Equilibria under monetary and fiscal policy interactions in a portfolio choice model

This paper studies how the presence of income-taxes changes the properties of general equilibrium models with monetary and fiscal policy interactions. It is found that from a global perspective, the only policy regime that leads to a unique equilibrium is one where fiscal policy is active, i.e. the fiscal theory of the price level regime in Sims (1994). From a local perspective, there are three regimes. In particular, and relative to the previous literature following Leeper (1991), a new third regime exists where a passive fiscal rule combined with a passive monetary rule can still deliver determinacy where the same area of the parameter space would lead to multiple solutions if taxes were lump sum. To obtain the size of the new regime, the paper characterizes analytically the extent to which tax cuts are self-financing and how the distortionary tax Laffer curve looks near the steady state. In the new regime, monetary and fiscal backstops are brought into play so as to rule out off equilibrium dynamics, and inflation can temporarily increase in order to increase seigniorage revenues. With this flexibility, the monetary policy is consistent with the real debt remaining bounded, and the arithmetic that follows is monetarist and unpleasant in the sense of Sargent and Wallace (1981).     

Optimal monetary policy under incomplete markets and aggregate uncertainty: A long-run perspective

This paper examines the role of monetary policy in an environment with aggregate risk and incomplete markets. In a two-period overlapping-generations model with aggregate uncertainty, optimal monetary policy attains the ex-ante Pareto optimal allocation. This policy aims to stabilize the savings rate in the economy by changing real returns of nominal bonds via variation in expected inflation. Optimal expected inflation is procylical and on average higher than without uncertainty. Simple inflation targeting rules closely approximate the optimal monetary policy.     

A theory of stochastic choice under uncertainty

In this paper we propose a characterization of stochastic choice under risk and under uncertainty. We presume that decision makers’ actual choices are governed by randomly selected states of mind, and study the representation of decision makers’ perceptions of the stochastic process underlying the selection of their state of mind. The connections of this work to the literatures on random choice, choice behavior when preference are incomplete; choice of menus; and grades of indecisiveness are also discussed.     

A model for irreversible investment with construction and revenue uncertainty

This paper presents a model of investment in projects that are characterized by uncertainty over both the construction costs and revenues. Both processes are modeled as spectrally negative Lévy jump-diffusions. The optimal stopping problem that determines the value of the project is solved under fairly general assumptions. It is found that the current value of the benefit-to-cost ratio (BCR) decreases in the frequency of negative shocks to the construction process. This implies that the cost overruns that can be expected if one ignores such shocks are increasing in their frequency. Based on calibrated data, the model is applied to the proposed construction of high-speed rail in the UK and it is found that its economic case cannot currently be made and is unlikely to be met at any time in the next decade. In addition it is found that ignoring construction uncertainty leads to a substantial probability of an erroneous decision being taken.     

Belgian economic policy uncertainty index: Improvement through text mining

Recently, the literature has measured economic policy uncertainty using news references, resulting in the frequently-mentioned ‘Economic Policy Uncertainty index’ (EPU). In the original setup, a news article is assumed to address policy uncertainty if it contains certain predefined keywords. We argue that the original setup is prone to measurement error, and propose an alternative methodology using text mining techniques. We compare the original method to modality annotation and support vector machines (SVM) classification in order to create an EPU index for Belgium. Validation on an out-of-sample test set speaks in favour of using an SVM classification model for constructing a news-based policy uncertainty indicator. The indicators are then used to forecast 10 macroeconomic and financial variables. The original method of measuring EPU does not have predictive power for any of these 10 variables. The SVM indicator has a higher predictive power and, notably, changes in the level of policy uncertainty during tumultuous periods of high uncertainty and risk can predict changes in the sovereign bond yield and spread, the credit default swap spread, and consumer confidence.     

Designing an integrated humanitarian logistics network for the preparedness and response phases under uncertainty

In the current paper, a model of possibilistic location-allocation is proposed for designing a multi-period bi-objective humanitarian logistics network that pursues a reduction in the total cost and maximizes the total network coverage. The model considers the inventory management of perishable relief items and the flow of affected and wounded people in pre-disaster and post-disaster phases, simultaneously. Moreover, wounded people are categorized according to the injury severity. The uncertainty associated with key parameters is also addressed. To handle the uncertainty, a fuzzy chance-constrained programming method originated in the Me measure is used. The purpose of this measure is to prevent the extreme attitudes of the decision maker by considering the combination of possibility and necessity measures and using the optimistic-pessimistic parameter. This paper presents a solution procedure derived from a fuzzy interactive programming approach and two meta-heuristic algorithms, imperialist competitive algorithm and invasive weed optimization, so as to solve the study model. As for the validation of the proposed model and solution procedures, a number of test problems have been generated. A real-life case study is also implemented to instantiate whether the proposed model is applicable or not.     

Economic policy uncertainty and capital structure choice: Evidence from China

This paper studies how economic policy uncertainty affects corporate capital structure for Chinese listed firms from 2003 to 2013. We show that as the degree of economic policy uncertainty increases, firms tend to lower their leverage ratios. However, firms that are from regions with lower degrees of marketization, are state-owned or have prior bank-firm relationships mitigate the negative effect of policy uncertainty. Moreover, we provide consistent evidence that this negative effect is sourced from the deterioration of the external financing environment. We also find that firms adjust their financing structures by using more trade credit when economic policy uncertainty increases. Our results are robust to sample selection, data frequency, model specification and endogeneity.     

Additive representation for preferences over menus in finite choice settings

This paper obtains an additive representation for preferences over subsets of a finite set relaxing the two substantive axioms in Kreps (1979) flexibility theorem. The result implies that the lottery structure and assumptions employed by Dekel, Lipman and Rustichini (2001) to identify the subjective state-space do not introduce extraneous restrictions on deterministic choice behavior. This property does not necessarily hold when additional axioms are imposed.     

Economic policy uncertainty effects for forecasting future real economic activity

Recently introduced measures for economic policy uncertainty (EPU), included in the data from 1997 to 2016, have a role in forecasting out-of-sample values for future real economic activity for both the euro area and UK economies. The inclusion of EPU measures, either for the US, the UK or for overall European economies, improves the forecasting ability of models based on standard financial market information, especially for the period before the 2008 global crisis. However, during and after the crisis period, the slope of the yield curve and excess stock market returns improves the out-of-sample forecast performance the most compared to an AR-benchmark model. Hence, the EPU information is important in times of normal business cycles, but might contain similar information components to financial market return variables during turbulent crisis periods in the financial markets and in the real economy.     

The macroeconomic effects of uncertainty shocks: The role of the financial channel

This paper studies the macroeconomic effects of uncertainty shocks with an emphasis on the interaction between elevated uncertainty and credit market conditions when the economy is in different regimes (recessions vs. non-recessions). We use a smooth-transition factor-augmented vector autoregression (ST-FAVAR) and a large monthly panel of U.S. macroeconomic and financial indicators in our estimation. Our findings are twofold. First, while an unanticipated increase in uncertainty has adverse effects on the real economy and financial markets, the effects are quantitatively larger during recessions. Second, the financial channel is important in the transmission of uncertainty shocks, with a greater role during recessions and in the short run.     

期房市场价格稳定效应分析及期房政策选择

回顾了商品房预售制度的起源及其在我国的发展,随后从理论层面分析了现房市场价格失效的原因及期房市场对现房市场价格稳定效应的作用机理.最后,结合其它国家和地区期房政策的实践经验,从调整和完善期房市场功能的角度,提出了弱化直接融资功能、发展间接融资功能、保护营销功能、发挥价格稳定功能等政策建议.     

Investing in Research and Education versus Commodity Programs: Implications for Agricultural Productivity

The long-term impact of research, education, and various government support programs on U.S. agricultural productivity was analyzed using an error correction model. Results indicate that the proposed reduction in commodity program expenditures (e.g. 1996 Farm Bill) is unlikely to reduce agricultural productivity. Results suggest that shifting public funds from commodity programs to education and research would raise U.S. agricultural productivity. Our estimates of long-term rates of return to public research are lower than those from most previous, perhaps due to our improved model specification, but are high enough to justify continued public investments to raise productivity.     

Labour adjustment costs: Estimation of a dynamic discrete choice model using panel data for Greek manufacturing firms

In this paper we estimate a dynamic structural model of employment at firm level. Our dataset consists of a balanced panel of 2790 Greek manufacturing firms. The empirical evidence of this dataset stresses three important stylized facts: (a) there are periods in which firms decide not to change their labour input, (b) there are periods of large employment changes (lumpy nature of labour adjustment) and (c) the commonality is employment spikes to be followed by smooth and low employment growth periods. Following Cooper and Haltiwanger [Cooper, R.W. and Haltiwanger, J. “On the Nature of Capital Adjustment Costs”, Review of Economic Studies, 2006; 73(3); 611–633], we consider a dynamic discrete choice model of a general specification of adjustment costs including convex, non-convex and “disruption of production” components. We use a method of simulated moments procedure to estimate the structural parameters. Our results indicate considerable fixed costs in the Greek employment adjustment.     

Should tariff-rate quotas mimic quotas?: Implications for trade liberalization under a supply management policy

This article compares price-equivalent import tariffs and quotas when domestic production is controlled by a marketing board with the power to restrict domestic supply through production quotas. Canada's dairy industry is supply-managed and protected by TRQs to achieve a domestic price target. TRQs are currently set to mimic the import quotas they replaced. However, they could be set to mimic tariffs instead. We provide welfare rankings between (domestic) price-equivalent quotas and tariffs under various assumptions regarding the powers of the marketing board to shed new light on liberalization in the Canadian dairy industry. When the marketing board is allowed to export, quotas can never be welfare-inferior to price-equivalent tariffs when transport costs between the two markets are insignificant. Import licensing methods have important implications for the ranking of the two trade instruments and the ranges of feasible domestic prices. If the marketing board controlled all import licenses and there was no rule preventing it from sleeping on part or all of its import licenses, the quota regime would support a small range of high feasible prices. In contrast, when the quota is a minimum access commitment, there are high prices under the tariff regime that are not feasible under the quota regime.     

Linking words in economic discourse: Implications for macroeconomic forecasts

This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.     

Long-term wealth growth portfolio allocation under parameter uncertainty: A non-conservative robust approach

The Kelly portfolio, which is documented to have the highest wealth growth rate of any other portfolio in the long run, has highly risky and unstable performance in the short term. This paper offers a hybrid approach to address this problem by integrating the concept of ridge regression and shrinkage estimation into a robustly modified Kelly portfolio. The proposed approach is a two-stage optimization process that not only takes into account the effect of estimation error but also solves the notoriously conservative problem introduced by the robust optimization method. By extending the worst-case scenarios considered by the robust Kelly portfolio, our approach significantly improves its out-of-sample performance without compromising risk reduction. In an extensive out-of-sample analysis with simulated and empirical data sets, we also characterize the impacts of the robustness level and the length of the rolling window on the final result. Moreover, we conduct a comparative study to confirm the validity of the proposed approach, and our model allows the investor to have a better risk-return trade-off than other traditional models.     

Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility

This paper constructs an aligned global economic policy uncertainty (GEPU) index based on a modified machine learning approach. We find that the aligned GEPU index is an informative predictor for forecasting crude oil market volatility both in- and out-of-sample. Compared to general GEPU indices without supervised learning, well-recognized economic variables, and other popular uncertainty indicators, the aligned GEPU index is rather powerful and can provide preponderant or complementary information. The trading strategy based on the aligned GEPU index can also generate sizable economic gains. The statistical source of the aligned GEPU index’s predictive power is that it can learn both the magnitude and sign of national EPU variables’ predictive ability and thus yields reasonable and informative loadings. On the other hand, the economic driving force probably stems from the ability for forecasting the shocks of oil-related fundamentals.     

A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty

Disaster response operations revolve around uncertainties. While uncertainties arising due to randomness can be avoided for post-disaster location problem, those arising because of impreciseness may persist long after the disaster's occurrence. Despite the uncertainties and lack of sufficient information about the extent of the damage, disaster response facilities must be established quickly after the occurrence of the disaster. Moreover, the decisions of whether to open, where to locate, and when to open disaster response facilities are based on the amount and quality of information available during the decision-making period. To address these issues, we develop a multi-objective location-allocation model for relief supply and distribution that accounts for the imprecise and time-varying nature of different parameters and time-varying coverage, while also accommodating the subjective attributes necessary to enable establishment and operation of the temporary logistics hubs (TLHs). A credibility-based fuzzy chance-constrained programming model is employed to account for the impreciseness inherent in predicting parameter values during disaster response. The results show where, when, and how many TLHs to open and how to allocate relief supplies. Meanwhile, the sensitivity analysis provides a broader understanding of the impact of limiting the number of TLHs as well as the confidence level and the spread of the symmetric triangular fuzzy numbers on the attainment of the model objectives.     

Minimisation of uncertainty in decision-making processes using optimised probabilistic Fuzzy Cognitive Maps: A case study for a rural sector

Several studies have focused on methods of increasing system and uncertainty knowledge for socio-economic and environmental policies; however, the nonlinearity and dynamism of real world increase the gap between uncertainty depiction and its evaluation in policy strategies. This work attempts to implement a methodology that is able to minimise uncertainty in decision support tools related to rural planning and management. Fuzzy Cognitive Maps, the Dempster–Shafer theory and nonlinear optimisation were applied to achieve the above-mentioned goal. The method was tested to describe suitable policies and intervention strategies to address the effects of the recent economic crisis in the agricultural sector.