The productivity curve places Real GDP/Labor Hour (or productivity) on the y-axis and Capital per Hour of Labor on the x-axis. When physical capital (factories, equipment, etc.) increases, a country moves upward along the productivity curve. Technology advances shifts the productivity curve upward.

# CFA Exam Level 2

## Changes in Productivity: The One-Third Rule

According to the One Third Rule, the changes in productivity in the US economy can be estimated as follows: %Δ Productivity = 1/3(%Δ Physical Capital/labor hour) + %Δ Technology For example, if productivity increased by a total of 3% and the stock of physical capital increased by 3%, then one could infer that 2% of […]

## Economic Growth

The total output of an economy can be represented by a national income measure called Gross Domestic Product (GDP). This section looks at the relationship between economic output, laborer productivity and the growth of an economy’s GDP. Further discussion of GDP accounting will be presented in section titled ‘Gross Domestic Product’. Sources of Economic Growth: […]

## CFA Level 2: Economics – Introduction

CFAI understands that high quality investment advisors and portfolio managers need to have base competency in economic theory in order to value and forecast asset prices. Economic concepts in Level II focus on: economic growth, the impacts of government regulation on an economy, gross domestic product as a measurement of a country’s economic output, international […]

## Standard V – Investment Analysis, Recommendations, and Actions

This standard has two parts: A. Diligence and Reasonable Basis This standard states that the member must exercise diligence, independence, and thoroughness while analyzing investments, making recommendations, and taking investment actions. The member must also have a reasonable basis supported by research and investigation for investment analysis, recommendations, and actions. Example of Violation Example 1: […]

## How to Select the Most Appropriate Time Series Model?

Simple Linear and Exponential Growth Models – If an analyst looks at a time series plot graph he/she may see patterns exhibiting possible linear or exponential growth relationship to the dependent variable. Serial correlation of the error terms must not be present and the Durbin Watson test can test for this. Auto-Regressive Models – If […]

## ARMA Models and ARCH Testing

Autoregressive Moving Average Model (ARMA) = calculates an average value over a period of time to smooth fluctuations in a time series. ARMA models are very sensitive to minor changes and may rarely forecast well. Auto Regressive Conditional Heteroskedasticity (ARCH) testing = can be used to determine if an AR, MA, or ARMA model suffers […]

## Auto-Regressive Models – Random Walks and Unit Roots

This is the case of an AR time series model where the predicted value is expected to equal the previous period plus a random error: xt = b0 + xt-1 + εt When b0 is not equal to zero, the model is a random walk with a drift, but the key characteristic is a b1 […]

## Auto-Regressive (AR) Time Series Models

Auto-Regressive (AR) Time Series Models This type of time series model utilizes a time period lagged observation as the independent variable to predict the dependent variable, which is the value in the next time period. xt = b0 + b1xt-1 + εt There can be more than one time period lag independent variable. Valid statistical […]

## Time Series Analysis: Simple and Log-linear Trend Models

Simple Time Series Models This is basic trend modeling. A simple trend model can be expressed as follows: yt = b0 + b1t+ εt b0 = the y-intercept; where t = 0. b1 = the slope coefficient of the time trend. t = the time period. ŷt = the estimated value for time t based […]