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Research in the area of forecasting and stock inventory control for intermittent demand is designed to provide robust models for the underlying demand which appears at random, with some time periods having no demand at all. Croston’s method is a popular technique for these models and it uses two single exponential smoothing (SES) models which involve smoothing constants. A key issue is the choice of the values due to the sensitivity of the forecasts to changes in demand. Suggested selections of the smoothing constants include values between 0.1 and 0.3. Since an ARIMA model has been illustrated to be equivalent to SES, an optimal smoothing constant can be selected from the ARIMA model for SES. This chapter will conduct simulations to investigate whether using an optimal smoothing constant versus the suggested smoothing constant is important. Since SES is designed to be an adapted method, data are simulated which vary between slow and fast demand.

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent developments in nonparametric econometrics. Therefore, we choose to limit our focus on the following areas. In Section 2, we review the recent developments of nonparametric estimation and testing of regression functions with mixed discrete and continuous covariates. We discuss nonparametric estimation and testing of econometric models for nonstationary data in Section 3. Section 4 is devoted to surveying the literature of nonparametric instrumental variable (IV) models. We review nonparametric estimation of quantile regression models in Section 5. In Sections 2–5, we also point out some open research problems, which might be useful for graduate students to review the important research papers in this field and to search for their own research interests, particularly dissertation topics for doctoral students. Finally, in Section 6 we highlight some important research areas that are not covered in this paper due to space limitation. We plan to write a separate survey paper to discuss some of the omitted topics.

Control charts are designed to be effective in detecting a shift in the distribution of a process. Typically, these charts assume that the data for these processes follow an approximately normal distribution or some known distribution. However, if a data-generating process has a large proportion of zeros, that is, the data is intermittent, then traditional control charts may not adequately monitor these processes. The purpose of this study is to examine proposed control chart methods designed for monitoring a process with intermittent data to determine if they have a sufficiently small percentage of false out-of-control signals. Forecasting techniques for slow-moving/intermittent product demand have been extensively explored as intermittent data is common to operational management applications (Syntetos & Boylan, 2001, 2005, 2011; Willemain, Smart, & Schwarz, 2004). Extensions and modifications of traditional forecasting models have been proposed to model intermittent or slow-moving demand, including the associated trends, correlated demand, seasonality and other characteristics (Altay, Litteral, & Rudisill, 2012). Croston’s (1972) method and its adaptations have been among the principal procedures used in these applications. This paper proposes adapting Croston’s methodology to design control charts, similar to Exponentially Weighted Moving Average (EWMA) control charts, to be effective in monitoring processes with intermittent data. A simulation study is conducted to assess the performance of these proposed control charts by evaluating their Average Run Lengths (ARLs), or equivalently, their percent of false positive signals.

One aspect of forecasting intermittent demand for slow-moving inventory that has not been investigated to any depth in the literature is seasonality. This is due in part to the reliability of computed seasonal indexes when many of the periods have zero demand. This chapter proposes an innovative approach which adapts Croston's (1970) method to data with a multiplicative seasonal component. Adaptations of Croston's (1970) method are popular in the literature. This method is one of the most popular techniques to forecast items with intermittent demand. A simulation is conducted to examine the effectiveness of the proposed technique extending Croston's (1970) method to incorporate seasonality.

In this paper we construct a nonparametric kernel estimator to estimate the joint multivariate cumulative distribution function (CDF) of mixed discrete and continuous variables. We use a data-driven cross-validation method to choose optimal smoothing parameters which asymptotically minimize the mean integrated squared error (MISE). The asymptotic theory of the proposed estimator is derived, and the validity of the cross-validation method is proved. We provide sufficient and necessary conditions for the uniqueness of optimal smoothing parameters when the estimation of CDF degenerates to the case with only continuous variables, and provide a sufficient condition for the general mixed variables case.

This research examines the use of a number of time series model structures of a moderate allocation mutual fund, PRWCX. PRWCX was rated as the top fund in its category during the past five years. The fund invests at least 50% of its total assets that the fund manager believes that have above average potential for capital growth. The remaining assets are generally invested in convertible securities, corporate and government debt bank loans, and foreign securities. Forecasting the total NAV of such a moderate allocation mutual fund, composed of an extremely large number of investments, requires a method that produces accurate results. These models are exponentially smoothing (single, double, and Winter’s Method), trend models (linear, quadratic, and exponential) are Box-Jenkins models.

Small-scale VARs are widely used in macroeconomics for forecasting US output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real-time forecasting. We use forecasts from univariate time series models, the Survey of Professional Forecasters, and the Federal Reserve Board's Greenbook as benchmarks.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

Diagrams are ubiquitous in economics and are uncontestably among the most used, if not the most important workhorses of economists, though they come in many forms. This essay examines the different uses of graphs and diagrams in the pioneering work of two Victorian economists, Stanley Jevons and Alfred Marshall. We stress the difference between their use as representations and as visual reasoning tools, a difference that became obscured in the twentieth century with the rise of econometrics.