Forecasting crude oil market volatility using variable selection and common factor

This paper aims to improve the predictability of aggregate oil market volatility with a substantially large macroeconomic database, including 127 macro variables. To this end, we use machine learning from both the variable selection (VS) and common factor (i.e., dimension reduction) perspectives. We first use the lasso, elastic net (ENet), and two conventional supervised learning approaches based on the significance level of predictors’ regression coefficients and the incremental R-square to select useful predictors relevant to forecasting oil market volatility. We then rely on the principal component analysis (PCA) to extract a common factor from the selected predictors. Finally, we augment the autoregression (AR) benchmark model by including the supervised PCA common index. Our empirical results show that the supervised PCA regression model can successfully predict oil market volatility both in-sample and out-of-sample. Also, the recommended models can yield forecasting gains in both statistical and economic perspectives. We further shed light on the nature of VS over time. In particular, option-implied volatility is always the most powerful predictor.     

Consumer credit on American Indian reservations

This paper provides the first encompassing quantitative picture of consumer credit in Indian Country. Drawing on a unique large-scale consumer credit database, we find that Equifax Risk Scores and the use of certain forms of credit, especially mortgages, are low on reservations. However, usage of other forms of credit on reservations is not always low. Moreover, the gaps in credit usage on versus off reservations differ significantly across states and can change notably over time. Among predictors of consumer credit outcomes, the percentage of American Indian residents is robustly negatively associated with favorable credit outcomes. Furthermore, once controlling for racial composition, the effect of an area's location vis-à-vis a reservation often becomes statistically insignificant. Other socio-economic variables are generally poor predictors of credit outcomes on reservations. State jurisdiction over legal matters is, at least on average, associated with favorable credit outcomes on reservations.     

Hiring for retention and performance

This study evaluated the usefulness of several pre‐hire variables to predict voluntary turnover and job performance. Analyses showed that applicants who knew current employees, had longer tenure with previous employers, were conscientious and emotionally stable, were motivated to obtain the job, and were confident in themselves and their decision making were less likely to quit, and had higher performance within six months after hire. Results also indicated that pre‐hire attitudes (employment motivation and personal confidence) did not predict turnover and performance beyond biodata (pre‐hire embeddedness in the organization and habitual commitment) and the personality traits (conscientiousness and emotional stability). For all predictors but personality, the strength of the relationships weakened over time up to two years after hire. Nonetheless, organizations can avoid voluntary turnover and increase performance by basing hiring decisions on the set of predictors analyzed in this study. © 2009 Wiley Periodicals, Inc.     

Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro‐level Analysis?

Forecasting labour market flows is important for budgeting and decision‐making in government departments and public administration. Macroeconomic forecasts are normally obtained from time series data. In this article, we follow another approach that uses individual‐level statistical analysis to predict the number of exits out of unemployment insurance claims. We present a comparative study of econometric, actuarial and statistical methodologies that base on different data structures. The results with records of the German unemployment insurance suggest that prediction based on individual‐level statistical duration analysis constitutes an interesting alternative to aggregate data‐based forecasting. In particular, forecasts of up to six months ahead are surprisingly precise and are found to be more precise than considered time series forecasts.     

Forecasting economic time series using targeted predictors

This paper studies two refinements to the method of factor forecasting. First, we consider the method of quadratic principal components that allows the link function between the predictors and the factors to be non-linear. Second, the factors used in the forecasting equation are estimated in a way to take into account that the goal is to forecast a specific series. This is accomplished by applying the method of principal components to ‘targeted predictors’ selected using hard and soft thresholding rules. Our three main findings can be summarized as follows. First, we find improvements at all forecast horizons over the current diffusion index forecasts by estimating the factors using fewer but informative predictors. Allowing for non-linearity often leads to additional gains. Second, forecasting the volatile one month ahead inflation warrants a high degree of targeting to screen out the noisy predictors. A handful of variables, notably relating to housing starts and interest rates, are found to have systematic predictive power for inflation at all horizons. Third, the targeted predictors selected by both soft and hard thresholding changes with the forecast horizon and the sample period. Holding the set of predictors fixed as is the current practice of factor forecasting is unnecessarily restrictive.     

Who Will We Recruit? Targeting Deep‐ and Surface‐Level Diversity with Human Resource Policy Advertising

Research on targeted recruitment has focused on targeting applicants with surface‐level attributes such as underrepresented demographic groups. The present study extends targeted recruitment research by examining how advertising human resource policies might be useful for targeting both surface‐ and deep‐level attributes. Specifically, the current study uses an experimental design to examine the impact of work‐family, diversity, and employee development policies on the job‐pursuit intentions of working adults in the United States. We examined surface‐ (demographic characteristics; e.g., race) and deep‐level differences (attitudes or values; e.g., diversity values) as predictors of whether participants intend to pursue jobs with firms advertising these human resource (HR) policies. Deep‐level differences consistently predicted job‐pursuit intentions for all three HR policies, but only marginal support was obtained for surface‐level variables as predictors. Findings suggest that targeted recruitment based on deep‐level attributes may be more successful than targeted recruitment based on surface‐level factors.     

Predictors of organizational commitment in India: strategic HR roles,organizational learning capability and psychological empowerment

Employee commitment continues to be one of the most exciting issues for both practitioners and academicians. Linkages in literature are found for HRM Practices, but there is a lacuna of research linking organizational commitment and strategic HR roles, psychological empowerment as well as organizational learning capability. The current study examines these linkages. Whether these variables predict organizational commitment in Indian managers or not, is also investigated. The sample size of the study comprises of 640 Indian managers. Bivariate and multivariate analysis confirms the hypotheses drawn from the literature. The paper discusses the theoretical and practical implications of the findings.     

Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model

Election forecasting has become a fixture of election campaigns in a number of democracies. Structural modeling, the major approach to forecasting election results, relies on ‘fundamental’ economic and political variables to predict the incumbent’s vote share usually a few months in advance. Some political scientists contend that adding vote intention polls to these models—i.e., synthesizing ‘fundamental’ variables and polling information—can lead to important accuracy gains. In this paper, we look at the efficiency of different model specifications in predicting the Canadian federal elections from 1953 to 2015. We find that vote intention polls only allow modest accuracy gains late in the campaign. With this backdrop in mind, we then use different model specifications to make ex ante forecasts of the 2019 federal election. Our findings have a number of important implications for the forecasting discipline in Canada as they address the benefits of combining polls and ‘fundamental’ variables to predict election results; the efficiency of varying lag structures; and the issue of translating votes into seats.     

A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth

This paper estimates a three-frequency dynamic factor model for nowcasting the Canadian provincial gross domestic product (GDP). The Canadian provincial GDP at market prices is released by Statistics Canada annually with a significant lag (11 months). This necessitates a mixed-frequency approach that can process timely monthly data, the quarterly national accounts, and the annual target variable. The model is estimated on a wide set of provincial, national and international data. In a pseudo real-time exercise, we find that the model outperforms simple benchmarks and is competitive with more sophisticated mixed-frequency approaches (MIDAS models). We also find that variables from the Labour Force Survey are important predictors of real activity. This paper expands previous work that has documented the importance of foreign variables for nowcasting Canadian GDP. This paper finds that including national and foreign predictors is useful for Ontario, while worsening the nowcast performance for smaller provinces.     

A control function approach for testing the usefulness of trending variables in forecast models and linear regression

Many predictors employed in forecasting macroeconomic and finance variables display a great deal of persistence. Tests for determining the usefulness of these predictors are typically oversized, overstating their importance. Similarly, hypothesis tests on cointegrating vectors will typically be oversized if there is not an exact unit root. This paper uses a control variable approach where adding stationary covariates with certain properties to the model can result in asymptotic normal inference for prediction regressions and cointegration vector estimates in the presence of possibly non-unit root trending covariates. The properties required for this result are derived and discussed.     

Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility

Using monthly data from 1973 through 2020, we explore whether it is possible to improve the accuracy of one-month ahead log-aggregate equity return realized volatility point forecasts by conditioning on various nonlinear crude oil price measures widely relied on in the literature. When evaluating the evidence of unconditional relative equal predictive ability as specified in Diebold and Mariano (1995), we observe that similar to well-known economic variables, such as the dividend yield, the default yield spread and the rate of inflation, we rarely observe evidence of statistical gains in relative point forecast accuracy in favor of the crude oil price-based models. However, when evaluating the evidence of conditionalrelative equal predictive ability as specified in Giacomini and White (2006), we observe that contrary to well-known economic predictors, certain nonlinear crude oil price variables, such as the one-year net crude oil price increase suggested in Hamilton (1996) offer sizable point forecast accuracy gains relative to the benchmark. These statistical gains can also be translated into economic gains.     

Forecasting and trading on the VIX futures market: A neural network approach based on open to close returns and coincident indicators

Previous work has highlighted the difficulty of obtaining accurate and economically significant predictions of VIX futures prices. We show that both low prediction errors and a significant amount of profitability can be obtained by using a neural network model to predict VIX futures returns. In particular, we focus on open-to-close returns (OTCRs) and consider intraday trading strategies, taking into account non-lagged exogenous variables that closely reflect the information possessed by traders at the time when they decide to invest. The neural network model with only the most recent exogenous variables (namely, the return on the Indian BSESN index) is superior to an unconstrained specification with ten lagged and coincident regressors, which is actually a form of weak efficiency involving markets of different countries. Moreover, the neural network turns out to be more profitable than either a logistic specification or heterogeneous autoregressive models.     

概率化预测隧道地质条件

为了预测隧道开挖面前方的地质条件,降低施工风险和成本,采用马尔科夫和神经网络构成的模型,既比马尔科夫方法经济,又能实现神经网络方法所不能实现的动态化预测。     

Real-time macroeconomic forecasting with leading indicators: An empirical comparison

This paper demonstrates that the Conference Board’s Composite Leading Index (CLI) has significant real-time predictive ability for Industrial Production (IP) growth rates at horizons from one to six months ahead over the period 1989-2009. A popular but unrealistic analysis, which combines real-time data for CLI and final vintage data for IP as predictor variables, obscures the actual predictive content of the CLI, in the sense that in that case, the improvements in forecast accuracy relative to a univariate AR model are not significant. The CLI appears to be less useful for forecasting growth rates of the Conference Board’s Composite Coincident Index (CCI) in real time, as a univariate AR model performs better. This result is mostly due to its disappointing performance during the first five years of the forecast period. The CLI may not be the best way of exploiting the information contained in the underlying individual leading indicator variables. The use of principal components instead of CLI leads to more accurate real-time forecasts for both IP and CCI growth rates.     

Application of the Yale Technology Concordance to the Construction of International Spillover Variables for India

Although there is increased interest in the role of international technology spillovers, empirical studies have been hampered by a host of measurement problems. This paper reviews recent attempts to address two of these problems. First, there are differing degrees of transferability of technology from one region to another. Second, both embodied and disembodied technology are transferred, and these different modes of transfer have distinct implications for both behavior and policy. To deal with the first measurement problem, the Yale Technology Concordance (YTC)—a matrix that maps patents into industries of manufacture and sectors of use—was used to construct indices of relevance of foreign technology to India. To deal with the second measurement problem, the YTC was again used to construct pools of embodied and disembodied international technology. After describing the variable construction, the paper highlights the performance of these variables in equations that predict Indian firms' R&D, technology purchases and output.     

LIFE STYLE-WORK GROUP STRUCTURE-TASK CONGRUENCE AND JOB SATISFACTION AND PERFORMANCE: A THREE-DIMENSIONAL CONTINGENCY MODEL

A Three -Dimensional contingency‘theory’of organizational behaviour was used to predict job satisfaction and performance in a sample of registered representatives of a national securities firm. Contingency variables composed of individual life style orientation, perceived work group structure and perceived job complexity did not predict a significant amount of variance in the dependent measures. Cautions concerning the assumed universal superiority of contingency predictors over simpler formulations are discussed.     

A sight for sore eyes: Assessing the value of view and land use in the housing market

We apply a hedonic model to the Geneva–Switzerland rental market to assess the value of view from dwellings and of land uses around buildings. Using a geographic information system, we calculate three-dimensional view variables, accessibility and land use variables. To our knowledge, this is the first paper to develop precise view measures at the dwelling level, considering surrounding land uses, in an urban context and with a large sample of 13,000 observations. The results show that view of various environmental amenities and its size has a significant impact on rents. The estimated rent premium for a dwelling located in a neighbourhood with an extended surface of water can be as high as 3%, and a view of water-covered area can raise rent up to 57%.     

Qualitative Input–Output Analysis of the Indian Economic Structure

Qualitative input–output (IO) analysis can be considered mostly in terms of a graph-theoretic formulation, which involves the construction and use of an adjacency matrix, derived from a binary transformation of the IO coefficient matrix. Classical attempts at qualitative IO analysis have derived the structure from the direct IO coefficient table. In more recent attempts, greater sophistication has been achieved by incorporating a degree of quantification into the qualitative analysis. Also, the concept of important coefficients (IC) has been used for the construction of the adjacency matrix. The present study is carried out along these lines for the Indian economy.     

Sex and salary: A survey of purchasing and supply professionals

The purpose of this paper is to test a series of hypotheses, derived from compensation theory, on predictors of purchasing and supply management salaries. One of the hypotheses, on the effect of sex on salary, assesses competing theories, i.e. human capital versus discrimination. These hypotheses are tested using the results of a survey on purchasing and supply management salaries in Canada. Six variables were found to be significant predictors of salary. Purchasers with a professional designation, more education, more experience, greater spend responsibility, at larger organizations, and with greater share of compensation coming as a bonus reported larger salaries. In addition, after controlling for these human capital and organizational context variables, sex had a significant impact on salary. Male purchasing and supply managers reported earning significantly higher salaries than their female counterparts. The paper concludes with implications for purchasing employees and employers, along with discussion of future research opportunities.     

Employee Responses to Changing Aspects of the Employer Brand Following a Multinational Acquisition: A Longitudinal Study

This study examines changing employment brands in the context of a multinational acquisition, specifically the implications for current employees. Using a sample (N = 251) from both the acquired and acquiring workforces, employees are tracked across 12 months following acquisition. The study explores predictors of identification with the acquiring organization, intent to quit, and discretionary effort. We focus on employment brand–related predictors, specifically perceptions linked to the provision of unique employment experiences, organizational identity strength, perceived prestige, and judgments of whether the acquiring organization acts in accordance with its corporate identity claims. The study showed that perceptions of prestige immediately after acquisition predict identification 12 months hence, as do judgments of whether the organization acts in accordance with its corporate social responsibility–based corporate identity claims. These judgments also predict subsequent levels of discretionary effort and long‐term intent to leave, as do perceptions linked to the provision of unique employment experiences. Perceived change in these unique employment experiences is also related to change in identification and intent to leave across time. Importantly, these elements have a varied effect on the adjustment outcomes when comparing the two workforces.