Essays in business cycles: housing market, adaptive learning, and credit market imperfections

In this thesis, we focus on the housing sector, which is important to the economy but is under-researched in business cycles analysis. We discuss several housing sector related issues in dynamics stochastic general equilibrium (DSGE) models. To begin with, we conduct a sensitivity analysis using a simple DSGE model with the feature of sticky prices and a fixed housing supply, which is similar with the basic model in Iacoviello (2005) but with representative agents. Then we introduce credit market imperfections in two different ways. The first case is referred to as 'borrowing to invest', in which entrepreneurs take loans and accumulate production housing, which is a factor of production. We observe the financial accelerator (or decelerator) effect since their borrowing is related to output directly. The second case is referred to as 'borrowing to live', in which impatient households take loans to buy housing and gain utility from it. In contrast with the first case, we do not find the financial accelerator (or decelerator) effect, since the borrowing is not directly related to output anymore. First, we add a variable housing supply, thus we can discuss the supply side effect in the housing market, including both the direct effect and the feedback effect. The direct effect is the impact of a housing technology shock, and the feedback effect is the impact of a change in new housing production, which is caused by other shocks. We find, however, that the magnitudes of these two effects are negligible under the standard setting of the housing market that is commonly used in the literature of DSGE model with housing, such as Davis and Heathcote (2005), Iacoviello and Neri (2010). The key assumption in the standard setting is that every household trades housing in a given period. An empirical examination of the U.S. housing sector suggests us to (i) re-construct the housing market and (ii) introduce the feature of time to build to new housing production. After constructing the new setting for the housing market by introducing the probability of trading housing, we find that (i) the steady state ratios from the model are consistent with their empirical targets and (ii) the magnitudes of both the direct effect and the feedback effect are 60 times larger. Furthermore, the feature of time to build, together with the new setting of the housing market, allows us to observe overshooting behaviour on the real house price. Second, we discuss the impact of the assumption of adaptive learning, as we are convinced that the house price bubble is partially contributed by this alternative way of forming expectations. After writing the Nottingham Learning Toolbox1, we find that, given the AR(l) learning model, in which variable is forecasted using its own lagged terms, the adaptive learning mechanism largely amplifies and propagates the effects of a goods sector technology shocks to the economy, and also, enlarges the impact of the time to build feature on the real house price. Furthermore, our sensitivity analysis shows that the values of initial beliefs are important to the mechanism but forecasting errors are not if the constant gain coefficient is small. Then we consider the assumption of heterogeneous expectations. From the impulse response analysis, we find that (i) the adaptive learning mechanism also has amplification and propagation effects to the economy when only a fraction of the population are learning agents; (ii) when two types of agents have equal weights, the impulse responses from heterogeneous expectations are much closer to those from rational expectations than those from adaptive learning; (iii) when rational agents are fully rational, the adaptive learning mechanism has larger amplification and propagation effects on the economy than when rational agents are partially rational. From the sensitivity analysis, We find that fully rational agents always have larger impacts on model variables than partially rational agents. Finally, we introduce credit market imperfections to the housing market, thus the mortgage market subjects to a costly verification problem. Our empirical analysis suggests that, while the default rate is countercyclical, the loan to value ratio is procyclical. Our impulse response analysis shows that, given a positive goods sector technology shock, the default rate is counter cyclical, but the loan to value ratio is also countercyclical. The reason we suppose is that, in our model, credit constrained households have less housing in an economic upturn, thus the volume of loans they receive also decreases, leading to a fall in the loan to value ratio. Moreover, we illustrate that, when the mean of the idiosyncratic shock is time-invariant, we always have a positive relation between the default rate and the loan to value ratio. In order to overcome this co-movement, we show that a time-varying mean is necessary. 1 The Nottingham Learning Toolbox is a series of Matlab files that can solve a general form of DSGE models under adaptive learning and heterogeneous expectations. The toolbox solves the model using the Klein's QZ decomposition method, and facilitates the impulse response analysis. The Cambridge Learning Toolbox provides helpful reference for this toolbox at the initial stage

Rare diseases in developing countries: Insights from China's collaborative network

Rare diseases (RDs) are complex conditions and a worldwide healthcare challenge. The healthcare policymakers in developing countries lack templates from countries at the same level of development. This article introduced and discussed the combination of top‐down strategies and bottom‐up interventions in addressing RDs in a developing country, China, as an example. The government leads the formulation of laws, policies, and guidance to coordinate national resources, while local authorities and nongovernment organisations (NGOs) are responsible for policy localisation and complement policy gaps. This article may inspire other developing countries of improving RD healthcare

Essays in business cycles: housing market, adaptive learning, and credit market imperfections

In this thesis, we focus on the housing sector, which is important to the economy but is under-researched in business cycles analysis. We discuss several housing sector related issues in dynamics stochastic general equilibrium (DSGE) models. To begin with, we conduct a sensitivity analysis using a simple DSGE model with the feature of sticky prices and a fixed housing supply, which is similar with the basic model in Iacoviello (2005) but with representative agents. Then we introduce credit market imperfections in two different ways. The first case is referred to as 'borrowing to invest', in which entrepreneurs take loans and accumulate production housing, which is a factor of production. We observe the financial accelerator (or decelerator) effect since their borrowing is related to output directly. The second case is referred to as 'borrowing to live', in which impatient households take loans to buy housing and gain utility from it. In contrast with the first case, we do not find the financial accelerator (or decelerator) effect, since the borrowing is not directly related to output anymore. First, we add a variable housing supply, thus we can discuss the supply side effect in the housing market, including both the direct effect and the feedback effect. The direct effect is the impact of a housing technology shock, and the feedback effect is the impact of a change in new housing production, which is caused by other shocks. We find, however, that the magnitudes of these two effects are negligible under the standard setting of the housing market that is commonly used in the literature of DSGE model with housing, such as Davis and Heathcote (2005), Iacoviello and Neri (2010). The key assumption in the standard setting is that every household trades housing in a given period. An empirical examination of the U.S. housing sector suggests us to (i) re-construct the housing market and (ii) introduce the feature of time to build to new housing production. After constructing the new setting for the housing market by introducing the probability of trading housing, we find that (i) the steady state ratios from the model are consistent with their empirical targets and (ii) the magnitudes of both the direct effect and the feedback effect are 60 times larger. Furthermore, the feature of time to build, together with the new setting of the housing market, allows us to observe overshooting behaviour on the real house price. Second, we discuss the impact of the assumption of adaptive learning, as we are convinced that the house price bubble is partially contributed by this alternative way of forming expectations. After writing the Nottingham Learning Toolbox1, we find that, given the AR(l) learning model, in which variable is forecasted using its own lagged terms, the adaptive learning mechanism largely amplifies and propagates the effects of a goods sector technology shocks to the economy, and also, enlarges the impact of the time to build feature on the real house price. Furthermore, our sensitivity analysis shows that the values of initial beliefs are important to the mechanism but forecasting errors are not if the constant gain coefficient is small. Then we consider the assumption of heterogeneous expectations. From the impulse response analysis, we find that (i) the adaptive learning mechanism also has amplification and propagation effects to the economy when only a fraction of the population are learning agents; (ii) when two types of agents have equal weights, the impulse responses from heterogeneous expectations are much closer to those from rational expectations than those from adaptive learning; (iii) when rational agents are fully rational, the adaptive learning mechanism has larger amplification and propagation effects on the economy than when rational agents are partially rational. From the sensitivity analysis, We find that fully rational agents always have larger impacts on model variables than partially rational agents. Finally, we introduce credit market imperfections to the housing market, thus the mortgage market subjects to a costly verification problem. Our empirical analysis suggests that, while the default rate is countercyclical, the loan to value ratio is procyclical. Our impulse response analysis shows that, given a positive goods sector technology shock, the default rate is counter cyclical, but the loan to value ratio is also countercyclical. The reason we suppose is that, in our model, credit constrained households have less housing in an economic upturn, thus the volume of loans they receive also decreases, leading to a fall in the loan to value ratio. Moreover, we illustrate that, when the mean of the idiosyncratic shock is time-invariant, we always have a positive relation between the default rate and the loan to value ratio. In order to overcome this co-movement, we show that a time-varying mean is necessary. 1 The Nottingham Learning Toolbox is a series of Matlab files that can solve a general form of DSGE models under adaptive learning and heterogeneous expectations. The toolbox solves the model using the Klein's QZ decomposition method, and facilitates the impulse response analysis. The Cambridge Learning Toolbox provides helpful reference for this toolbox at the initial stage

Acetylshikonin Sensitizes Hepatocellular Carcinoma Cells to Apoptosis through ROS-Mediated Caspase Activation

This work is licensed under a Creative Commons Attribution 4.0 International License.The tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has shown strong and explicit cancer cell-selectivity, which results in little toxicity toward normal tissues, and has been recognized as a potential, relatively safe anticancer agent. However, several cancers are resistant to the apoptosis induced by TRAIL. A recent study found that shikonin b (alkannin, 5,8-dihydroxy-2-[(1S)-1-hydroxy-4-methylpent-3-en-1-yl]naphthalene-1,4-dione) might induce apoptosis in TRAIL-resistant cholangiocarcinoma cells through reactive oxygen species (ROS)-mediated caspases activation. However, the strong cytotoxic activity has limited its potential as an anticancer drug. Thus, the current study intends to discover novel shikonin derivatives which can sensitize the liver cancer cell to TRAIL-induced apoptosis while exhibiting little toxicity toward the normal hepatic cell. The trypan blue exclusion assay, western blot assay, 4′,6-diamidino-2-phenylindole (DAPI) staining and the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay as well as the ‘comet’ assay, were used to study the underlying mechanisms of cell death and to search for any mechanisms of an enhancement of TRAIL-mediated apoptosis in the presence of ASH. Herein, we demonstrated that non-cytotoxic doses of acetylshikonin (ASH), one of the shikonin derivatives, in combination with TRAIL, could promote apoptosis in HepG2 cells. Further studies showed that application of ASH in a non-cytotoxic dose (2.5 μM) could increase intracellular ROS production and induce DNA damage, which might trigger a cell intrinsic apoptosis pathway in the TRAIL-resistant HepG2 cell. Combination treatment with a non-cytotoxic dose of ASH and TRAIL activated caspase and increased the cleavage of PARP-1 in the HepG2 cell. However, when intracellular ROS production was suppressed by N-acetyl-l-cysteine (NAC), the synergistic effects of ASH and TRAIL on hepatocellular carcinoma (HCC) cell apoptosis was abolished. Furthermore, NAC could alleviate p53 and the p53 upregulated modulator of apoptosis (PUMA) expression induced by TRAIL and ASH. Small (or short) interfering RNA (siRNA) targeting PUMA or p53 significantly reversed ASH-mediated sensitization to TRAIL-induced apoptosis. In addition, Bax gene deficiency also abolished ASH-induced TRAIL sensitization. An orthotopical HCC implantation mice model further confirmed that co-treated ASH overcomes TRAIL resistance in HCC cells without exhibiting potent toxicity in vivo. In conclusion, the above data suggested that ROS could induce DNA damage and activating p53/PUMA/Bax signaling, and thus, this resulted in the permeabilization of mitochondrial outer membrane and activating caspases as well as sensitizing the HCC cell to apoptosis induced by TRAIL and ASH treatment

Exonuclease III protection assay with FRET probe for detecting DNA-binding proteins

We describe a new method for the assay of sequence-specific DNA-binding proteins in this paper. In this method, the sensitive fluorescence resonance energy transfer (FRET) technology is combined with the common DNA footprinting assay in order to develop a simple, rapid and high-throughput approach for quantitatively detecting the sequence-specific DNA-binding proteins. We named this method as exonuclease III (ExoIII) protection assay with FRET probe. The FRET probe used in this assay was a duplex DNA which was designed to contain one FRET pair in the center and two flanking protein-binding sites. During protein detection, if a target protein exists, it will bind to the two protein-binding sites of the FRET probe and thus protect the FRET pair from ExoIII digestion, resulting in high FRET. However, if the target protein does not exist, the FRET pair on the naked FRET probe will be degraded by ExoIII, resulting in low FRET. Three kinds of recombinant transcription factors including NF-κB, SP1 and p50, and the target protein of NF-κB in HeLa cell nuclear extracts, were successfully detected by the assay. This assay can be extensively used in biomedical research targeted at DNA-binding proteins

Salt Compartmentation and Antioxidant Defense in Roots and Leaves of Two Non-Salt Secretor Mangroves under Salt Stress

The effects of increasing NaCl (100–400 mM) on cellular salt distribution, antioxidant enzymes, and the relevance to reactive oxygen species (ROS) homeostasis were investigated in 1-year-old seedlings of two non-salt secretor mangroves, Kandelia obovata and Bruguiera gymnorhiza. K. obovata accumulated less Na+ and Cl− in root cells and leaf compartments under 400 mM NaCl compared to B. gymnorhiza. However, B. gymnorhiza leaves are notable for preferential accumulation of salt ions in epidermal vacuoles relative to mesophyll vacuoles. Both mangroves upregulated antioxidant enzymes in ASC-GSH cycle to scavenge the salt-elicited ROS in roots and leaves but with different patterns. K. obovata rapidly initiated antioxidant defense to reduce ROS at an early stage of salt stress, whereas B. gymnorhiza maintained a high capacity to detoxify ROS at high saline. Collectively, our results suggest that salinized plants of the two mangroves maintained ROS homeostasis through (i) ROS scavenging by antioxidant enzymes and (ii) limiting ROS production by protective salt compartmentation. In the latter case, an efficient salt exclusion is favorable for K. obovata to reduce the formation of ROS in roots and leaves, while the effective vacuolar salt compartmentation benefited B. gymnorhiza leaves to avoid excessive ROS production in a longer term of increasing salinity