Questions and Answers of Econometrics

8 Compare and contrast the Tobit model with the Heckman model with regard to their assumptions and estimation.
7 Present the Heckman model and illustrate the two-stage estimation procedure, paying attention to the decision process and the level of involvement process.
6 What is defined as an inverse Mill’s ratio? What is its links to the issues of bias in estimation of censored and truncated samples?
5 What is selection bias? How does selection bias arise? By what means can selection bias be detected and how can selection bias be corrected?
4 Describe and discuss the Tobit model with reference to its implementation and estimation, and the issues in its estimation.
3 Contrast censoring with truncation, and then contrast limited dependent variables associated with censoring and truncation with limited dependent variables associated with discrete choice.
2 Why and how do censoring and truncation arise? At what points do censoring and truncation usually come across?
1 What is featured by a censored sample? What is featured by a truncated sample?
10 Collect data on dual listings from various sources, e.g. Thomson ONE Banker, the websites of relevant stock exchanges and companies, and then run multinomial logistic regression for the choice of
9 Collect data from various sources, e.g. Acquisition Monthly, Thomson ONE Banker and company annual reports, and then estimate a probit model and a logistic regression model for the choice of
8 What are marginal effects in discrete choice models? Why are the issues of marginal effects raised and addressed specifically for discrete choice models but not mentioned for linear regression and
7 What are defined as ordered probit and ordered logit? What differentiates an ordered logit model from a multinomial logit model?
6 Present and describe the multinomial logit model and multinomial logistic regression, and further discuss their role in modelling discrete choice.
5 Contrast the probit model with the logit model, paying attention to their probability density functions.
4 Describe and discuss the logit model with regard to its functional form and in relation to logistic regression.
3 Describe and discuss the probit model with regard to its functional form.
2 What is defined as a limited dependent variable? What are features of limited dependent variables?
1 Describe binary choice and, in general, discrete choice, with a few examples in daily life, corporate and individual.
8 Collect data from DataStream and estimate phases and coherence for the following pairs of time series:(a) total returns of Tesco and Sainsbury’s,(b) total returns of Intel and Motorola.
7 Collect data from various sources and estimate phases and coherence for the following pairs of time series:(a) GDP of the US and the Canada,(b) GDP and retail sales of the UK.
6 Collect data from DataStream and estimate phases and coherence for the following pairs of time series:(a) the spot and forward foreign exchange rates of the UK£ vis-à-vis the US$,(b) the spot
5 Collect data from various sources and perform the Fourier transform for the following time series (using RATS, GAUSS or other packages):(a) GDP of selected countries,(b) total return series of
4 What are phases and coherence in spectral analysis? Contrast them with leads/lags and correlation in the time domain.
3 Describe the Fourier transform and the inverse Fourier transform.
2 Discuss the advantages and disadvantages of the analysis in the frequency domain.
1 What is spectral analysis of time series? Does spectral analysis render new or more information?
5 The implementation of the Kalman filter is always complicated and the results may be sensitive to even slightly different settings. To practice, collect data from various sources and repeat the
4 Collect data from various sources, and estimate the following time series using the conventional ARIMA and in the state space using the Kalman filter (using RATS, GAUSS or other packages):(a) GDP
3 Describe the three steps of the Kalman filter algorithm in estimating a state space model.
2 Discuss the advantages of the state space model and the difficulties in the empirical implementation of the model.
1 What is the state variable and what is an unobserved component in a state space model?
6 Collect two companies’ data from Datastream to test for cointegration between the price and dividend. One of the companies is a fast growing firm, and the other is rather stable. Again data are
5 Collect data from Datastream to test for cointegration between the price and dividend, using UK market indices:(a) with the original data,(b) data in logarithm.
4 It is often claimed that cointegration of two or more financial time series means market inefficiency. But in this chapter, cointegration between the price and dividend is a prerequisite for market
3 What are the advantages of linking value and income with the present value model in the logarithm form? Is modelling with the logarithm form an overall improvement over that with the original form,
2 What are the advantages of linking value and income with the present value model in the original form? What are the shortcomings associated with this kind of modelling?
1 Why and how could the underlying economic processes and characteristics be better represented and reflected in an appropriate modelling strategy and framework? How could the consideration on
7 Estimate a two-state time-varying transition probability model in the above time series.
6 Collect data from various sources, and estimate a two-state constant transition probability model in the following time series (using RATS, GAUSS or other packages):(a) industrial production of
5 Discuss the advantages of adopting time-varying transition probabilities in the Markov process.
4 What is smoothing is the estimation of a Markov process? Why is smoothing required?
3 Cite examples of economic and financial variables which can be shown as a Markov process.
2 What is the Chapman–Kolmogorov equation for calculating multi-step transition probabilities?
1 Describe the state and the state transition probability in a Markov chain.
11 Collect data from various sources and carry out generalised impulse response analysis in the following groups of time series:(a) sectoral output indices in the UK,(b) GDPs of the UK, the US and
10 Collect data from various sources and carry out (orthogonal) impulse response analysis in the following groups of time series:(a) sectoral output indices in the UK,(b) GDP of the UK, the US and
9 Collect data from various sources and test for multivariate persistence in the following groups of time series:(a) the spot foreign exchange rates of selected industrialised nations vis-à-vis the
8 Collect data from various sources and test for persistence in the following time series:(a) the spot foreign exchange rates of selected industrialised nations and developing economies vis-à-vis
7 The contribution by the shock in each of the sources, expressed as a percentage of the total variance, sums to 100 per cent in this chapter. Discuss its rationale.
6 What is meant by generalised impulse response analysis? Can generalised impulse response analysis avoid all the complications in orthogonalisation while achieving the same goal?
5 Why is orthogonalisation required in impulse response analysis?
4 Describe impulse response analysis and its application in evaluating the impact of shocks and policy changes.
3 Discuss the advantages of the procedure in this chapter to standardise the multivariate persistence measure and its rationale.
2 Compare persistence analysis and the test for unit roots.
1 What is meant by persistence? How is persistence measured?
7 Discuss and comment on the new developments in modelling time-varying volatilities.
6 Collect data from Datastream and apply various multivariate GARCH models to the following time series:(a) the spot and forward foreign exchange rates of selected industrialised nations and
5 Collect data from Datastream to test for GARCH phenomena, using the following time series:(a) foreign exchange rates of selected industrialised nations and developing economies vis-à-vis the US$,
4 Compare different specifications of multivariate GARCH models and comment on their advantages and disadvantages.
3 What is the stochastic volatility model? Discuss the similarities and differences between a GARCH type model and a stochastic volatility model.
2 Discuss many variations of GARCH and their relevance to financial modelling.
1 Describe ARCH and GARCH in comparison with AR andARMAin the mean process.
9 Collect data from Datastream to test for cointegration between the following pairs:(a) the sterling vis-à-vis US$ exchange rates, spot and 30 days forward,(b) Tesco and Sainsbury’s share
8 Test for unit roots in the above time series in log differences. What do you find of their characteristics?
7 Collect data from Datastream to test for unit roots in the following time series:(a) GDP of the UK, US, Japan, China, Russia, and Brazil in logarithms,(b) total return series of IBM, Microsoft,
6 Discuss in what circumstances cointegration implies market inefficiency and in what circumstances cointegration means market efficiency.
5 Discuss the common cycle relationship in econometrics and the comovement of certain stationary variables in economics and finance.
4 What are the features of common cycles in contrast to common trends and cointegration?
3 Discuss the cointegration relationship in econometrics and the comovement of certain non-stationary financial and economic variables, e.g. dividends and prices, inflation and nominal interest
2 Describe a unit root process and show it does not have a constant limited variance.
1 Discuss the concept of stationarity and non-stationarity in relation to the characteristics of financial variables, e.g. prices and returns are the accumulation of income (dividends) over time, so
Explain MM and GMM approaches
Differentiate between t-distributions and F-distributions
define Stochastic processes and financial data generating processes
Class Exercise: Collect data on inflation and unemployment rates in the U.S. for the quarterly periods in 1980–2007 and develop and estimate a VAR model for the two variables. To compute the
Class Exercise: Pick a stock market index of your choosing and obtain daily data on the value of the chosen index for five years to find out if the stock index is characterized by ARCH effects.
Refer to any statistical package and estimate the impulse response function for a period of up to 8 lags for the VAR model that you developed in Exercise 22.16.In exercise 22.17Repeat Exercise 22.16,
Repeat Exercise 22.16, using the data on LDIVIDENDS and LCP.In exercise 22.16Using the data on LPCE and LDPI introduced in Section 21.1 (see the book’s website for the actual data), develop a
Repeat Exercise 22.11 for the LCP.In exercise 22.11Consider the data on log DPI (personal disposable income) introduced in Section 21.1 (see the book’s website for the actual data). Suppose you
Repeat Exercise 22.11 for the LPCE (personal consumption expenditure) data introduced in Section 21.1 (again, see the book’s website for the actual data).Exercise 22.11Consider the data on log DPI
Generate two random walk series as indicated in Eqs. (21.7.1) and (21.7.2) and regress one on the other. Repeat this exercise but now use their first differences and verify that in this regression
Check the identifiability of the models of Exercise 19.3 by applying both the order and rank conditions of identification.
Use the data of Exercise 17.22 but assume thatwhere X∗t are the desired sales. Estimate the parameters of this model and compare the results with those obtained in Exercise 17.22. How would you
Using the stock adjustment model (why?), estimate the short- and long-run elasticities of expenditure on new plant and equipment with respect to sales. Compare your results with those for Exercise
Baltagi and Griffin considered the following gasoline demand function:*ln Yit = β1 + β2 ln X2it + β3 ln X3it + β4 ln X4it + uitWhere Y = gasoline consumption per car; X2 = real income per capita,
Refer to the estimated regression in Eqs. (11.6.2) and (11.6.3). The regression results are quite similar. What could account for this outcome?
A simple alternative to the fixed effects model is called the differencing model, in which all the variables and the error term are expressed as differences. For a panel data set with two time
In 2003, ten states increased the taxes they placed on cigarettes. Because taxes increase the price of cigarettes, we’d expect that a tax increase would reduce the consumption of cigarettes. In
Fifteen years ago, the town of Easton decided to increase its annual spending on education so that its high school graduates would be able to earn higher wages. Now Easton has asked you to evaluate
Write the meaning of each of the following terms without referring to the book (or your notes), and then compare your definition with the version in the text for each.a. Control groupb.
Suppose you have been given two different ARIMA (1,0,0) fitted timeseries models of the variable Yt:Model A: Yt = 15.0 + 0.5Yt-1 + εtModel T: Yt = 45.0 - 0.5Yt-1 + εtWhere εt is a normally
Some of the most interesting applications of econometric forecasting are in the political arena. Examples of regression analysis in politics range from part-time marketing consultants who help local
To understand the difficulty of conditional forecasting, use Equation 1.19 to forecast the weights of the next three males you see, using your estimates of their heights. (Ask for actual values after
Calculate the following unconditional forecasts:a. The median price (PR) of a new single-family house in 2014, given the fact that the U.S. GDP in 2014 was roughly $17,400 billion and the following
Write the meaning of each of the following terms without referring to the book (or your notes), and compare your definition with the version in the text for each.a. ARIMAb. Autoregressive processc.
Suppose that a fad for oats (resulting from the announcement of the health benefits of oat bran) has made you toy with the idea of becoming a broker in the oat market. Before spending your money, you
Determine the identification properties of the following equations. In particular, be sure to note the number of predetermined variables in the system, the number of slope coefficients in the
Section 14.1 works through Equations 14.2 and 14.3 to show the violation of Classical Assumption III by an unexpected increase in ε1. Show the violation of Classical Assumption III by working
Write the meaning of each of the following terms without referring to the book (or your notes), and compare your definition with the version in the text for each.a. Endogenous variablesb. Exogenous
Return to our data on women’s labor force participation and consider the possibility of adding Ai, the age of the ith woman, to the equation. Be careful when you develop your expected sign and

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