“A Bias Recognized Is a Bias Sterilized”: The Effects of a Bias in Forecast Evaluation

Are traditional tests of forecast evaluation well behaved when the competing (nested) model is biased? No, they are not. In this paper, we show analytically and via simulations that, under the null hypothesis of no encompassing, a bias in the nested model may severely distort the size properties of traditional out-of-sample tests in economic forecasting. Not surprisingly, these size distortions depend on the magnitude of the bias and the persistency of the additional predictors. We consider two different cases: (i) There is both in-sample and out-of-sample bias in the nested model. (ii) The bias is present exclusively out-of-sample. To address the former case, we propose a modified encompassing test (MENC-NEW) robust to a bias in the null model. Akin to the ENC-NEW statistic, the asymptotic distribution of our test is a functional of stochastic integrals of quadratic Brownian motions. While this distribution is not pivotal, we can easily estimate the nuisance parameters. To address the second case, we derive the new asymptotic distribution of the ENC-NEW, showing that critical values may differ remarkably. Our Monte Carlo simulations reveal that the MENC-NEW (and the ENC-NEW with adjusted critical values) is reasonably well-sized even when the ENC-NEW (with standard critical values) exhibits rejections rates three times higher than the nominal size.

“Go Wild for a While!”: A New Test for Forecast Evaluation in Nested Models

In this paper, we present a new asymptotically normal test for out-of-sample evaluation in nested models. Our approach is a simple modification of a traditional encompassing test that is commonly known as Clark and West test (CW). The key point of our strategy is to introduce an independent random variable that prevents the traditional CW test from becoming degenerate under the null hypothesis of equal predictive ability. Using the approach developed by West (1996), we show that in our test, the impact of parameter estimation uncertainty vanishes asymptotically. Using a variety of Monte Carlo simulations in iterated multi-step-ahead forecasts, we evaluated our test and CW in terms of size and power. These simulations reveal that our approach is reasonably well-sized, even at long horizons when CW may present severe size distortions. In terms of power, results were mixed but CW has an edge over our approach. Finally, we illustrate the use of our test with an empirical application in the context of the commodity currencies literature.

A Performance Comparison of Various Bootstrap Methods for Diffusion Processes

In this paper, we compare the finite sample performances of various bootstrap methods for diffusion processes. Though diffusion processes are widely used to analyze stocks, bonds, and many other financial derivatives, they are known to heavily suffer from size distortions of hypothesis tests. While there are many bootstrap methods applicable to diffusion models to reduce such size distortions, their finite sample performances are yet to be investigated. We perform a Monte Carlo simulation comparing the finite sample properties, and our results show that the strong Taylor approximation method produces the best performance, followed by the Hermite expansion method.

FDML versus GMM for Dynamic Panel Models with Roots Near Unity

This paper evaluates the first-differenced maximum likelihood (FDML) and the continuously updating system generalized method of moments (CU-GMM) estimators of dynamic panel models when the data is close to non-stationary. This case is far from trivial, as a high degree of persistence is the norm rather than the exception in economic panels, particularly in financial management. While the CU-GMM is shown to have lower bias and higher power, it suffers from severe size distortions, which are exacerbated when the data approaches non-stationarity.

Regulated seasonal unit root process

Unfortunately, time series problems do not appear in data singly. We focus on the joint occurrence of nonstationarity, seasonality and bounded data. Seasonal unit root tests and bounded unit root tests already exist in the literature, yet when all these issues are combined their performance needs improvement. That is why we offer a testing procedure for bounded seasonal unit root processes. The combination of these tests is not straightforward as the nonlinearity coming from bounds causes the limiting distribution of the proposed test statistic to be multivariate Brownian motion while the others have univariate distributions. The simulation exercises reveal that the existing tests, which ignores the presence of bounds or seasonality, suffer significant size problems. Our statistic removes the size distortions and also maintain satisfactory power performance.

Micrometric research results of vacuum dough divider components

Introduction. Nowadays, vacuum-type dough dividers are used in industries with a production volume of up to 4,000 loaves per day. In the dough divider operation, due to wear of the working surfaces of the piston, chamber, and drum, the gap between them goes beyond the value equal to 50 microns, which provides vacuum in the suction chamber. As a result, the suction process becomes unstable; the dough divider disturbs the weight accuracy of bakery goods. Repair of such equipment is carried out mainly through a full or partial replacement of worn parts and assemblies with new ones. To increase their durability, there is a need to develop a new highly efficient technology with the restoration of worn part surfaces using the welding and surfacing methods.Materials and Methods. A new technique of determining the number of objects for research using the “STATISTICA” program is presented. Wear surfaces of the vacuum dough divider parts are determined.Research Results. Micrometric studies of the dough divider components were carried out. They showed the presence of appreciable size distortions due to the local wear of the working surfaces. In this case, a side gap between the suction chamber and the main piston and between the drum and the suction chamber is 6 times higher than the permissible one, and a vertical gap between the division box and the piston exceeds the permissible gap by more than 10 times. Wear of the working surfaces of the dough divider parts is local in nature, while the range of values is as follows: for the main piston, it is 10-200 microns; for the gaging piston, it is 250- 900 microns; for the suction chamber and division box, it is 300-400 microns; for the drum surfaces, it is 280-300 microns.Discussion and Conclusions. The conducted micrometric studies showed the presence of appreciable size distortions due to the local wear of the working surfaces. Based on the results obtained, it can be argued that the most productive and economically viable technique for the restoration of worn surfaces of dough divider parts is, for example, the electrospark machining.

Industrial Policies in Production Networks*

Many developing economies adopt industrial policies favoring selected sectors. Is there an economic logic to this type of intervention? I analyze industrial policy when economic sectors form a production network via input-output linkages. Market imperfections generate distortionary effects that compound through backward demand linkages, causing upstream sectors to become the sink for imperfections and have the greatest size distortions. My key finding is that the distortion in sectoral size is a sufficient statistic for the social value of promoting that sector; thus, there is an incentive for a well-meaning government to subsidize upstream sectors. Furthermore, sectoral interventions’ aggregate effects can be simply summarized, to first order, by the cross-sector covariance between my sufficient statistic and subsidy spending. My sufficient statistic predicts sectoral policies in South Korea in the 1970s and modern-day China, suggesting that sectoral interventions might have generated positive aggregate effects in these economies.

Pitfalls of Two-Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model

In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, conditional on the break dates found, test for changes in the variance (or in the coefficients). In this note, we provide evidence that such procedures have poor finite sample properties when the changes in the first step are not correctly accounted for. In doing so, we show that testing for changes in the coefficients (or in the variance) ignoring changes in the variance (or in the coefficients) induces size distortions and loss of power. Our results illustrate a need for a joint approach to test for structural changes in both the coefficients and the variance of the errors. We provide some evidence that the procedures suggested by Perron et al. (2019) provide tests with good size and power.