Mixed random forest,cointegration, and forecasting gasoline prices

One of the most successful forecasting machine learning (ML) procedures is random forest (RF). In this paper, we propose a new mixed RF approach for modeling departures from linearity that helps identify (i) explanatory variables with nonlinear impacts, (ii) threshold values, and (iii) the closest parametric approximation. The methodology is applied to weekly forecasts of gasoline prices, cointegrated with international oil prices and exchange rates. Recent specifications for nonlinear error correction (NEC) models include threshold autoregressive models (TAR) and double-threshold smooth transition autoregressive (STAR) models. We propose a new mixed RF model specification strategy and apply it to the determinants of weekly prices of the Spanish gasoline market from 2010 to 2019. In particular, the mixed RF is able to identify nonlinearities in both the error correction term and the rate of change of oil prices. It provides the best weekly gasoline price forecasting performance and supports the logistic error correction model (ECM) approximation.     

偏差修正的预白化HAC法在平稳过程伪回归中的应用

在传统预白化HAC法存在有限样本偏差的基础上,提出将自回归参数偏差修正法和残差调整法来减少预白化HAC法的偏差,从而降低相互独立的平稳过程之间发生伪回归的概率。通过一系列的蒙特卡罗模拟表明：第一修正的预白化HAC法确实减少了伪回归概率,且自回归参数偏差修正法减少的幅度要比残差调整法要大得多;第二相对于同方差情形而言,存在GARCH类异方差的回归中预白化HAC法具有更低的伪回归概率;第三当数据过程是AR(2)过程时,在持久性相同的情况下预白化HAC法的伪回归概率要低于相应的AR(1)数据过程。但在高于2阶的自回归数据过程的回归中,残差调整的预白化HAC的伪回归概率具有优势。在样本容量较大(T≥500)时自回归参数修正的预白化HAC法的伪回归概率很接近检验水平,但残差调整的预白化HAC法具有微弱的向下检验水平扭曲.     

Bias corrections for exponentially transformed forecasts: Are they worth the effort?

In many economic applications, it is convenient to model and forecast a variable of interest in logs rather than in levels. However, the reverse transformation from log forecasts to levels introduces a bias. This paper compares different bias correction methods for such transformations of log series which follow a linear process with various types of error distributions. Based on Monte Carlo simulations and an empirical study of realized volatilities, we find no choice of correction method that is uniformly best. We recommend the use of the variance-based correction, either by itself or as part of a hybrid procedure where one first decides (using a pretest) whether the log series is highly persistent or not, and then proceeds either without bias correction (high persistence) or with bias correction (low persistence).     

Bartlett correction in the stable second‐order autoregressive model with intercept and trend

This paper derives the Bartlett factors that can be used to obtain higher‐order improvements for testing hypotheses about the autoregressive (AR) parameters in the stable AR(2) model with possible intercept and linear trend. The factors are obtained for testing hypotheses about individual parameters (φ₁ and φ₂) as well as their sum. Moreover, the effect of deterministic terms on the correction factors is found explicitly. All corrections are non‐decreasing in the AR parameters. Furthermore, the Bartlett corrections for φ₁ and φ₂ tend to infinity as φ₂ approaches 1, whereas the correction for φ₁ + φ₂ tends to infinity as φ₁ + φ₂ is close to 1. The effectiveness of these Bartlett corrections in finite samples is evaluated by simulations.     

Spread determinants and the day-of-the-week effect

In this paper, we examine the determinants of the dollar bid–ask spread for each day of the week over the period 1998–2008. Using a panel cointegration approach, we estimate the determinants of the spread in both the short-run and long-run. Our main findings suggest that: (1) there are day-of-the-week effects for certain groups of firms; (2) the panel error correction model also reveals day-of-the-week effects, and the speed of adjustment to equilibrium following a shock is faster on Fridays; and (3) the effects of volume and volatility on the spread are mixed, with only some sectors experiencing the day-of-the-week effect.     

SELECTION BIAS CORRECTIONS BASED ON THE MULTINOMIAL LOGIT MODEL: MONTE CARLO COMPARISONS

Abstract.  This survey presents the set of methods available in the literature on selection bias correction, when selection is specified as a multinomial logit model. It contrasts the underlying assumptions made by the different methods and shows results from a set of Monte Carlo experiments. We find that, in many cases, the approach initiated by Dubin and MacFadden (1984) as well as the semi-parametric alternative recently proposed by Dahl (2002) are to be preferred to the most commonly used Lee (1983) method. We also find that a restriction imposed in the original Dubin and MacFadden paper can be waived to achieve more robust estimators. Monte Carlo experiments also show that selection bias correction based on the multinomial logit model can provide fairly good correction for the outcome equation, even when the IIA hypothesis is violated.     

Structural FECM: Cointegration in large‐scale structural FAVAR models

Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a factor‐augmented vector autoregressive (FAVAR) model is positively related to the strength of the error correction mechanism and the cross‐section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified permanent real (productivity) and monetary policy shocks.     

On the second-order properties of empirical likelihood with moment restrictions

This paper considers the second-order properties of empirical likelihood (EL) for a parameter defined by moment restrictions, which is the inferential framework of the generalized method of moments. It is shown that the EL defined for this general framework still admits the delicate second-order property of Bartlett correction. This represents a substantial extension of all the established cases of Bartlett correction for the EL. An empirical Bartlett correction is proposed, which is shown to work effectively in improving the coverage accuracy of confidence regions for the parameter.     

A comparison among partial adjustment,rational expectations and error correction estimates of the canadian demand for money

Starting from a dynamic optimization principle, the currently most popular approaches to modelling money demand functions are derived. The partial adjustment/adaptive expectations, rational expectations, and error correction mechanism formulations are then estimated using a common data set. The error correction mechanism equation is found to dominate the others either because their implicit restrictions are rejected (rational expectations) or by employing the encompassing principle (partial adjustment/adaptive expectations). Surprisingly all three forms have similar long-run solutions. Since the short-run dynamics differ substantially, the results have important implications for the conduct of monetary policy.     

Hedging with the Nikkei index futures: The convential model versus the error correction model

This study estimates and compares the hedge ratios of the conventional and the error correction models using Japan's Nikkei Stock Average (NSA) index and the NSA index futures with different time intervals. Comparisons of out-of-sample hedging performance reveal that the error correction model outperforms the conventional model, suggesting that the hedge ratios obtained by using the error correction model do a better job in reducing the risk of the cash position than those from the conventional model. In addition, this paper evaluates the effects of temporal aggregation on hedge ratios. It is found that temporal aggregation has important effects on the hedge ratio estimates.     

Urban disamenities and the measure of economic welfare

A labor market model is used to estimate elasticities between various disamenity factors of urban areas and the wage in those areas. The results are used to calculate a correction to the personal income (PI) data for use in the construction of an index to be used as a measure of economic welfare. The labor supply equation of the model includes some population-related variables which Tobin and Nordhaus have hypothesized represent disamenities of urban living. The labor demand equation is derived from a production function which includes a measure of agglomeration economies. A two-stage least-squares regression analysis provides results which indicate that the hypothesized factors are actually amenity factors. The resulting correction to the personal income data is thus an addition: Economic welfare is higher than that indicated by the PI statistics.     

Selection correction and sensitivity analysis for ordered treatment effect on count response

In estimating the effect of an ordered treatment τ on a count response y with an observational data where τ is self‐selected (not randomized), observed variables x and unobserved variables ε can be unbalanced across the control group (τ = 0) and the treatment groups (τ = 1, …, J). While the imbalance in x causes ‘overt bias’ which can be removed by controlling for x, the imbalance in ε causes ‘covert (hidden or selection) bias’ which cannot be easily removed. This paper makes three contributions. First, a proper counter‐factual causal framework for ordered treatment effect on count response is set up. Second, with no plausible instrument available for τ, a selection correction approach is proposed for the hidden bias. Third, a nonparametric sensitivity analysis is proposed where the treatment effect is nonparametrically estimated under no hidden bias first, and then a sensitivity analysis is conducted to see how sensitive the nonparametric estimate is to the assumption of no hidden bias. The analytic framework is applied to data from the Health and Retirement Study: the treatment is ordered exercise levels in five categories and the response is doctor office visits per year. The selection correction approach yields very large effects, which are however ruled out by the nonparametric sensitivity analysis. This finding suggests a good deal of caution in using selection correction approaches. Copyright © 2004 John Wiley & Sons, Ltd.     

Forecasting and explaining aggregate consumer credit delinquency behaviour

We model aggregate delinquency behaviour for consumer credit (including credit card loans and other consumer loans) and for residential real estate loans using data up until 2008. We test for cointegrating relationships and then estimate short run error correction models. We find evidence to support the portfolio explanations of declines in credit quality for consumer and for real estate loans, but support for the reduced stigma explanation was restricted to real estate loans. Evidence supportive of household-level explanations of irrational borrowing and unexpected net income shocks was found for consumer and real estate loans, but evidence of strategic default was restricted to the volume of consumer loans and real estate loans, and not for credit cards. We also found that the error correction model gave forecasts of the volume of delinquent consumer debt which were of an accuracy comparable to that of an ARIMA model.     

Bartlett correction to the likelihood ratio test for MCAR with two‐step monotone sample

Assuming that two‐step monotone missing data is drawn from a multivariate normal population, this paper derives the Bartlett‐type correction to the likelihood ratio test for missing completely at random (MCAR), which plays an important role in the statistical analysis of incomplete datasets. The advantages of our approach are confirmed in Monte Carlo simulations. Our correction drastically improved the accuracy of the type I error in Little's (1988, Journal of the American Statistical Association, 83 , 1198–1202) test for MCAR and performed well even on moderate sample sizes.     

G856和GSM-19T质子磁力仪观测数据日变改正处理新方法

使用G856和GSM-19T质子磁力仪在同一个测区进行磁测数据采集,由于这两种型号的质子磁力仪观测数据文件格式不一样,用其配套软件相互日变改正特别困难和繁琐,并且用其配套软件日变改正没有五点或七点滑动平均。为了解决这些问题,在Microsoft Office Excel平台编写五点或七点滑动平均和日变改正VBA程序。该程序经实验验证和一个测区20971个磁测数据生产应用验证表明VBA程序的正确性。这个VBA程序适用不同型号的质子磁力仪观测数据之间日变改正,只要把观测数据转换到Microsoft Office Excel中并插入VBA程序代码即可进行日变改正,非常实用、快捷。     

基于AMESim软件的双轮铣槽机纠偏机构液压系统分析

本文介绍了双轮铣槽机纠偏系统的主要工作性能以及纠偏的三种主要形式,并设计了液压纠偏系统原理图。建立了AMESim仿真模型,对纠偏系统做仿真分析,以纠偏角度为仿真变量,得出了两个纠偏油缸的受力曲线、泵口压力、流量等随时间变化曲线,为连续墙铣削装置纠偏系统的机构设计、液压系统的设计与调试等提供了参考和依据。     

An examination of two-step estimators for models with lagged dependent variables and autocorrelated errors

The possible roles of the Durbin Equation and the first observation correction in improving the efficiency of parameter estimates in the lagged dependent variables-serial correlation model are examined. Unconstrained estimation of the Durbin Equation results in an estimate of ρ which is inefficient and its use in feasible generalized least squares does not provide asymptotically efficient estimates. Evidently, the first observation correction is a very important determinant of small sample properties in the present model. Asymptotically inefficient estimators which use a first observation correction frequently outperform Hatanaka's asymptotically efficient estimator in finite samples, essentially because it does not use the first observation.     

The international element of economic fluctuations of 20 OECD-countries 1948–1975

The concept of the international element and some of the problems involved in its measurement are first discussed and a simple correlation-index p for the calculation of the international element in a time-series is presented. The measure is then applied to 13 main series in 20 countries from 1948 to 1975. A few central features of the resulting patterns are finally discussed. The two main findings are: (1) the inadequacy of the trade flows as transmitters of more than, at most, half of the interconnection observed, and (2) the relatively small differences in the international elements in open and closed economies, even when a correction for the relative sizes of the economies is made.     

Garbage and Recycling with Endogenous Local Policy

This paper estimates the impact of garbage fees and curbside recycling programs on garbage and recycling amounts. Without correction for endogenous policy, a price per bag of garbage has a negative effect on garbage and a positive cross-price effect on recycling. Correction for endogenous local policy increases the effect of the user fee on garbage and the effect of curbside recycling collection on recycling. Introducing a fee of $1 per bag is estimated to reduce garbage by 412 pounds per person per year (44%), but to increase recycling by only 30 pounds per person per year.     

浅析遥感图像的几何校正原理及方法

几何校正,就是清除遥感图像中的几何变形,是遥感影像应用的一项重要的前期处理工作。本文简单分析了几何校正的原理和基本方法,并以ERDAS软件为例,对青海海东地区遥感影像进行了几何校正,从而直观地表述了遥感图像几何校正的完整过程。结果表明,几何校正的精度受多方面因素影响,最主要的是控制点GCP的选取数量和选取位置。本次校正精度小于0.5个像元,符合要求。