Effects of Demographic Structure and Tax Policies on Real Estate Prices

This paper compared the effects of demographic structure and tax policies on real estate prices by using an overlapping generation model under two situations: real estate as an investment good and real estate as a consumption good. We found that both economic growth rate and market interest rate play the important roles in both situations. In the former situation with real estate as an investment good, when the economic growth rate is higher (lower) than the market interest rate, the youth dependency ratio, the elderly population ratio, real estate tax rate, and income tax rate are reversely (positively) correlated with real estate prices. In the latter situation with real estate as a consumption good, the effect of the young dependency ratio and income tax rate on real estate prices reveals a positive (negative) correlation when the economic growth rate is higher (lower) than the market interest rate

Examining the Double Dividend Effect of Energy Tax with the Overlapping Generations Model

This paper constructed a two-period overlapping generations (OLG) model to investigate the effects of the energy tax on environmental quality (the first dividend) and output level (the second dividend) to review the double dividend effect of the energy tax. According to the results of comparative static analysis, we found that the energy tax can improve environmental quality but cannot affect the output level. This suggests that the double effect of the energy tax is not supported in the OLG model. This is because an agent can only survive two periods, and need to give consideration to the consumption level of two-generation and the environmental quality of second-generation for pursuing the maximization of lifetime utility, therefore, the agent must maintain consumption (output) stability, and the double dividend effect does not exist. Keywords: Energy Tax, Double Dividend Effect, Overlapping Generations Model JEL Classifications: H23, H24, Q4

The Effects of Anti-Dumping Duties in a Fixed Exchange Rate Regime

In this paper, New Open Economy Macroeconomics with micro-foundation was served as an analytical framework to explore long-term effect of domestic antidumping tax on macroeconomic variables (e.g. consumption, output, the price, and terms of trade) in a fixed exchange rate regime while the dumping by the foreign country into the home country. With theoretical derivation and simulation analysis, we found that when the ratio of export product price selling below its domestic retail price is lower, the antidumping tax is in positive relationship with the domestic consumption, foreign consumption, world consumption, domestic and foreign price indices, while is in negative relationship with domestic output, foreign output, and terms of trade. Besides, the larger ratio of export product price selling below its domestic retail price, the larger the degree of each macroeconomic variable fluctuates

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to a deep linear model. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on the Amazon review dataset and the spam dataset from the ECML/PKDD 2006 discovery challenge.Comment: ijca

Timestamps as Prompts for Geography-Aware Location Recommendation

Location recommendation plays a vital role in improving users' travel experience. The timestamp of the POI to be predicted is of great significance, since a user will go to different places at different times. However, most existing methods either do not use this kind of temporal information, or just implicitly fuse it with other contextual information. In this paper, we revisit the problem of location recommendation and point out that explicitly modeling temporal information is a great help when the model needs to predict not only the next location but also further locations. In addition, state-of-the-art methods do not make effective use of geographic information and suffer from the hard boundary problem when encoding geographic information by gridding. To this end, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed. The temporal prompt is firstly designed to incorporate temporal information of any further check-in. A shifted window mechanism is then devised to augment geographic data for addressing the hard boundary problem. Via extensive comparisons with existing methods and ablation studies on five real-world datasets, we demonstrate the effectiveness and superiority of the proposed method under various settings. Most importantly, our proposed model has the superior ability of interval prediction. In particular, the model can predict the location that a user wants to go to at a certain time while the most recent check-in behavioral data is masked, or it can predict specific future check-in (not just the next one) at a given timestamp

Variational Metric Scaling for Metric-Based Meta-Learning

Metric-based meta-learning has attracted a lot of attention due to its effectiveness and efficiency in few-shot learning. Recent studies show that metric scaling plays a crucial role in the performance of metric-based meta-learning algorithms. However, there still lacks a principled method for learning the metric scaling parameter automatically. In this paper, we recast metric-based meta-learning from a Bayesian perspective and develop a variational metric scaling framework for learning a proper metric scaling parameter. Firstly, we propose a stochastic variational method to learn a single global scaling parameter. To better fit the embedding space to a given data distribution, we extend our method to learn a dimensional scaling vector to transform the embedding space. Furthermore, to learn task-specific embeddings, we generate task-dependent dimensional scaling vectors with amortized variational inference. Our method is end-to-end without any pre-training and can be used as a simple plug-and-play module for existing metric-based meta-algorithms. Experiments on mini-ImageNet show that our methods can be used to consistently improve the performance of existing metric-based meta-algorithms including prototypical networks and TADAM. The source code can be downloaded from https://github.com/jiaxinchen666/variational-scaling.Comment: AAAI202

The Effects of Anti-Dumping Duties in a New Open Economy Macroeconomics Model

This paper presents New Open Economy Macroeconomics as the analytical framework in attempt to integrate the characteristics of imperfect competition market and anti-dumping behavior into a two-country (home country and foreign country) model with micro-foundation. We analyze the long-term effect of implementing antidumping duty in home country on various microeconomic variables (i.e. consumption, output, price, exchange rate, and terms of trade) when foreign country engage in dumping behaviors toward the home country. Theoretical inference and simulation analysis of this paper suggests a positive correlation between antidumping duty and domestic consumption, foreign consumption, world consumption, domestic price index, foreign price index, and exchange rate; whereas a negative correlation between antidumping duty and the domestic output, foreign output, and terms of trade. Moreover, the level of volatility in all macroeconomic variables rises when the ratio of export product price selling below its retail price in home country expands. Keywords: Anti-Dumping Duties, Micro-foundation, New Open Economy Macroeconomics JEL Classifications: F12, F13, F4

Adversarial Deep Network Embedding for Cross-network Node Classification

In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification