PDF
... Seasonal data are widely used in time-series analysis, usually at a quarterly or monthly frequency. Some seasonal data have significant seasonal fluctuations, such as data on retail sales, travel, and electricity usage. Seasonality brings many difficulties to model specification, estimation, and inferenc ...
... Seasonal data are widely used in time-series analysis, usually at a quarterly or monthly frequency. Some seasonal data have significant seasonal fluctuations, such as data on retail sales, travel, and electricity usage. Seasonality brings many difficulties to model specification, estimation, and inferenc ...
Spatial Statistics in Econometrics
... Test for spatial autocorrelation • One-sided parametric hypotheses about the spatial autocorrelation level ρ H0: ρ ≤ 0 against H1: ρ > 0 for positive spatial autocorrelation. H0: ρ ≥ 0 against H1: ρ < 0 for negative spatial autocorrelation. • Inference for Moran’s I is usually based on a normal app ...
... Test for spatial autocorrelation • One-sided parametric hypotheses about the spatial autocorrelation level ρ H0: ρ ≤ 0 against H1: ρ > 0 for positive spatial autocorrelation. H0: ρ ≥ 0 against H1: ρ < 0 for negative spatial autocorrelation. • Inference for Moran’s I is usually based on a normal app ...
Model Uncertainty in Panel Vector Autoregressive Models
... matrix between the errors in the VARs of country i and country j. We refer to this specification as the unrestricted PVAR. Note that the unrestricted PVAR is very general and that lagged variables from any country can influence any other country (e.g. lagged values of country 1 variables can impact ...
... matrix between the errors in the VARs of country i and country j. We refer to this specification as the unrestricted PVAR. Note that the unrestricted PVAR is very general and that lagged variables from any country can influence any other country (e.g. lagged values of country 1 variables can impact ...
Beating Human Analysts in Nowcasting Corporate Earnings by
... to the learner in order of their arrival together with their now known label. Because the intervals between earnings announcement are fixed, this method only adds a constant factor to the space complexity of the algorithm. For each point in time t ∈ N, a standard online regression algorithm estimate ...
... to the learner in order of their arrival together with their now known label. Because the intervals between earnings announcement are fixed, this method only adds a constant factor to the space complexity of the algorithm. For each point in time t ∈ N, a standard online regression algorithm estimate ...
Empirical Estimation of the Solow Growth Model: A Panel Approach
... along with the single cross-section Islam (1995) implemented a panel framework using the same sample as MRW. His research highlighted lower the value of output elasticity with respect to capital, the higher the rates of conditional convergence. After controlling for country specific effects in panel ...
... along with the single cross-section Islam (1995) implemented a panel framework using the same sample as MRW. His research highlighted lower the value of output elasticity with respect to capital, the higher the rates of conditional convergence. After controlling for country specific effects in panel ...
Management & Engineering Executives Owning Shareholdings
... 5.4 The relationship between the rate of assets and liabilities and earnings manipulation According to the empirical analysis, there’s negative correlation between the rate of assets and liabilities and earnings manipulation where executives hold actual stocks. It is not consistent with the foregoin ...
... 5.4 The relationship between the rate of assets and liabilities and earnings manipulation According to the empirical analysis, there’s negative correlation between the rate of assets and liabilities and earnings manipulation where executives hold actual stocks. It is not consistent with the foregoin ...
PDF
... model where a large proportion of the dependent variable is zero (Deaton 1986, Greene 1993). The problem arises as households participating in the survey do not report or consume all types of food products during the survey period. While several econometric approaches have been proposed to deal with ...
... model where a large proportion of the dependent variable is zero (Deaton 1986, Greene 1993). The problem arises as households participating in the survey do not report or consume all types of food products during the survey period. While several econometric approaches have been proposed to deal with ...
Hydra-MIP: Automated Algorithm Configuration and Selection for
... We constructed cost-sensitive DFs as collections of T cost-sensitive decision trees [27]. Following [4], given n training data points with k features each, for each tree we construct a bootstrap sample of n training data points sampled uniformly at random with repetitions; during tree construction, ...
... We constructed cost-sensitive DFs as collections of T cost-sensitive decision trees [27]. Following [4], given n training data points with k features each, for each tree we construct a bootstrap sample of n training data points sampled uniformly at random with repetitions; during tree construction, ...
Model Selection and Adaptation of Hyperparameters
... In chapters 2 and 3 we have seen how to do regression and classification using a Gaussian process with a given fixed covariance function. However, in many practical applications, it may not be easy to specify all aspects of the covariance function with confidence. While some properties such as stati ...
... In chapters 2 and 3 we have seen how to do regression and classification using a Gaussian process with a given fixed covariance function. However, in many practical applications, it may not be easy to specify all aspects of the covariance function with confidence. While some properties such as stati ...
PDF
... wholesale quantity equal to one pound of retail cuts and farm price is the market value to producers equal to one pound of retail cuts. Overall, this procedure permits describing relationships among primary and derived demands, marketing margins, and market-level prices on a single price-quantity gr ...
... wholesale quantity equal to one pound of retail cuts and farm price is the market value to producers equal to one pound of retail cuts. Overall, this procedure permits describing relationships among primary and derived demands, marketing margins, and market-level prices on a single price-quantity gr ...
Linear regression
In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. (This term should be distinguished from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.)In linear regression, data are modeled using linear predictor functions, and unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly, linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X, which is the domain of multivariate analysis.Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.Linear regression has many practical uses. Most applications fall into one of the following two broad categories: If the goal is prediction, or forecasting, or error reduction, linear regression can be used to fit a predictive model to an observed data set of y and X values. After developing such a model, if an additional value of X is then given without its accompanying value of y, the fitted model can be used to make a prediction of the value of y. Given a variable y and a number of variables X1, ..., Xp that may be related to y, linear regression analysis can be applied to quantify the strength of the relationship between y and the Xj, to assess which Xj may have no relationship with y at all, and to identify which subsets of the Xj contain redundant information about y.Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the ""lack of fit"" in some other norm (as with least absolute deviations regression), or by minimizing a penalized version of the least squares loss function as in ridge regression (L2-norm penalty) and lasso (L1-norm penalty). Conversely, the least squares approach can be used to fit models that are not linear models. Thus, although the terms ""least squares"" and ""linear model"" are closely linked, they are not synonymous.