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28 Cards in this Set
- Front
- Back
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secular, cyclical, seasonal, random
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components of a time-series that can cause variations
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specification and measurement error
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forgetting a key variable, get strange results
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Contribution Margin
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difference between selling price and variable cost
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linear and growth model forecasting
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used when trend has a constant amount of change
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implicit costs
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opportunity costs of time and capital invested
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naïve forecasting
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best used when there is no trend
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R-squared
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coefficient of determination, choose highest, explains variation in dependent variable
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s-shaped, cubic
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shape of short-run production function
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first-order exponential smoothing
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smoothing using geometrically declining weights with a forecast as a function of all past observations
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Seasonal forecasting
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controlled for by using ratio-to-trend and dummy variables
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Multicollinearity
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two or more independent variables are highly correlated
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Survivor analysis
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analyzes firms size where what sizes are succeeding or failing
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time-series forecasting models
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based on historical observations
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long-run production function
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all inputs are considered variable
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diseconomies of scale
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costs increase as output increase
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Methodology
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Minimize the sum of squared error terms
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lagging, leading, coincident
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barometric indicators
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L-shaped
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isoquants for inputs that are perfect complements have this shape
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f - test
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tests for how well the data fit the model
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sunk costs
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pay regardless of whether or not a firm goes forward with a new decision
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RMSE
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shows accuracy of model in forecasting, choose lowest
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the existence of a significant pattern in successive values of the error term
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autocorrelation
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Cobb-Douglas production function
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exponents are elasticites and can measure returns to scale
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Marginal Product (of L or K)
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change in total output produced by one more unit of variable input
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short-run production function
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one or more inputs, usually capital are fixed
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moving averages
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smoothing used for data with no trend
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Heteroscedasticity
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different variances for different sub-samples
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t - test
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Tests for statistical significance of independent variables
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