Forecasting the economy with mathematical models: is it worth the effort?

Forecasting ; Mathematical models

Technical note on "The real exchange rate in sticky price models: does investment matter?"

This technical note is developed as a mathematical companion to the paper "The Real Exchange Rate in Sticky Price Models: Does Investment Matter?" (Institute working paper no. 17). It contains three basic calculations. First, we derive the equilibrium conditions of the model. Second, we compute the zero-inflation, zero-trade balance (deterministic) steady state. Third, we describe the log-linearization of the equilibrium conditions around the deterministic steady state. Simultaneously, we explain the system of equations that constitutes the basis for the paper to broaden its scope. Commentary is provided whenever necessary to complement the model description and to place into context the assumptions embedded in our DSGE framework.Globalization ; Foreign exchange ; International finance ; Forecasting ; Mathematical models

The real exchange rate in sticky price models: does investment matter?

This paper re-examines the ability of sticky-price models to generate volatile and persistent real exchange rates. We use a DSGE framework with pricing-to-market akin to those in Chari, et al. (2002) and Steinsson (2008) to illustrate the link between real exchange rate dynamics and what the model assumes about physical capital. We show that adding capital accumulation to the model facilitates consumption smoothing and significantly impedes the model's ability to generate volatile real exchange rates. Our analysis, therefore, caveats the results in Steinsson (2008) who shows how real shocks in a sticky-price model without capital can replicate the observed real exchange rate dynamics. Finally, we find that the CKM (2002) persistence anomaly remains robust to several alternative capital specifications including set-ups with variable capital utilization and investment adjustment costs (see, e.g., Christiano, et al., 2005). In summary, the PPP puzzle is still very much alive and well.Globalization ; Foreign exchange ; International finance ; Forecasting ; Mathematical models

Methods for experimental design, central composite design and the Box–Behnken design, to optimise operational parameters: A review

In recent years response surface analysis has been increasingly used to optimise membrane separation. It has many advantages, such as reducing the number of experiments to be performed, which requires lower energy consumption and significantly less laboratory work. For more accurate data analysis and forecasting, mathematical models are used that analyse the relevance of the factors examined and the interaction effects between the factors. In this research, two experimental designs that use response surface methodology are presented, namely, the central composite design and the Box–Behnken design. After the general characterisation of the experimental designs, their application in membrane technology is presented

The dynamics of economics functions: modelling and forecasting the yield curve

The class of Functional Signal plus Noise (FSN) models is introduced that provides a new, general method for modelling and forecasting time series of economic functions. The underlying, continuous economic function (or "signal") is a natural cubic spline whose dynamic evolution is driven by a cointegrated vector autoregression for the ordinates (or "y-values") at the knots of the spline. The natural cubic spline provides flexible cross-sectional fit and results in a linear, state space model. This FSN model achieves dimension reduction, provides a coherent description of the observed yield curve and its dynamics as the cross-sectional dimension N becomes large, and can feasibly be estimated and used for forecasting when N is large. The integration and cointegration properties of the model are derived. The FSN models are then applied to forecasting 36-dimensional yield curves for US Treasury bonds at the one month ahead horizon. The method consistently outperforms the Diebold and Li (2006) and random walk forecasts on the basis of both mean square forecast error criteria and economically relevant loss functions derived from the realised profits of pairs trading algorithms. The analysis also highlights in a concrete setting the dangers of attempts to infer the relative economic value of model forecasts on the basis of their associated mean square forecast errors.Time-series analysis ; Forecasting ; Mathematical models ; Macroeconomics - Econometric models

Using Satellite Data in Weather Forecasting: I

The GOES Product Generation System (GPGS) is a set of computer codes and scripts that enable the assimilation of real-time Geostationary Operational Environmental Satellite (GOES) data into regional-weather-forecasting mathematical models. The GPGS can be used to derive such geophysical parameters as land surface temperature, the amount of precipitable water, the degree of cloud cover, the surface albedo, and the amount of insolation from satellite measurements of radiant energy emitted by the Earth and its atmosphere. GPGS incorporates a priori information (initial guesses of thermodynamic parameters of the atmosphere) and radiometric measurements from the geostationary operational environmental satellites along with mathematical models of physical principles that govern the transfer of energy in the atmosphere. GPGS solves the radiative-transfer equation and provides the resulting data products in formats suitable for use by weather-forecasting computer programs. The data-assimilation capability afforded by GPGS offers the potential to improve local weather forecasts ranging from 3 hours to 2 days - especially with respect to temperature, humidity, cloud cover, and the probability of precipitation. The improvements afforded by GPGS could be of interest to news media, utility companies, and other organizations that utilize regional weather forecasts

Optimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugal

The COVID-19 pandemic has forced policy makers to decree urgent confinements to stop a rapid and massive contagion. However, after that stage, societies are being forced to find an equilibrium between the need to reduce contagion rates and the need to reopen their economies. The experience hitherto lived has provided data on the evolution of the pandemic, in particular the population dynamics as a result of the public health measures enacted. This allows the formulation of forecasting mathematical models to anticipate the consequences of political decisions. Here we propose a model to do so and apply it to the case of Portugal. With a mathematical deterministic model, described by a system of ordinary differential equations, we fit the real evolution of COVID-19 in this country. After identification of the population readiness to follow social restrictions, by analyzing the social media, we incorporate this effect in a version of the model that allow us to check different scenarios. This is realized by considering a Monte Carlo discrete version of the previous model coupled via a complex network. Then, we apply optimal control theory to maximize the number of people returning to "normal life" and minimizing the number of active infected individuals with minimal economical costs while warranting a low level of hospitalizations. This work allows testing various scenarios of pandemic management (closure of sectors of the economy, partial/total compliance with protection measures by citizens, number of beds in intensive care units, etc.), ensuring the responsiveness of the health system, thus being a public health decision support tool.publishe

Experimental Study on Forecasting Mathematical Model of Drying Shrinkage of Recycled Aggregate Concrete

On the basis of basic law in AASHTO2007 model, the forecasting mathematical model of drying shrinkage of recycled aggregate concrete (RAC) is established by regression analysis and experimental study. The research results show that (1) with the replacement rate of RCA increases, the drying shrinkage value of RAC increases; this trend is even more obvious in the early drying time. (2) The addition of fly ash can inhibit the drying shrinkage of RAC, but the effect is not very obvious. Specifically, the addition of fly ash will increase the shrinkage to some extent when the mixing amount is 20%. (3) The addition of expansive agent can obviously inhibit the shrinkage of RAC; the inhibition affection is better than that of fly ash. (4) The forecasting mathematical models of drying shrinkage of RAC established in this paper have high accuracy and rationality according to experiment validation and error analysis

Using artificial neural networks for prediction of logistics costs of engineering enterprises

В статті розглядається можливість застосування штучних нейронних мереж для прогнозування логістичних витрат на основі інформації про витрати попередніх періодів. Запропоновано використання тришарової мережі з навчанням за методом зворотного розповсюдження помилки. Визначено оптимальну конфігурацію такої нейронної мережі для використання з щомісячною інформацією щодо логістичних витрат машинобудівних підприємств.The article deals with investigation of possibility of using artificial neural networks to predict the logistics costs. The forecasting based on the information of previous periods is considered. Author proposes to use a three-layer feedforward neural network with learning by backpropagation algorithm. An optimal configuration of the neural network for use on monthly logistics cost information is defined. Article emphasizes the urgency of the approach usage at the variety of machine-building enterprises