🎳Intro to Econometrics Unit 1 – Probability & Statistics Fundamentals

Key Concepts and Definitions

• Probability measures the likelihood of an event occurring, expressed as a number between 0 and 1
  • 0 indicates an impossible event, while 1 represents a certain event
• Random variables assign numerical values to outcomes of a random experiment
  • Discrete random variables have countable outcomes (number of defective items in a batch)
  • Continuous random variables have an infinite number of possible outcomes within a range (height of students in a class)
• Probability distributions describe the likelihood of different outcomes for a random variable
  • Probability mass functions (PMFs) define probability distributions for discrete random variables
  • Probability density functions (PDFs) define probability distributions for continuous random variables
• Expected value represents the average outcome of a random variable over a large number of trials, calculated as the sum of each outcome multiplied by its probability
• Variance and standard deviation measure the spread or dispersion of a probability distribution
  • Variance is the average squared deviation from the mean, denoted as $\sigma^2$
  • Standard deviation is the square root of variance, denoted as $\sigma$
• Covariance and correlation measure the relationship between two random variables
  • Covariance indicates the direction of the linear relationship (positive, negative, or zero)
  • Correlation is a standardized measure of the linear relationship, ranging from -1 to 1

Probability Basics

• The law of large numbers states that as the number of trials increases, the average of the results will converge to the expected value
• Conditional probability is the probability of an event A occurring, given that event B has already occurred, denoted as $P(A|B)$
• The multiplication rule states that the probability of two events A and B occurring together is the product of the probability of A and the conditional probability of B given A, expressed as $P(A \cap B) = P(A) \times P(B|A)$
• Independent events have no influence on each other's probability
  • For independent events A and B, $P(A|B) = P(A)$ and $P(B|A) = P(B)$
  • The probability of independent events occurring together is the product of their individual probabilities, $P(A \cap B) = P(A) \times P(B)$
• Mutually exclusive events cannot occur at the same time
  • The probability of mutually exclusive events A and B occurring is the sum of their individual probabilities, $P(A \cup B) = P(A) + P(B)$
• The complement of an event A is the probability of A not occurring, denoted as $P(A')$ or $1 - P(A)$
• Bayes' theorem describes the probability of an event based on prior knowledge and new evidence, expressed as $P(A|B) = \frac{P(B|A) \times P(A)}{P(B)}$

Types of Distributions

• Bernoulli distribution models a single trial with two possible outcomes (success or failure)
  • The probability of success is denoted as $p$, and the probability of failure is $1-p$
• Binomial distribution models the number of successes in a fixed number of independent Bernoulli trials
  • Characterized by the number of trials $n$ and the probability of success $p$
• Poisson distribution models the number of rare events occurring in a fixed interval of time or space
  • Characterized by the average rate of occurrence $\lambda$
• Normal (Gaussian) distribution is a continuous probability distribution with a bell-shaped curve
  • Characterized by its mean $\mu$ and standard deviation $\sigma$
  • The standard normal distribution has a mean of 0 and a standard deviation of 1
• Uniform distribution has equal probability for all outcomes within a given range
  • Discrete uniform distribution has a fixed number of equally likely outcomes
  • Continuous uniform distribution has an infinite number of equally likely outcomes within a range
• Exponential distribution models the time between events in a Poisson process
  • Characterized by the rate parameter $\lambda$, which is the inverse of the mean
• Student's t-distribution is similar to the normal distribution but with heavier tails, used when the sample size is small or the population standard deviation is unknown

Descriptive Statistics

• Measures of central tendency describe the center or typical value of a dataset
  • Mean is the arithmetic average of all values in a dataset
  • Median is the middle value when the dataset is ordered from lowest to highest
  • Mode is the most frequently occurring value in a dataset
• Measures of dispersion describe the spread or variability of a dataset
  • Range is the difference between the maximum and minimum values
  • Interquartile range (IQR) is the difference between the first and third quartiles
  • Variance and standard deviation measure the average distance of data points from the mean
• Skewness measures the asymmetry of a distribution
  • Positive skewness indicates a longer right tail, while negative skewness indicates a longer left tail
• Kurtosis measures the heaviness of the tails and peakedness of a distribution compared to a normal distribution
  • Leptokurtic distributions have heavier tails and a higher peak than a normal distribution
  • Platykurtic distributions have lighter tails and a lower peak than a normal distribution
• Percentiles and quartiles divide a dataset into equal parts
  • Percentiles divide a dataset into 100 equal parts
  • Quartiles divide a dataset into four equal parts (Q1, Q2 or median, Q3)
• Boxplots visually represent the five-number summary of a dataset (minimum, Q1, median, Q3, maximum)
  • Outliers are data points that fall outside the whiskers of a boxplot

Inferential Statistics

• Hypothesis testing is a statistical method for making decisions based on sample data
  • The null hypothesis ($H_0$) represents the status quo or no effect
  • The alternative hypothesis ($H_a$ or $H_1$) represents the claim or effect being tested
• Type I error (false positive) occurs when rejecting a true null hypothesis
  • The significance level $\alpha$ is the probability of making a Type I error
• Type II error (false negative) occurs when failing to reject a false null hypothesis
  • The power of a test is the probability of correctly rejecting a false null hypothesis
• Confidence intervals estimate the range of values that likely contain the true population parameter
  • The confidence level (e.g., 95%) represents the probability that the interval contains the true value
• p-values measure the strength of evidence against the null hypothesis
  • A small p-value (typically < 0.05) indicates strong evidence against the null hypothesis
• t-tests compare means between two groups or a sample mean to a known population mean
  • Independent samples t-test compares means between two independent groups
  • Paired samples t-test compares means between two related groups or measurements
  • One-sample t-test compares a sample mean to a known population mean
• Analysis of Variance (ANOVA) tests for differences in means among three or more groups
  • One-way ANOVA compares means across one categorical variable
  • Two-way ANOVA compares means across two categorical variables and their interaction

Data Visualization Techniques

• Scatter plots display the relationship between two continuous variables
  • Each data point represents an observation with its x and y coordinates
• Line plots connect data points in order, typically used for time series data
  • Multiple line plots can be used to compare trends across different categories
• Bar plots compare values across different categories
  • Vertical bar plots (column charts) are used for categories with no natural ordering
  • Horizontal bar plots are useful when category labels are long or numerous
• Histograms display the distribution of a continuous variable
  • The x-axis is divided into bins, and the y-axis shows the frequency or count of observations in each bin
• Pie charts show the proportion or percentage of each category in a whole
  • Best used when the number of categories is small and the proportions are significantly different
• Heatmaps display values using color intensity, useful for visualizing patterns in matrices or tables
• Box plots summarize the distribution of a continuous variable across different categories
  • They display the five-number summary and any outliers
• Violin plots combine a box plot and a kernel density plot to show the distribution shape
• Faceting (small multiples) creates multiple subplots based on one or more categorical variables, allowing for comparisons across subgroups

Applications in Econometrics

• Regression analysis models the relationship between a dependent variable and one or more independent variables
  • Simple linear regression models the relationship between two continuous variables
  • Multiple linear regression models the relationship between a dependent variable and multiple independent variables
• Time series analysis studies data collected over time to identify trends, seasonality, and other patterns
  • Autoregressive (AR) models use past values of the variable to predict future values
  • Moving average (MA) models use past forecast errors to predict future values
  • Autoregressive integrated moving average (ARIMA) models combine AR and MA components and account for non-stationarity
• Panel data analysis studies data collected over time for multiple individuals, firms, or other entities
  • Fixed effects models control for unobserved, time-invariant individual characteristics
  • Random effects models assume individual-specific effects are uncorrelated with the independent variables
• Instrumental variables (IV) estimation addresses endogeneity issues when independent variables are correlated with the error term
  • Valid instruments are correlated with the endogenous variable but not with the error term
• Difference-in-differences (DID) estimation compares the change in outcomes between a treatment and control group before and after an intervention
  • Parallel trends assumption: the treatment and control groups would have followed the same trend in the absence of the intervention
• Propensity score matching (PSM) estimates the effect of a treatment by comparing treated and untreated observations with similar propensity scores
  • The propensity score is the probability of receiving the treatment based on observed characteristics

Common Pitfalls and Misconceptions

• Correlation does not imply causation: a strong correlation between two variables does not necessarily mean that one causes the other
  • Confounding variables or reverse causality may explain the observed relationship
• Outliers can heavily influence statistical measures and model results
  • It is important to identify and appropriately handle outliers based on the research context
• Overfitting occurs when a model is too complex and fits the noise in the data rather than the underlying pattern
  • Overfitted models may have poor performance on new, unseen data
• Underfitting occurs when a model is too simple and fails to capture the true relationship between variables
  • Underfitted models may have high bias and low variance
• Multicollinearity arises when independent variables in a regression model are highly correlated with each other
  • Multicollinearity can lead to unstable coefficient estimates and difficulty in interpreting individual variable effects
• Heteroscedasticity refers to the situation where the variance of the error term is not constant across observations
  • Heteroscedasticity can lead to biased standard errors and invalid inference
• Autocorrelation occurs when the error terms in a time series or panel data model are correlated with each other
  • Autocorrelation can lead to biased standard errors and inefficient coefficient estimates
• Simpson's paradox occurs when a trend or relationship observed in aggregated data disappears or reverses when the data is disaggregated by a confounding variable
  • It highlights the importance of considering subgroup effects and controlling for relevant variables