaotic systems is presented. The presented model predictive control algorithm is based on an extended state observer, and the precise mathematical modeling is not required. Through this method, not only the required coefficient matrix of impulse response can be derived analytically, but also the future output prediction is explicitly calculated by only using the current output sample. Therefore, the computational complexity can be reduced sufficiently. The merits of this method include, the Diophantine equation needing no calculation, and independence of precise mathematical modeling. According to the variation of the cost function, the order of the controller can be reduced, and the system stability is enhanced. Finally, numerical simulations of three kinds of chaotic systems confirm the effectiveness of the proposed method

Temperature-dependent Exciton Dynamics of Superacid Treatment in Monolayers of the Metal Dichalcogenide MoS2

To improve optoelectronic semiconductor materials, one of the most efficient research areas is the two-dimensional (2D) transition-metal dichalocogenides (TMDCs). It has been shown that organic nonoxidizing superacid bis(trifluoromethane)sulfonamide (TFSI) treatment of molybdenum disulfide (MoS2) monolayer could uniformly enhance its photoluminescence by more than two orders of magnitude and also extend the lifetime of excitons. This could greatly improve the efficiency of the solar energy usage, but the mechanism behind it has not been fully understood. Extreme low temperatures (approximately 7K), which slow the surface exciton mobility, were applied to investigate the changes of treated MoS2 monolayer surfaces. This approach also requires cover slip caps to protect samples from degrading in the vacuum and low temperature environment. Our results show that the defect stages of the MoS2 surface still occur at low temperatures which differs from the previous mechanism proposed. To determine the true mechanism of superacid treatment of MoS2 monolayer we will need further experiments

Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension and generation tasks. We have the following main findings. First, for the zero-shot setting, instructed LLMs are very competitive on code comprehension and generation tasks and sometimes even better than small SOTA models specifically fine-tuned on each downstream task. We also find that larger instructed LLMs are not always better on code-related tasks. Second, for the few-shot setting, we find that adding demonstration examples substantially helps instructed LLMs perform better on most code comprehension and generation tasks; however, the examples would sometimes induce unstable or even worse performance. Furthermore, we find widely-used BM25-based shot selection strategy significantly outperforms the basic random selection or fixed selection only on generation problems. Third, for the fine-tuning setting, we find that fine-tuning could further improve the model performance on downstream code comprehension and generation tasks compared to the zero-shot/one-shot performance. In addition, after being fine-tuned on the same downstream task dataset, instructed LLMs outperform both the small SOTA models and similar-scaled LLMs without instruction tuning. Based on our findings, we further present practical implications on model and usage recommendation, performance and cost trade-offs, and future direction

FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design

Robust network design, which aims to guarantee network availability under various failure scenarios while optimizing performance/cost objectives, has received significant attention. Existing approaches often rely on model-based mixed-integer optimization that is hard to scale or employ deep learning to solve specific engineering problems yet with limited generalizability. In this paper, we show that failure evaluation provides a common kernel to improve the tractability and scalability of existing solutions. By providing a neural network function approximation of this common kernel using graph attention networks, we develop a unified learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design. FERN represents rich problem inputs as a graph and captures both local and global views by attentively performing feature extraction from the graph. It enables a broad range of robust network design problems, including robust network validation, network upgrade optimization, and fault-tolerant traffic engineering that are discussed in this paper, to be recasted with respect to the common kernel and thus computed efficiently using neural networks and over a small set of critical failure scenarios. Extensive experiments on real-world network topologies show that FERN can efficiently and accurately identify key failure scenarios for both OSPF and optimal routing scheme, and generalizes well to different topologies and input traffic patterns. It can speed up multiple robust network design problems by more than 80x, 200x, 10x, respectively with negligible performance gap

Low-energy spin excitations in optimally doped CaFe0.88_{0.88}Co0.12_{0.12}AsF superconductor studied with inelastic neutron scattering

There are few inelastic neutron scattering (INS) reports on the superconducting single crystals of FeAs-1111 system, even though it was first discovered in 2008, due to the extreme difficulty in large single crystal growth. In this paper, we have studied the low-energy spin excitations in the optimally electron-doped CaFe$_{0.88}$Co$_{0.12}$AsF single crystals with $T_\mathrm{c}$ = 21 K by INS. The resonance energy of the superconducting spin resonant mode with $E_\mathrm{r}$ = 12 meV amounts to 6.6 $k_\mathrm{B}$$T_\mathrm{c}$, which constitutes the largest $E_\mathrm{r}$/$k_\mathrm{B}$$T_\mathrm{c}$ ratio among iron-based superconductors reported to date. The large ratio implies a strong coupling between conduction electrons and magnetic excitations in CaFe$_{0.88}$Co$_{0.12}$AsF. The resonance possesses a magnonlike upward dispersion along transverse direction due to the anisotropy of spin-spin correlation length within $ab$ plane in the normal-state, which points to a spin fluctuation mediated sign-reversed ${s}\mathbf\pm$ wave pairing in CaFe$_{0.88}$Co$_{0.12}$AsF