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Electricity is an important energy source in university as lecture classes need electricity supply to function. It is also important for the development of the university. Since electricity consumption is a necessity of a university's operation, the forecast of electricity consumption on the university campus should be made. This is essential for the development of the university as the treasury department can manage the funding from the government according to the value forecasted to make full use of the funding in the university's development. There are several forecasting methods used in this study, including time series regression, seasonal exponential smoothing, Box-Jenkins (SARIMA), decomposition and the naïve method. Error measurements used to evaluate the performance of forecasting model were mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE) and geometric root mean square error (GRMSE). The results of this study showed that the seasonal exponential smoothing model was the best in the 1-step ahead and 2-step ahead forecasting while SARIMA (0,2,2)(0,2,1)12 was the best in the 3-step ahead forecast. The overall performance of seasonal exponential smoothing was the best in this study. Throughout this study, suggestions were made for the next study regarding electricity consumption in university to consider factors such as semester breaks and students' activities in order to examine its effect in electricity consumption.

We consider P-spline smoothing in a varying coefficient regression model when the response is subject to random right censoring.We introduce two data transformation approaches to construct a synthetic response vector that is used in a penalized least squares optimization problem. We prove the consistency and asymptotic normality of the P-spline estimators for a diverging number of knots and show by simulation studies and real data examples that the combination of a data transformation for censored observations with P-spline smoothing leads to good estimators of the varying coefficient functions. ; The research of the first author was supported by the Research Foundation Flanders (FWO) (Grant No. 11W7315N). Support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy) is gratefully acknowledged. For the simulations, we used the infrastructure of the VSC - Flemish Supercomputer Center, funded by the Hercules Foundation and the Flemish Government - department EWI.

This study aims to identify the preferences of the monetary authority in the Peruvian regime of inflation targeting through the derivation of optimal monetary rules. To achieve that, we used a calibration strategy based on the choice of values of the parameters of preferences that minimize the square deviation between the true interest rate and interest rate optimal simulation. The results showed that the monetary authority has applied a system of flexible inflation targeting, prioritizing the stabilization of inflation, but without disregarding gradualism in interest rates. On the other hand, concern over output stabilization has been minimal, revealing that the output gap has been important because it contains information about future inflation and not because it is considered a variable goal in itself. Finally, when the smoothing of the nominal exchange rate is considered in the loss function of the monetary authority, the rank order of preferences has been maintained and the smoothing of the exchange rate proved insignificant.

Intro -- Preface -- Contents -- Part I Keynote Addresses -- Robust Estimation of Heckman Model -- 1 Introduction -- 2 Basic Robustness Tools -- 2.1 Basic Approaches -- 2.2 Influence Function and Change-of-Variance Function -- 3 Application to Heckman Model -- 3.1 Estimation of Heckman Model -- 3.2 Influence Function and Change-of-Variance Function of Heckman's Two-Stage Estimator -- 3.3 A Robust Two-Stage Estimator -- 3.4 A Simulation Study -- 4 Conclusion -- References -- Part II Fundamental Theory -- Sequential Monte Carlo Sampling for State Space Models -- 1 Introduction -- 2 Linear State Space Models -- 2.1 Gaussian Linear State Space Models and the Kalman Filter -- 2.2 Non-Gaussian Linear State Space Models -- 3 Non-Gaussian and Non-linear State Space Models -- 3.1 Illustrative Example -- 3.2 Bayesian Inference of the Transition System -- 4 Sequential Monte Carlo Sampling -- 4.1 Importance Sampling -- 4.2 Sequential Importance Sampling -- 4.3 Sequential Monte Carlo with Adaptive Resampling -- 5 Examples and Backward Smoothing -- 5.1 Linear State Space Models -- 5.2 Non-Gaussian and Non-linear State Space Models -- 5.3 Stochastic Volatility Model for Asset Prices -- 5.4 Backward Smoothing and Resample-Moves -- 6 Conclusions and Outlook -- References -- Robustness as a Criterion for Selecting a Probability Distribution Under Uncertainty -- 1 Formulation of the Problem -- 2 Robustness: From an Informal General Idea to a Precise Description -- 3 Selecting a Probability Distribution that Minimizes int(ρ(x))2dx -- 4 Selecting a Probability Distribution that Minimizes int(ρ(x))3dx -- 5 Selecting a Probability Distribution that Minimizes maxx ρ(x) -- References -- Why Cannot We Have a Strongly Consistent Family of Skew Normal (and Higher Order) Distributions -- 1 Formulation of the Problem -- 2 Analysis of the Problem and the Main Result.

Applied Regression Including Computing and Graphics; Contents; Preface; PART I INTRODUCTION; 1 Looking Forward and Back; 2 Introduction to Regression; 3 Introduction to Smoothing; 4 Bivariate Distributions; 5 Two-Dimensional Plots; PART II. TOOLS; 6 Simple Linear Regression; 7 Introduction to Multiple Linear Regression; 8 Three-Dimensional Plots; 9 Weights and Lack-of-Fit; 10 Understanding Coefficients; 11 Relating Mean Functions; 12 Factors and Interactions; 13 Response Transformations; 14 Diagnostics I: Curvature and Nonconstant Variance; 15 Diagnostics II: Influence and Outliers.

Metaphor has a wide range of applications in everyday speech and allows us to communicate our thoughts vividly. The main functions of metaphor in political discourse are heuristic, argumentative, and interactive, smoothing the most dangerous political speech that touches on controversial political issues. This paper reviewed studies on metaphor in political discourse to assess the role of metaphor in politics. Besides, in this article, we investigated conceptual metaphors used in modern political discourses by British politicians and tried to analyse them from a cognitive point of view.

A discrete step steepest descent search algorithm is developed for the estimation of interregional migration rates in local areas from historical time series. Objective function values are obtained from a constrained least-squares optimization, and it is found that the dimensions of the search space correspond to the number of smoothing operations in the columns of the data input matrix. The iterative algorithm requires the use of symptomatic indicators of intercensal population and is shown to yield improved results over a simple least squares approach.

Crop forecasting is a formidable challenge for every nation. The Government of India has developed a number of forecasting systems. The national and state governments need such pre-harvest forecasts for various policy decisions on storage, distribution, pricing, marketing, import-export and many more. In this paper, univariate forecasting models such as random walk, random walk with drift, moving average, simple exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) models are considered and analyzed for their efficiency for forecasting vegetable production in the Haryana state. The State annual data on vegetable production were divided into the training data set from 1966-67 to 2013-14 and the test data set from 2014-15 to 2018-19. Suitable models were selected on the basis of error analysis on the training data and a percent error deviation test on the test data. Model diagnostic checking was carried out on ACF and PACF in residual terms through runs above and below the median, runs up and down and Ljung-Box tests. It is inferred that ARIMA (2,1,1) was found to be optimal and that the forecast values for the years 2019-20 to 2023-24 were estimated on the basis of this model, which were 7.82,8.23,8.72,9.2 and 9.72 million tonnes for the year 2019-20 to 2023-24, respectively. The significance of the mode is that we can forecast the values using this best fit model and forecast values are very important for the policymakers and other government agencies for proper policy decision regarding food security.

This paper is concerned with the least-squares linear estimation problem for a class of discrete-time networked systems whose measurements are perturbed by random parameter matrices and time-correlated additive noise, without requiring a full knowledge of the state-space model generating the signal process, but only information about its mean and covariance functions. Assuming that the measurement additive noise is the output of a known linear systemdriven by white noise, the time-differencing method is used to remove this time-correlated noise and recursive algorithms for the linear filtering and fixed-point smoothing estimators are obtained by an innovation approach. These estimators are optimal in the least-squares sense and, consequently, their accuracy is evaluated by the estimation error covariance matrices, for which recursive formulas are also deduced. The proposed algorithms are easily implementable, as it is shown in the computer simulation example, where they are applied to estimate a signal from measured outputs which, besides including time-correlated additive noise, are affected by the missing measurement phenomenon and multiplicative noise (random uncertainties that can be covered by the current model with random parameter matrices). The computer simulations also illustrate the behaviour of the filtering estimators for different values of the missing measurement probability. ; Ministerio de Economía, Industria y Competitividad ; Agencia Estatal de Investigación ; European Union (EU) MTM201784199-P

The paper presents chosen methods for estimating the ROC (Receiver Operating Characteristic) curve, including parametric and nonparametric procedures. Nonparametric approach may involve the use of empirical method or kernel method of the ROC curve estimation. In the analysis, an attempt of comparison of empirical and kernel ROC estimators is done, considering the impact of sample size, choice of smoothing parameter and kernel function in kernel estimation on the results of the estimation. Based on the results of simulation studies, some suggestions, useful in the procedures of nonparametric ROC curve are determined.

The meso trajectory is considered as a sequential process of performing core functions on the phases of the development of the national innovation system (NIS). It constitutes a three-phase process. They describe emergence (origination), diffusion (adoption, adaptation and assimilation) and retention of a new successful rule (innovation). The study focuses on two groups of factors of these phases. The first is external factors whose action is manifested in the existence of high risks and uncertainties distributed over the stages of the meso trajectory. The second group occurs within the meso populations of the trajectory. The actors of these populations shape the group. Regarding the first group of factors, two conclusions are drawn. First, government policy that aims to mitigate inherent risks and uncertainties must be dual. It means that, on the one hand, the policy should provide compensation for a part of the uncertainties and risks inherent in innovation activity, and, on the other hand, make the actors carry some significant portion of risks themselves. Secondly, if the policy is intended for regulating the effects of externalities (e.g. technological spillover) on different phases of the trajectory, then its task is to maintain the necessary balance of their influences on NIS core functions on the adjacent phases of the trajectory. The paper introduces the concept of factor-forming populations. The action of factor-forming populations contributes to the dynamics of meso trajectories and performance of NIS functions at the phases of the trajectory. In the study, such populations are carrying the attributes of factors having a significant impact on meso trajectories. The public policy must weaken a factor-forming population with a negative impact on performing the NIS functions and reinforce those that have a positive effect on them. If factor-forming populations demonstrate both positive and negative influence on the trajectory, the policy should assist in strengthening useful parameters and smoothing ...

The surface treatment of single-crystal germanium with an argon cluster ion beam has been investigated.
The initial surface of germanium wafers was bombarded by argon cluster ions with high (105 eV/atom)
and low (10 eV/atom) specific energy. Using an atomic force microscope, images were obtained and the
surface topography was compared before and after cluster ion bombardment. Using the power spectral
density function of roughness, surface smoothing is demonstrated in the range of spatial frequencies:
1) ν = 1 − 8 µm−1 — for the high-energy mode; 2) ν = 0.7 − 2.5 µm−1 — for low-energy mode.

Social science theories often make predictions not only about the mean but also about the variance of the outcome of interest. For instance, comparative political scientists argue that democracies and non-democracies have, on average, the same rate of economic growth, but the former usually has less variance in the rate than the latter. Thus, both the mean and variance of economic performance can be functions of regime types. I review four important methods to model and estimate the error variance or its function: the naive two-stage estimation, variance function regression, joint maximum-likelihood estimation, and quantile regression (plus smoothing) estimation. Using simulated data, I compare the performance of these models when the sample size is small or large, when the variance function misspecification is mild or severe, and when the mean function is misspecified. I then apply these methods in two original studies: 1) How does county leaders' in-office time affect economic volatility in Chinese counties? 2) How does the pre-WTO economic unpredictability in Chinese municipalities affect foreign direct investment (FDI) inflows after China obtained its WTO membership in 2001?

Regarding the importance of fiscal policy in smoothing the impact of shocks such as the international financial and economic crises, the paper analyzes the sustainability of the Brazilian fiscal policy by taking into consideration the possibility of multiple endogenous structural breaks on the coefficients of government reaction function. From monthly data in the period 1991–2008, tests on the reliable estimates dictate the occurrence of structural change in May 1994, and another in February 2003. There has been a situation of fiscal solvency in Brazil, but only from May 1994 the hitherto innocuous actions of government to formulate policies on public debt turn out to be significant, as it rose twofold after February 2003. This reinforces the existence of a more flexible alternative to implement strategic policy in Brazil, if an eventual alternative for increasing public spending is a way of hindering the effects of international financial crises without compromising the fiscal targets. ; Considerando a importância da política fiscal na suavização dos impactos de choques como as crises financeiras e econômicas internacionais, o artigo analisa a sustentabilidade da política fiscal brasileira a partir da estimação de uma função de reação que permite múltiplas quebras estruturais endógenas em seus coeficientes. Os resultados evidenciam uma mudança estrutural na política fiscal brasileira a partir de maio de 1994 e outra em fevereiro de 2003. Constata-se uma situação de solvência fiscal no Brasil, mas apenas a partir de maio de 1994 a até então inócua capacidade de resposta do setor público aos aumentos da dívida pública passa a ser significativa e mais que duplica após fevereiro de 2003. Este fato reforça a existência de uma margem de manobra mais confortável aos formuladores de política no Brasil, se uma eventual opção pelo aumento do gasto público for uma alternativa obstruir os efeitos da crise financeira internacional, sem comprometer as metas fiscais.

In this paper, we propose a new method to assess the impact of sovereign ratings on sovereign bond yields. We estimate the impulse response of the interest rate, following a change in the rating. Since ratings are ordinal and moreover extremely persistent, it proves difficult to estimate those impulse response functions using a VAR modeling ratings, yields and other macroeconomic indicators. However, given the highly stochastic nature of the precise timing of ratings, we can treat most rating adjustments as shocks. We thus no longer rely on a VAR for shock identification, making the estimation of the corresponding IRFs well suited for so called local projections - that is estimating impulse response functions through a series of separate direct forecasts over different horizons. Yet, the rare occurrence of ratings makes impulse response functions estimated through that procedure highly sensitive to individual observations, resulting in implausibly volatile impulse responses. We propose an augmentation to restrict jointly estimated local projections in a way that produces economically plausible impulse response functions. We develop a semiparametric local projections method where smoothness can be imposed as constraint without assuming a specific functional form. The degree of smoothing can be assessed using a standard information criterion. While rating downgrades can play some role, we find no evidence for an impact of ratings in the most critical case of countries that are unusually well rated compared to their debt situation. Rather, ratings seem to be adjusted when the market evaluation of the risk associated with the high level of debt has already peaked.