Question 1

  1. Suppose that Germany and Italy have the factor endowments given in the table below:

    Germany Italy
    Capital 80 machines 20 machines
    Labor 400 workers 120 workers

    Suppose further that the production requirements for a unit of manufacturing goods is two machines and eight workers, and the requirement for a unit of agricultural goods is one machine and eight workers.

    Which good, manufacturing or agricultural, is relatively intensive in the use of capital? In labor? Show how you know. Which country would export agricultural goods? Why?

    Your response should be at least 200 words in length.

 

Question 2

  1. Suppose that there are two countries (Germany and Italy) two goods (computers and food) and two factors of production (skilled labor and unskilled labor). Also, assume that Germany is skilled-labor abundant while Italy is unskilled-labor abundant. Suppose further that computers are skilled-labor intensive and food is unskilled-labor intensive. What will happen to the wage of skilled labor relative to the wage of unskilled labor in each country following trade liberalization? Explain.

    Your response should be at least 200 words in length.

 

 

Question 3

  1. Suppose that before trade opens up, Brazil is at a point on its production possibility curve (PPC) where it produces 20 apples and 20 cars. Once trade opens up, the price of a car becomes two apples. In response, Brazil moves along its PPC to a new point where it is producing 30 cars and 10 apples. Is Brazil now better off? Explain.

    Your response should be at least 200 words in length.

 

 

Question 4

  1. Suppose that New Zealand receives an inflow of foreign direct investment (FDI). Additionally, assume that labor and capital are used in the production of corn and steel. Suppose that steel is capital intensive as compared with corn. Using the long-run specific-factors model, explain what happens to the output of each good.

    Your response should be at least 200 words in length.

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Problem Description The MNIST database of handwritten digits (from 0 to 9) has a training set of 55,000 examples, and a test set of 10,000 examples. The digits have been size-normalized and centered in a fixed-size image (28×28 pixels) with values from 0 to 1. You can use the following code with TensorFlow in Python to download the data. from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() Every MNIST data point has two parts: an image of a handwritten digit and a corresponding label. We will call the images ? and the labels ?. Both the training set and test set contain ? and ?. Each image is 28 pixels by 28 pixels and can be flattened into a vector of 28×28 = 784 numbers. As mentioned, the corresponding labels in the MNIST are numbers between 0 and 9, describing which digit a given image is of. In this assignment, we regard the labels as one-hot vectors, i.e. 0 in most dimensions, and 1 in a single dimension. In this case, the ?-th digit will be represented as a vector which is 1 in the ? dimensions. For example, 3 would be [0,0,0,1,0,0,0,0,0,0]. The assignment aims to build NNs for classifying handwritten digits in the MNIST database, train it on the training set and test it on the test set. Please read the following comments and requirements very carefully before starting the assignment: 1. The assignment is based on the content of Labs. 2. In Lecture 1, we talked about the use of training set, validation set and test set in machine learning. In the assignment, you are asked to train the NN on the training set and test the NN on the test set, instead of doing the two steps on the same data set as what was done in Lab 5. You do NOT need the validation set in the assignment. 3. In the assignment, the performance of a NN is measured by the its prediction accuracy in classifying images from the test set, i.e. number of the correctly predicted images / number of the images in the test set. 4. You are asked to model THREE NNs by changing the architecture. For example, you may change the number of layers, use different type of layers, and try various activation layers. 5. You are encouraged to repeatedly train and test your NNs with different parameter setting, e.g. learning rate. 6. Your report MUST at least contain the following content a. Names and student numbers of all group members;

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