One of the core ideas in statistics, which extends to many other fields including forecasting, is the concept of structure versus noise. You’ve probably heard of it, but it’s often overlooked by those without a strong quantitative background. So, let’s discuss. The core of the idea is that any data consists of two fundamental parts: […]

# statistics

# Complex-Valued Econometrics with Examples in R

Back in 2022, my father asked me to help him in amending and editing a monograph he wrote on the topic of “Complex-Valued Econometrics”. The original book focused on dynamic models, but after looking through the material and a thorough discussion, we decided to write something more fundamental. The monograph is based on the research […]

# Multistep loss functions: Geometric Trace MSE

While there is a lot to say about multistep losses, I’ve decided to write the final post on one of them and leave the topic alone for a while. Here it goes. Last time, we discussed MSEh and TMSE, and I mentioned that both of them impose shrinkage and have some advantages and disadvantages. One […]

# Multistep loss functions: Trace MSE

As we discussed last time, there are two possible strategies in forecasting: recursive and direct. The latter aligns with the estimation of a model using a so-called multistep loss function, such as Mean Squared Error for h-steps-ahead forecast (MSEh). But this is not the only loss function that can be efficiently used for model estimation. […]

# Recursive vs Direct Forecasting Strategy

Have you heard about the recursive vs direct forecasts? There’s literature about them in the areas of both ML and statistics. What’s so special about them? Here is a short post. The term “recursive” forecasting refers to the approach, when we produce one-step-ahead forecast first, then use it to produce two-steps-ahead, three-steps-ahead, and so on. […]

# Statistical tests flowchart

In Lancaster University, I teach the module called “Statistics and Descriptive Analytics”, which is compulsory for master students of the programme “Business Analytics“. This year, the module has been delivered by Alisa Yusupova and me, and I have prepared a flowchart that should (hopefully) help students decide, which of the statistical tests to use in […]

# An Integrated Method for Estimation and Optimisation

My PhD student, Congzheng Liu (co-supervised with Adam Letchford) has written a paper, entitled “Newsvendor Problems: An Integrated Method for Estimation and Optimisation“. This paper has recently been published in EJOR. In this paper we build upon the existing Ban & Rudin (2019) approach for newsvendor problem, showing that in case of the linear model, […]

# greybox package for R

I am delighted to announce a new package on CRAN. It is called “greybox”. I know, what my American friends will say, as soon as they see the name – they will claim that there is a typo, and that it should be “a” instead of “e”. But in fact no mistake was made – […]

# Comparing additive and multiplicative regressions using AIC in R

One of the basic things the students are taught in statistics classes is that the comparison of models using information criteria can only be done when the models have the same response variable. This means, for example, that when you have \(\log(y_t)\) and calculate AIC, then this value is not comparable with AIC from a […]

# “smooth” package for R. Common ground. Part II. Estimators

UPDATE: Starting from the v2.5.1 the cfType parameter has been renamed into loss. This post has been updated since then in order to include the more recent name. A bit about estimates of parameters Hi everyone! Today I want to tell you about parameters estimation of smooth functions. But before going into details, there are […]