Forecast-error-based estimation of forecast uncertainty when the horizon is increased

Past forecast errors are employed frequently in the estimation of the unconditional forecast uncertainty, and several institutions have increased their forecast horizons in recent times. This work addresses the question of how forecast-error-based estimation can be performed if there are very few errors available for the new forecast horizons. It extends the results of Knüppel (2014) in order to relax the condition on the data structure that is required for the SUR estimator to be independent of unknown quantities. It turns out that the SUR estimator of the forecast uncertainty, which estimates the forecast uncertainty for all horizons jointly, tends to deliver large efficiency gains relative to the OLS estimator (i.e., the sample mean of the squared forecast errors for each individual horizon) in the case of increased forecast horizons. The SUR estimator is applied to the forecast errors of the Bank of England, the US Survey of Professional Forecasters, and the FOMC.     

The effects of anticipated goal‐inconsistent behavior on present goal choices

Prior work has examined how, in the pursuit of long‐term goals, past goal behavior influences present goal choices. Instead, the present work focuses on how anticipating future goal behavior, specifically future goal‐inconsistent behavior, influences present goal choices. For example, how anticipating overspending on an upcoming vacation influences current spending behavior. The authors propose that the effect of anticipated goal‐inconsistent behavior on present goal choice is moderated by the perceived changeability of the future behavior. When future goal‐inconsistent behavior is perceived as changeable, consumers tend to imagine it away, and it has no systematic effect on present goal choices. However, when future goal‐inconsistent behavior is perceived as unchangeable, consumers accept it as a matter of fact, and systematic effects occur. Specifically, some consumers not only fail to buffer against future goal‐inconsistent behavior's negative consequences, but tend to exacerbate those consequences by increasing their goal‐inconsistent behavior in the present. Four studies examine this surprising behavior, using an individual difference (the response‐to‐failure scale) to identify when and for whom it occurs. The studies demonstrate the role of perceived changeability using various manipulations across multiple critical goal domains such as spending, eating, and academics.     

Semiparametric time series regression modeling with a diverging number of parameters

Variable selection and error structure determination of a partially linear model with time series errors are important issues. In this paper, we investigate the regression coefficient and autoregressive order shrinkage and selection via the smoothly clipped absolute deviation penalty for a partially linear model with a divergent number of covariates and finite order autoregressive time series errors. Both consistency and asymptotic normality of the proposed penalized estimators are derived. The oracle property of the resultant estimators is proved. Simulation studies are carried out to assess the finite‐sample performance of the proposed procedure. A real data analysis is made to illustrate the usefulness of the proposed procedure as well.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

Medicaid and long‐term care: The effects of penalizing strategic asset transfers

Medicaid provides a critical source of insurance for long‐term care, and individuals may strategically offload assets (typically to children) to meet the means‐tested eligibility requirement. In this article, we quantify the extent of such behavior using variation in the penalty for improper parent‐to‐child transfers induced by the Deficit Reduction Act of 2005. We estimate difference‐in‐differences models based on the hypothesis that only individuals with high levels of nursing home risk (high risk) will alter transfers because of the Act. We find that over a 2‐year horizon, high‐risk individuals reduced transfers to children on the extensive margin by 11 percent and that the average total amount of transfers decreased by $4,860. The results hold only for coupled respondents. We also conduct a triple‐differences analysis to examine heterogeneity with financial literacy and find that even those with a low level of financial literacy responded to the penalty.     

Tourism,terrorism and political violence in Tunisia: Evidence from Markov-switching models

This study investigates the impact of terrorist attacks and political violence on the number of tourist arrivals and overnight stays in Tunisia. The dataset employed consists of monthly data that covers the period from January 2000 to September 2016, which includes several political and terrorist attacks in Tunisia and the region. Empirically, we investigate the true data generating process (DGP) of these two proxies of tourism activity by accounting for four statistical properties that characterize these series: (1) seasonality, (2) unit roots, (3) breaks, and (4) long memory behavior.Our empirical findings show strong evidence of stationarity, five breaks in the tourist arrival time series and spurious long memory behavior. By estimating a 3-state Markov switching model consisting of the mean, trend, and variance, we find that the Tunisian Jasmine revolution and two recent terrorist attacks, one at the Bardo National Museum on March 18, 2015 and the other at the tourist resort at Port El Kantaoui, Sousse on June 26, 2015, played an important role in influencing the tourism activity of the country. Our empirical findings show also that local shocks have a more important impact than international shocks in influencing tourism activity. Interestingly, we find that the effects of terrorist shocks have a long duration compared to political violence shocks. Several security, marketing, and economic policies have been proposed and discussed in the paper.     

Volatility of volatility is (also) rough

Using high-frequency data for major volatility indexes, we compute the volatility of volatility and show that its logarithm follows a fractional Brownian motion with Hurst parameter smaller than 1/2 thereby extending to the volatility asset class the recent findings obtained for the equity index markets. The results confirm that the volatility of volatility is a rough process and it possesses the long memory property. We also show that the correlation between the volatility and the volatility of volatility is positive, consistent with observations in the volatility option market. Lastly, a robustness check using volatility futures confirms the findings.     

双选信道下一种基于混沌交织的FBMC系统

高速移动环境下，无线信道具有时频双选性衰落的特性，使得滤波器组多载波(Filter Bank Multi-carrier，FBMC)系统产生长突发差错。将一种基于Baker映射的混沌交织算法应用在滤波器组多载波系统中，根据混沌密钥对发送数据进行分块和重新排列，按照Baker映射规则完成数据交织。此方法可以将长突发差错变为单突发差错，结合卷积编码能有效地纠正双选信道产生的长突发差错。仿真结果表明，在双选择信道中，基于混沌交织的滤波器组多载波系统误比特率性能优于传统基于块交织的滤波器组多载波系统。     

Some comments on the Sraffian Supermultiplier approach to growth and distribution

Abstract

The article discusses the Sraffian Supermultiplier (SSM) approach to growth and distribution. It makes 5 points. First, in the short run, the role of autonomous expenditure can be appreciated within a standard post Keynesian framework (Kaleckian, Kaldorian, Robinsonian, etc.). Second, and related to the first, the SSM model is a model of the long run and has to be evaluated as such. Third, in the long run, one way that capacity adjusts to demand is through an endogenous adjustment of the rate of utilization. Fourth, the SSM model is a peculiar way to reach what Garegnani called the “second Keynesian position.” Although, it respects the letter of the “Keynesian hypothesis,” it makes investment quasi-endogenous and subjects it to the growth of autonomous expenditure. Fifth, in the long run it is unlikely that “autonomous expenditure” is really autonomous. From a stock-flow consistent point of view this implies unrealistic adjustments after periods of changes in stock-flow ratios. Moreover, if we were to take this kind of adjustment at face value, there would be no space for Minskyan financial cycles. This also creates serious problems for the empirical validation of the model.     

The asymptotic expansion of the regular discretization error of Itô integrals

We study an Edgeworth‐type refinement of the central limit theorem for the discretization error of Itô integrals. Toward this end, we introduce a new approach, based on the anticipating Itô formula. This alternative technique allows us to compute explicitly the terms of the corresponding expansion formula. Two applications to finance are given; the asymptotics of discrete hedging error under the Black–Scholes model and the difference between continuously and discretely monitored variance swap payoffs under stochastic volatility models.