Sentences with phrase «variables in the simple model»

One of the important variables in the simple model has become a constant and therefore vanished from an equation where it should still reside.

Not exact matches

It doesn't help that 10 - year bond yields are still lower than the prospective operating earnings yield on the S&P 500 (the «Fed Model»), not only because the model is built on an omitted variables bias (see the August 22 2005 comment), but also because the model statistically underperforms a simpler rule that says «get in when stock yields are high and interest rates are falling, and get out when the reverse is true.&rModel»), not only because the model is built on an omitted variables bias (see the August 22 2005 comment), but also because the model statistically underperforms a simpler rule that says «get in when stock yields are high and interest rates are falling, and get out when the reverse is true.&rmodel is built on an omitted variables bias (see the August 22 2005 comment), but also because the model statistically underperforms a simpler rule that says «get in when stock yields are high and interest rates are falling, and get out when the reverse is true.&rmodel statistically underperforms a simpler rule that says «get in when stock yields are high and interest rates are falling, and get out when the reverse is true.»

The graphic outlines, from left, interrelated variables in a simple statistical model, a neural network model with populations of neurons that capture the same structure, and a variant of the neural network collapsed into a more realistic overlapping configuration.

There are several variables to mix and match when developing a simple tactical asset allocation model like those detailed in Faber's book.

In more sophisticated models some of what were boundary conditions in simpler models have now become prognostic variableIn more sophisticated models some of what were boundary conditions in simpler models have now become prognostic variablein simpler models have now become prognostic variables.

I'm not an expert in economics, but from what little I understand, economics also has feedbacks and potentially unforseen variables (like innovation) that are not accounted for in the simple models.

In the present case, as C1 can be modeled using only three variables as opposed to five or more variables for the other combinations, it qualifies as the simplest combination to derive the entropy of the nth SC and is given as follows: (4)

In fact, using a simple model like this can be very informative, because there are so few variables that you can easily examine the effects of changing each one.

My idea is to take a simple sinusoidal model of a beat wave composed of 9 and 20 year cycles (the two main frequencies in the instrumental record of global temperature) and subject them to disturbances with a random variable having a standard deviation comparable to the standard deviation of monthly changes in the rate of change in global temperature.

General Introduction Two Main Goals Identifying Patterns in Time Series Data Systematic pattern and random noise Two general aspects of time series patterns Trend Analysis Analysis of Seasonality ARIMA (Box & Jenkins) and Autocorrelations General Introduction Two Common Processes ARIMA Methodology Identification Phase Parameter Estimation Evaluation of the Model Interrupted Time Series Exponential Smoothing General Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in Time Series

In step 1, age, sex, and variables that were shown to be statistically significant in simple regression analyses were simultaneously entered into the model as potential confounderIn step 1, age, sex, and variables that were shown to be statistically significant in simple regression analyses were simultaneously entered into the model as potential confounderin simple regression analyses were simultaneously entered into the model as potential confounders.