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I am Jason B. I am a Microeconomics Theory Homework Expert at economicshomeworkhelper.com. I hold a Master's in Economics, from Princeton University, USA. I have been helping students with their homework for the past 8 years. I solve homework related to Microeconomics Theory.
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I am Michael Nathan. I am a Microeconomics Theory Exam Helper at economicsexamhelp.com. I hold a MSc in Economics at Hanken School of Economics. I have been helping students with their exams for the past 10 years. You can hire me to take your exam on Microeconomics Theory Exam.
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You can also call on +1 678 648 4277 for any assistance with the Microeconomics Theory Exam Help.
This document summarizes a machine learning homework assignment with 4 problems:
1) Probability questions about constructing random variables.
2) Questions about Poisson generalized linear models (GLM) including log-likelihood, prediction, and regularization.
3) Questions comparing square loss and logistic loss for an outlier point.
4) Questions about batch normalization including its effect on model expressivity and gradients.
1) The document reviews concepts from probability and statistics including discrete and continuous random variables, their distributions (e.g. binomial, Poisson, normal), and multivariate distributions.
2) It then discusses key properties of multivariate normal distributions including their probability density function and how marginal and conditional distributions can be derived from the joint distribution.
3) Concepts like independence, mean vectors, covariance matrices, and their implications are also covered as they relate to multivariate normal distributions.
- A function is a rule that maps each input to a unique output. Not every rule defines a valid function.
- For a rule to be a valid function, it must map each input to only one output. The domain is the set of valid inputs, and the range is the set of corresponding outputs.
- Functions can be represented graphically by plotting the input-output pairs. The graph of a valid function should only intersect the vertical line above each input once.
Answers To Exercises Microeconomic Analysis Third EditionStephen Faucher
This document contains answers to exercises from the book "Microeconomic Analysis" by Hal R. Varian. It provides detailed solutions to exercises from chapters 1-4 on technology, profit maximization, the profit function, and cost minimization. The answers reference concepts from the text like production functions, factor demands, isoquants, and first-order conditions to derive optimal solutions for the various problems posed.
This document discusses moment-generating functions and their properties and applications in probability theory. It defines the moment-generating function for both discrete and continuous random variables. Some key properties of moment-generating functions are that each probability distribution has a unique moment-generating function, and they can be used to find the distribution of sums of random variables. The document also describes several common probability distributions and derives their corresponding moment-generating functions.
Moment-Generating Functions and Reproductive Properties of DistributionsIJSRED
The document discusses moment-generating functions and their applications in probability theory. It defines the moment-generating function for both discrete and continuous random variables. Some key properties and examples of moment-generating functions for common distributions like binomial, Poisson, exponential, normal and gamma are provided. Reproductive properties of distributions are also described briefly.
I am Jason B. I am a Microeconomics Theory Homework Expert at economicshomeworkhelper.com. I hold a Master's in Economics, from Princeton University, USA. I have been helping students with their homework for the past 8 years. I solve homework related to Microeconomics Theory.
Visit economicshomeworkhelper.com or email info@economicshomeworkhelper.com.
You can also call on +1 678 648 4277 for any assistance with Microeconomics Theory Homework.
I am Michael Nathan. I am a Microeconomics Theory Exam Helper at economicsexamhelp.com. I hold a MSc in Economics at Hanken School of Economics. I have been helping students with their exams for the past 10 years. You can hire me to take your exam on Microeconomics Theory Exam.
Visit economicsexamhelp.com or email support@economicsexamhelp.com
You can also call on +1 678 648 4277 for any assistance with the Microeconomics Theory Exam Help.
This document summarizes a machine learning homework assignment with 4 problems:
1) Probability questions about constructing random variables.
2) Questions about Poisson generalized linear models (GLM) including log-likelihood, prediction, and regularization.
3) Questions comparing square loss and logistic loss for an outlier point.
4) Questions about batch normalization including its effect on model expressivity and gradients.
1) The document reviews concepts from probability and statistics including discrete and continuous random variables, their distributions (e.g. binomial, Poisson, normal), and multivariate distributions.
2) It then discusses key properties of multivariate normal distributions including their probability density function and how marginal and conditional distributions can be derived from the joint distribution.
3) Concepts like independence, mean vectors, covariance matrices, and their implications are also covered as they relate to multivariate normal distributions.
- A function is a rule that maps each input to a unique output. Not every rule defines a valid function.
- For a rule to be a valid function, it must map each input to only one output. The domain is the set of valid inputs, and the range is the set of corresponding outputs.
- Functions can be represented graphically by plotting the input-output pairs. The graph of a valid function should only intersect the vertical line above each input once.
Answers To Exercises Microeconomic Analysis Third EditionStephen Faucher
This document contains answers to exercises from the book "Microeconomic Analysis" by Hal R. Varian. It provides detailed solutions to exercises from chapters 1-4 on technology, profit maximization, the profit function, and cost minimization. The answers reference concepts from the text like production functions, factor demands, isoquants, and first-order conditions to derive optimal solutions for the various problems posed.
This document discusses moment-generating functions and their properties and applications in probability theory. It defines the moment-generating function for both discrete and continuous random variables. Some key properties of moment-generating functions are that each probability distribution has a unique moment-generating function, and they can be used to find the distribution of sums of random variables. The document also describes several common probability distributions and derives their corresponding moment-generating functions.
Moment-Generating Functions and Reproductive Properties of DistributionsIJSRED
The document discusses moment-generating functions and their applications in probability theory. It defines the moment-generating function for both discrete and continuous random variables. Some key properties and examples of moment-generating functions for common distributions like binomial, Poisson, exponential, normal and gamma are provided. Reproductive properties of distributions are also described briefly.
This document summarizes the key concepts and results regarding optimal stopping time in stochastic calculus. It begins with introducing the problem of finding the optimal time to sell an asset whose price follows a stochastic process. It then discusses several approaches to solving this problem, including using supermean and excessive functions, the least superharmonic majorant, and the Snell envelope. The document also draws a link between optimal stopping problems and stochastic control problems. It concludes by characterizing the optimal stopping time using the Snell envelope and conditions on the stopped process being both a martingale and equal to the original process.
I am Bon Leofen Currently associated with economicshomeworkhelper.com as an economics homework helper. After completing my master's at Ambrose University, I was in search of an opportunity that would expand my area of knowledge hence I decided to help students with their assignments. I have written several economics assignments to date to help students overcome numerous difficulties they face.
1) Machine learning draws on areas of mathematics including probability, statistical inference, linear algebra, and optimization theory.
2) While there are easy-to-use machine learning packages, understanding the underlying mathematics is important for choosing the right algorithms, making good parameter and validation choices, and interpreting results.
3) Key concepts in probability and statistics that are important for machine learning include random variables, probability distributions, expected value, variance, covariance, and conditional probability. These concepts allow quantification of relationships and uncertainties in data.
- A function is a rule that maps an input number (independent variable) to a unique output number (dependent variable).
- To determine if a rule describes a valid function, you can plot points from the rule on a graph and check that each input only maps to one output using a vertical ruler.
- For a rule to describe a valid function, its domain must be restricted if multiple outputs are possible for any single input. The domain is the set of possible inputs, and the range is the set of corresponding outputs.
- A function is a rule that maps an input number (independent variable) to a unique output number (dependent variable).
- To determine if a rule describes a valid function, you can plot points from the rule on a graph and check that each input only maps to one output using a vertical ruler.
- For a rule to describe a valid function, its domain must be restricted if multiple outputs are possible for any single input. The domain is the set of possible inputs, and the range is the set of corresponding outputs.
Banque de France's Workshop on Granularity: François Geerolf's slides, June 2...Soledad Zignago
A Static Theory of Pareto Distributions, by François Geerolf(UCLA), presented at the Banque de France and Sciences Po workshop on Granularity of Macroeconomics Fluctuations, 24 June 2016. Slides of presentations & discussions are available online: https://www.banque-france.fr/en/economics-statistics/research/seminars-and-symposiums/research-workshop-on-the-granularity-of-macroeconomic-fluctuations-where-do-we-stand.html
A function relates variables, with one as the input and another as the output. Operations on functions include addition, subtraction, multiplication, and division. For addition and subtraction, the functions are simply added or subtracted term by term. Multiplication multiplies all terms of one function with the other. Division divides one function by the other term by term. Examples are provided to demonstrate each type of operation.
This document discusses polynomial functions in MATLAB. It covers:
- Defining polynomials as coefficient vectors and finding roots.
- Adding, subtracting, multiplying and dividing polynomials using functions like conv and deconv.
- Evaluating and differentiating polynomials with polyval and polyder.
- Using polyfit for polynomial curve fitting to minimize squared errors between a polynomial and data set.
- An example of fitting increasing degree polynomials from 2 to 8 to cosine wave data, showing better fitting with higher degrees.
This document discusses three methods for finding the roots of nonlinear equations:
1) Bisection method, which converges linearly but is guaranteed to find a root.
2) Newton's method, which converges quadratically (much faster) but may diverge if the starting point is too far from the root.
3) Secant method, which is faster than bisection but slower than Newton's, and also requires starting points close to the root. Newton's and secant methods can be extended to systems of nonlinear equations.
Properties of Functions
Odd and Even Functions
Periodic Functions
Monotonic Functions
Bounded Functions
Maxima and Minima of Functions
Inverse Function
Sequence and Series
A very wide spectrum of optimization problems can be efficiently solved with proximal gradient methods which hinge on the celebrated forward-backward splitting (FBS) schema. But such first-order methods are only effective when low or medium accuracy is required and are known to be rather slow or even impractical for badly conditioned problems. Moreover, the straightforward introduction of second-order (Hessian) information is beset with shortcomings as, typically, at every iteration we need to solve a non-separable optimisation problem. In this talk we will follow a different route to the solution of such optimisation problems. We will recast non-smooth optimisation problems as the minimisation of a real-valued, continuously differentiable function known as the forward-backward envelope. We will then employ a semismooth Newton method to solve the equivalent optimisation problem instead of the original one. We will then apply the proposed semismooth Newton method to L1-regularised least squares (LASSO) problems which is motivated by an an interesting application: recursive compressed sensing. Compressed sensing is a signal processing methodology for the reconstruction of sparsely sampled signals and it offers a new paradigm for sampling signals based on their innovation, that is, the minimum number of coefficients sufficient to accurately represent it in an appropriately selected basis. Compressed sensing leads to a lower sampling rate compared to theories using some fixed basis and has many applications in image processing, medical imaging and MRI, photography, holography, facial recognition, radio astronomy, radar technology and more. The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed; the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. We will see how we can tailor the forward-backward Newton method to solve recursive compressed sensing problems at one tenth of the time required by other algorithms such as ISTA, FISTA, ADMM and interior-point methods (L1LS).
This document discusses inverse functions, including exponential, logarithmic, and inverse trigonometric functions. It begins by defining an inverse function as two functions f and g where g(f(x)) = x and f(g(y)) = y. It then discusses how to find the inverse of a function by solving an equation like y = f(x) for x in terms of y. For a function to have an inverse, it must assign distinct outputs to distinct inputs. The document provides examples of finding inverses and discusses domains, ranges, and interpretations of inverse functions.
The document discusses input and output statements in C++. It explains that the iostream library includes cout and cin for standard output and input. cout uses the insertion operator << to output data to the screen, while cin uses the extraction operator >> to input data from the keyboard. The document provides examples of using cout and cin to output text, numbers, and calculate values from user input.
This document discusses graphs of polynomial functions. It defines a polynomial function as a function of the form f(x) = anxn + an-1xn-1 + ... + a1x + a0, where n is a nonnegative integer and each ai is a real number. The degree of the polynomial is n and the leading coefficient is an. Polynomial graphs are continuous and smooth without breaks or cusps. The behavior of graphs as x approaches positive or negative infinity depends on the sign of the leading coefficient and whether the degree is odd or even. Polynomials can have real zeros where they intersect the x-axis. Their graphs may have up to n turning points and n zeros.
INDIRECT UTILITY FUNCTION AND ROY’S IDENTITIY by Maryam LoneSAMEENALONE2
- Utility is a measure of satisfaction derived from consuming goods and services. Individuals seek to maximize their utility subject to a budget constraint.
- Indifference curves represent combinations of goods that provide equal utility. The slope of the indifference curve is the marginal rate of substitution (MRS).
- The budget constraint shows affordable combinations given prices and income. Utility is maximized at the point where the MRS equals the price ratio, where the indifference curve is tangent to the budget constraint.
- Using tools like Lagrangian optimization and the envelope theorem, the amounts demanded of each good can be derived as functions of prices and income.
The document provides an overview of key concepts in calculus limits including:
1) Limits describe the behavior of a function as its variable approaches a constant value.
2) Tables of values and graphs can be used to evaluate limits by showing how the function values change as the variable nears the constant.
3) Common limit laws are described such as addition, multiplication, and substitution which allow evaluating limits of combined functions.
This document provides instructions on how to evaluate functions by substituting values for variables and simplifying expressions using order of operations. It gives examples of evaluating various functions, such as f(x)=10+6x^2, by substituting the given value for x and simplifying. The key steps are to substitute the input value for the variable, then simplify the resulting expression using rules like PEMDAS. Floor and ceiling functions are also explained. Practice problems are provided to evaluate functions like f(x)=-5+3x for given x values.
Lesson 4- Math 10 - W4Q1_General Form of a Polynomial Function.pptxErlenaMirador1
The document discusses polynomial functions. It defines polynomials as algebraic expressions consisting of terms in the form axn where a is a real number and n is a non-negative integer. The degree of a polynomial refers to the highest exponent of its terms. A polynomial function P(x) is written as P(x) = anxn + an-1xn-1 + ... + a2x2 + a1x + a0 where n is the degree. The document provides examples of finding the degree of polynomials and performing operations like addition and multiplication on polynomial functions. It also demonstrates using synthetic division to divide one polynomial by another.
Welcome to our SlideShare channel, where we explore the ever-evolving world of economics and offer valuable insights into the economic challenges and opportunities of 2023 and beyond.
🌍 What's Trending in Economics? - Stay ahead of the curve by discovering the latest trends and emerging economic issues. From inflation and supply chain disruptions to sustainable economic growth, we'll keep you informed.
💡 Expert Analysis - Our team of experienced economists and analysts will provide in-depth analyses of key economic topics. Get ready to dive deep into economic theories, policy changes, and their real-world impact.
📈 Data-driven Insights - We love data, and we'll show you why. Our presentations will include charts, graphs, and data visualizations to help you better understand complex economic concepts.
📚 Academic Excellence - If you're a student looking for economics homework help, you're in the right place. We offer expert assistance for your economics assignments and projects.
🌐 Global Perspective - Economics is a global discipline, and we'll explore economic issues from around the world. Whether it's the economic implications of a global event or regional economic disparities, we've got it covered.
🤝 Join the Discussion - Economics is a dynamic field that thrives on discussion and debate. We encourage you to engage with our content, ask questions, and share your thoughts. Your insights are valuable to us!
🚀 Explore Our Website - Ready to take your economics knowledge to the next level? Visit our website at EconomicsHomeworkHelper.com for expert assistance, resources, and more.
🔗 Connect with Us - Follow us on SlideShare to stay updated with our latest presentations and connect with us on social media for even more economics insights.
More Related Content
Similar to Best Microeconomics Assignment Help by the Leading Experts.pptx
This document summarizes the key concepts and results regarding optimal stopping time in stochastic calculus. It begins with introducing the problem of finding the optimal time to sell an asset whose price follows a stochastic process. It then discusses several approaches to solving this problem, including using supermean and excessive functions, the least superharmonic majorant, and the Snell envelope. The document also draws a link between optimal stopping problems and stochastic control problems. It concludes by characterizing the optimal stopping time using the Snell envelope and conditions on the stopped process being both a martingale and equal to the original process.
I am Bon Leofen Currently associated with economicshomeworkhelper.com as an economics homework helper. After completing my master's at Ambrose University, I was in search of an opportunity that would expand my area of knowledge hence I decided to help students with their assignments. I have written several economics assignments to date to help students overcome numerous difficulties they face.
1) Machine learning draws on areas of mathematics including probability, statistical inference, linear algebra, and optimization theory.
2) While there are easy-to-use machine learning packages, understanding the underlying mathematics is important for choosing the right algorithms, making good parameter and validation choices, and interpreting results.
3) Key concepts in probability and statistics that are important for machine learning include random variables, probability distributions, expected value, variance, covariance, and conditional probability. These concepts allow quantification of relationships and uncertainties in data.
- A function is a rule that maps an input number (independent variable) to a unique output number (dependent variable).
- To determine if a rule describes a valid function, you can plot points from the rule on a graph and check that each input only maps to one output using a vertical ruler.
- For a rule to describe a valid function, its domain must be restricted if multiple outputs are possible for any single input. The domain is the set of possible inputs, and the range is the set of corresponding outputs.
- A function is a rule that maps an input number (independent variable) to a unique output number (dependent variable).
- To determine if a rule describes a valid function, you can plot points from the rule on a graph and check that each input only maps to one output using a vertical ruler.
- For a rule to describe a valid function, its domain must be restricted if multiple outputs are possible for any single input. The domain is the set of possible inputs, and the range is the set of corresponding outputs.
Banque de France's Workshop on Granularity: François Geerolf's slides, June 2...Soledad Zignago
A Static Theory of Pareto Distributions, by François Geerolf(UCLA), presented at the Banque de France and Sciences Po workshop on Granularity of Macroeconomics Fluctuations, 24 June 2016. Slides of presentations & discussions are available online: https://www.banque-france.fr/en/economics-statistics/research/seminars-and-symposiums/research-workshop-on-the-granularity-of-macroeconomic-fluctuations-where-do-we-stand.html
A function relates variables, with one as the input and another as the output. Operations on functions include addition, subtraction, multiplication, and division. For addition and subtraction, the functions are simply added or subtracted term by term. Multiplication multiplies all terms of one function with the other. Division divides one function by the other term by term. Examples are provided to demonstrate each type of operation.
This document discusses polynomial functions in MATLAB. It covers:
- Defining polynomials as coefficient vectors and finding roots.
- Adding, subtracting, multiplying and dividing polynomials using functions like conv and deconv.
- Evaluating and differentiating polynomials with polyval and polyder.
- Using polyfit for polynomial curve fitting to minimize squared errors between a polynomial and data set.
- An example of fitting increasing degree polynomials from 2 to 8 to cosine wave data, showing better fitting with higher degrees.
This document discusses three methods for finding the roots of nonlinear equations:
1) Bisection method, which converges linearly but is guaranteed to find a root.
2) Newton's method, which converges quadratically (much faster) but may diverge if the starting point is too far from the root.
3) Secant method, which is faster than bisection but slower than Newton's, and also requires starting points close to the root. Newton's and secant methods can be extended to systems of nonlinear equations.
Properties of Functions
Odd and Even Functions
Periodic Functions
Monotonic Functions
Bounded Functions
Maxima and Minima of Functions
Inverse Function
Sequence and Series
A very wide spectrum of optimization problems can be efficiently solved with proximal gradient methods which hinge on the celebrated forward-backward splitting (FBS) schema. But such first-order methods are only effective when low or medium accuracy is required and are known to be rather slow or even impractical for badly conditioned problems. Moreover, the straightforward introduction of second-order (Hessian) information is beset with shortcomings as, typically, at every iteration we need to solve a non-separable optimisation problem. In this talk we will follow a different route to the solution of such optimisation problems. We will recast non-smooth optimisation problems as the minimisation of a real-valued, continuously differentiable function known as the forward-backward envelope. We will then employ a semismooth Newton method to solve the equivalent optimisation problem instead of the original one. We will then apply the proposed semismooth Newton method to L1-regularised least squares (LASSO) problems which is motivated by an an interesting application: recursive compressed sensing. Compressed sensing is a signal processing methodology for the reconstruction of sparsely sampled signals and it offers a new paradigm for sampling signals based on their innovation, that is, the minimum number of coefficients sufficient to accurately represent it in an appropriately selected basis. Compressed sensing leads to a lower sampling rate compared to theories using some fixed basis and has many applications in image processing, medical imaging and MRI, photography, holography, facial recognition, radio astronomy, radar technology and more. The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed; the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. We will see how we can tailor the forward-backward Newton method to solve recursive compressed sensing problems at one tenth of the time required by other algorithms such as ISTA, FISTA, ADMM and interior-point methods (L1LS).
This document discusses inverse functions, including exponential, logarithmic, and inverse trigonometric functions. It begins by defining an inverse function as two functions f and g where g(f(x)) = x and f(g(y)) = y. It then discusses how to find the inverse of a function by solving an equation like y = f(x) for x in terms of y. For a function to have an inverse, it must assign distinct outputs to distinct inputs. The document provides examples of finding inverses and discusses domains, ranges, and interpretations of inverse functions.
The document discusses input and output statements in C++. It explains that the iostream library includes cout and cin for standard output and input. cout uses the insertion operator << to output data to the screen, while cin uses the extraction operator >> to input data from the keyboard. The document provides examples of using cout and cin to output text, numbers, and calculate values from user input.
This document discusses graphs of polynomial functions. It defines a polynomial function as a function of the form f(x) = anxn + an-1xn-1 + ... + a1x + a0, where n is a nonnegative integer and each ai is a real number. The degree of the polynomial is n and the leading coefficient is an. Polynomial graphs are continuous and smooth without breaks or cusps. The behavior of graphs as x approaches positive or negative infinity depends on the sign of the leading coefficient and whether the degree is odd or even. Polynomials can have real zeros where they intersect the x-axis. Their graphs may have up to n turning points and n zeros.
INDIRECT UTILITY FUNCTION AND ROY’S IDENTITIY by Maryam LoneSAMEENALONE2
- Utility is a measure of satisfaction derived from consuming goods and services. Individuals seek to maximize their utility subject to a budget constraint.
- Indifference curves represent combinations of goods that provide equal utility. The slope of the indifference curve is the marginal rate of substitution (MRS).
- The budget constraint shows affordable combinations given prices and income. Utility is maximized at the point where the MRS equals the price ratio, where the indifference curve is tangent to the budget constraint.
- Using tools like Lagrangian optimization and the envelope theorem, the amounts demanded of each good can be derived as functions of prices and income.
The document provides an overview of key concepts in calculus limits including:
1) Limits describe the behavior of a function as its variable approaches a constant value.
2) Tables of values and graphs can be used to evaluate limits by showing how the function values change as the variable nears the constant.
3) Common limit laws are described such as addition, multiplication, and substitution which allow evaluating limits of combined functions.
This document provides instructions on how to evaluate functions by substituting values for variables and simplifying expressions using order of operations. It gives examples of evaluating various functions, such as f(x)=10+6x^2, by substituting the given value for x and simplifying. The key steps are to substitute the input value for the variable, then simplify the resulting expression using rules like PEMDAS. Floor and ceiling functions are also explained. Practice problems are provided to evaluate functions like f(x)=-5+3x for given x values.
Lesson 4- Math 10 - W4Q1_General Form of a Polynomial Function.pptxErlenaMirador1
The document discusses polynomial functions. It defines polynomials as algebraic expressions consisting of terms in the form axn where a is a real number and n is a non-negative integer. The degree of a polynomial refers to the highest exponent of its terms. A polynomial function P(x) is written as P(x) = anxn + an-1xn-1 + ... + a2x2 + a1x + a0 where n is the degree. The document provides examples of finding the degree of polynomials and performing operations like addition and multiplication on polynomial functions. It also demonstrates using synthetic division to divide one polynomial by another.
Similar to Best Microeconomics Assignment Help by the Leading Experts.pptx (20)
Welcome to our SlideShare channel, where we explore the ever-evolving world of economics and offer valuable insights into the economic challenges and opportunities of 2023 and beyond.
🌍 What's Trending in Economics? - Stay ahead of the curve by discovering the latest trends and emerging economic issues. From inflation and supply chain disruptions to sustainable economic growth, we'll keep you informed.
💡 Expert Analysis - Our team of experienced economists and analysts will provide in-depth analyses of key economic topics. Get ready to dive deep into economic theories, policy changes, and their real-world impact.
📈 Data-driven Insights - We love data, and we'll show you why. Our presentations will include charts, graphs, and data visualizations to help you better understand complex economic concepts.
📚 Academic Excellence - If you're a student looking for economics homework help, you're in the right place. We offer expert assistance for your economics assignments and projects.
🌐 Global Perspective - Economics is a global discipline, and we'll explore economic issues from around the world. Whether it's the economic implications of a global event or regional economic disparities, we've got it covered.
🤝 Join the Discussion - Economics is a dynamic field that thrives on discussion and debate. We encourage you to engage with our content, ask questions, and share your thoughts. Your insights are valuable to us!
🚀 Explore Our Website - Ready to take your economics knowledge to the next level? Visit our website at EconomicsHomeworkHelper.com for expert assistance, resources, and more.
🔗 Connect with Us - Follow us on SlideShare to stay updated with our latest presentations and connect with us on social media for even more economics insights.
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The document provides information about a program called Programa Escuelas de Calidad (PEC) instituted in Mexico in 2001 that provided grants and training to primary schools. It describes a dataset on dropout rates of 74,701 primary schools from 2000-2001 to 2003-2004, including schools that enrolled in PEC. It asks a series of questions about how to estimate the causal effect of PEC on dropout rates using this data, and discusses the strengths and weaknesses of different approaches such as simple before-after comparisons or comparing enrolled vs. non-enrolled schools. The best approach is determined to be a differences-in-differences analysis comparing changes in dropout rates over time between schools that did and did not enroll in P
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*Show that the monotone likelihood ratio condition (f(x)/g(x) increasing in x,
where x is a real number) implies first-order stochastic dominance (F(x) £ G(x) for
all x).
Let q = q1 or q2 denote the state of nature in a two state world. Let p0 = Pr(q = q1).
Let f(p) be an arbitrary distribution on the unit interval such that òpf(p)dp = p0.
Show that f(p) can be construed as a distribution over posterior probabilities of p,
stemming from an experiment with outcomes y, a prior probability p = p0 and two
likelihood functions p(y|q1) and p(y|q2). [It may be easier to solve the problem if
you assume that f(p) consists of two mass points so that the experiment will have
only two relevant outcomes, say, yL and yH.]
An agent has to decide between two actions a1 and a2, uncertain of the prevailing
state of nature s, which can be either s1 or s2. The agent’s payoff as a function of the
action and the state of nature is as follows:
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Give a graphical representation of the agent’s decision problem as a function
of the probability of state s1. If p = (.4), what is the agent’s optimal decision?
Let the agent have access to a signal y before taking an action. Assume y has
two possible outcomes with the following likelihoods
*An agent can work hard (e = eH) or be lazy (e = eL), where eH > eL > 0. There are
two profit levels, x1 and x2; x1 < x2. Hard work makes the high profit level more
likely. Specifically, Prob(x = x2) is fH if the agent works hard and fL < fH, if the agent
is lazy.
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The principal can only observe realized profits x. The principal is risk neutral and
the agent is risk averse with preferences u(w) - e. The utility function u is strictly
concave and increasing.
Assume that the Principal’s reservation utility is 0; that is, the Principal has to
be assured a non-negative profit. Show how to solve for the Pareto Optimal
action and incentive scheme
s(xi) = si, i = 1,2, where si represents the payment to the agent if the outcome is xi.
Suppose that the agent is given the opportunity to choose a third action eM
with the features that Prob(x2|eM) = ½fH + ½fL and eM > ½eH + ½eL. Can the
Principal implement eM?
Suppose that the second-best solution in Part a is such that it is optimal to
implement eH. Let the agent have access to the action eM described above, but
assume now that eM < ½eH + ½eL. Will the solution to Part a stand once the
agent is given access to eM?
Consider the risk-sharing problem we saw in class. Show that the Pareto frontier is
concave (in expected utility space), or, equivalently, that the feasible set of utility
pairs is convex.