计量经济学stata论文--英文个人版

Ch7 单变量时间序列分析7.1. ARMA 建模 1. AR (1)过程t t t y m y εα++=−1t t t m y L A y L εα+==−)()1( t t Ly εαμ−=−11注： "+++=−=−−22111)1()(L L L L A ααα""+++++++=−−2212)1(t t t t m y εααεεαα)1/()(α−=m y E t2221ασσε−=y。

平稳性条件：1<α。

自协方差： 2y k kσαγ= 自相关系数： （拖尾）kk αρ=2. AR （2）过程t t t y m y εα++=−1 )1/()(21αα−−=m y E t)1)(1)(1()1(21212222ααααασασε−+−−+−=y 平稳性条件：（表述1） 12<α 121<+αα112<−αα Yule －Walker 方程： 自协方差： 12011γαγαγ+= 02112γαγαγ+= 自相关系数： 1211ρααρ+= 2112αραρ+=2111ααρ−=， 222121αααρ+−=22113−−>+=k k k ραραρ （拖尾） 滞后算子多项式的根：t t t m y L A y L L εαα+==−−)()1(221 )1)(1()(21L L L A λλ−−=24,221121αααλλ++=Ld L c L L L A 2121111)]1)(1/[(1)(λλλλ−+−=−−=−t t t t t Ld Lc x x y ελελμ212111−+−=+=−两个具有相同扰动的AR(1)过程的叠加。

特征方程：)(1221z A z z =−−αα 根：i i z λ/1= 平稳性条件：（表述2）11>z ，12>z如果出现复根，其模要小于1.即：特征方程的根位于单位园外。

计量经济学论文英文Econometric Analysis of the Relationship between Education and Income1. IntroductionEconomic literature has long recognized the positive relationship between education and income. It is widely believed that individuals with higher levels of education tend to earn higher incomes compared to those with lower educational attainment. This relationship has important implications for understanding the dynamics of income inequality and social mobility.2. Literature ReviewMany studies have attempted to quantify the relationship between education and income using econometric methods. The results of these studies have varied, with some finding a strong positive relationship between the two variables, while others finding a weaker or even non-existent relationship. The inconsistency in findings has prompted further investigation into the determinants of income and the role of education in shaping individuals' earning potential.3. MethodologyIn this study, we use econometric techniques to analyze the relationship between education and income. We use panel data from a nationally representative survey to estimate the effect of education on individuals' income levels. We control for variousindividual and household characteristics, such as age, gender, race, and family background, to isolate the impact of education on income.4. Empirical ResultsOur findings suggest that education has a significant positive effect on income. Individuals with higher levels of education tend to earn substantially higher incomes compared to those with lower educational attainment, even after controlling for other relevant factors. The magnitude of the effect varies across different levels of education, with higher levels of education associated with larger income gains.5. Policy ImplicationsThe results of our analysis have important policy implications. They suggest that investing in education can have a strong positive impact on individuals' earning potential and can help reduce income inequality. Policymakers should consider implementing measures to improve access to quality education and to support individuals in obtaining higher levels of education.6. ConclusionIn conclusion, our econometric analysis provides robust evidence of the positive relationship between education and income. Our findings underscore the importance of education as a driver of economic opportunity and individual prosperity. Policymakers should prioritize investments in education to promote socialmobility and reduce income inequality.7. Limitations and Future ResearchOur study has several limitations that should be considered. First, our analysis is based on cross-sectional data, which limits our ability to establish causality between education and income. Future research could improve upon our study by using longitudinal data to track individuals' income changes over time in response to changes in their education levels.8. Additionally, our analysis may not capture the full range of factors that influence the relationship between education and income. For example, we did not examine the quality of education or the field of study, which can have a significant impact on individuals' earning potential. Future research could explore these nuanced factors to better understand the mechanisms through which education affects income.9. Another important consideration is that our analysis focuses on the individual-level effects of education on income. It is also crucial to examine how education at the aggregate level influences the overall distribution of income and the broader economic outcomes of a society.10. Furthermore, as the labor market evolves and new technologies emerge, the relationship between education and income may change. Future research could explore how the demand for different types of skills and educational credentials is shaping the income landscape in the context of technological advancements and globalization.11. Practical Implications for IndividualsIndividuals can also benefit from understanding the relationship between education and income. Our findings suggest that investing in higher education can significantly increase earning potential. It is important for individuals to consider the long-term benefits of education when making decisions about their educational and career paths.12. ConclusionIn conclusion, our study contributes to the existing literature by providing empirical evidence of the positive relationship between education and income. However, further research is needed to deepen our understanding of this complex relationship, including its causal mechanisms and its implications for the labor market and income distribution. These insights are crucial for informing policy decisions and individual choices related to education and economic well-being.。

J.M.StateUniversity Wooldridge MichiganRUDIMENTS OF STATAThis handout covers the most often encountered Stata commands. It is not comprehensive, but the summary will allow you to do basic data management and basic econometrics. I will provide some information on more advanced estimation commands through class handouts of Stata output.Reading Data FilesThe command to read a Stata file is use. If the Stata file is called WAGE1.DTA, and the file is on the diskette in the A: drive in the directory DATA, the command isuse a:\data\wage1After entering this command the data file WAGE1.DTA is loaded into memory.There is also a command, called infile, that allows you to read an ASCII file. I will not cover that here since all of the files we use have already been converted to Stata format. You should read the Stata manual or, even better, a publication called Getting Started with Stata for Windows, which is published by the Stata Press (College Station, Texas).If you have loaded a file, say WAGE1.DTA, completed your analysis, and then wish to use a different data set, you simply clear the existing data set from memory:clearIn doing this, it is important to know that any changes you made to the previous data set will be lost. You must explicitly save any changes you made (see "Leaving Stata" below). If, for example, you created a bunch of new variables, and you would like to have these variables available the next time you use the data, you should save the data set before using the clear command.Looking At and Summarizing Your DataAfter reading in a data file, you can get a list of the available variables by typingdesOften a short description has been given to each variable. To look at the observations on one or more variable, use the list command. Thus, to look at the variables wage and educ for all observations, I typelist wage educThis will list, one screen at a time, the data on wage and educ for every person in the sample. (Missing values in Stata are denoted by a period.) If the data set is large, you may not wish to look at all observations. You can always stop the listing (which Stata provides a screenful at a time) by hitting Ctrl-Break. In fact, Ctrl-Break can be used to interrupt any Stata command.Alternatively, there are various ways to restrict the range of the list and many other Stata commands. Suppose I want to look at the first 20 observations on wage and educ. Then I typelist wage educ in 1/20Rather than specify a range of observations, I can use a logical command instead. For example, to look at the data on marital status and age for people with zero hours worked, I can typelist married age if hours == 0Note how the double equals here is used by Stata to determine equivalence. The other relational operators in Stata are > (greater than), < (less than), >= (greater than or equal), <= (less than or equal), and, ~= (not equal). If I want to restrict attention to nonblacks, I can typelist married age if ~blackThe variable black is a binary indicator equal to unity for black individuals, and zero otherwise. The "~" is the logical "not" operator. We can combine many different logical statements: the commandlist married age if black & (hours >= 40)restricts attention to black people who work at least 40 hours a week. (Logical and is "&" and logical or is "|" in Stata.)Two useful commands for summarizing data are the sum and tab commands. The sum command computes the sample average, standard deviation, and the minimum and maximum values of all (nonmissing) observations. Since this command tells you how many observations were used for each variable in computing the summary statistics, you can easily find out how many missing data points there for any variable. Thus, the commandsum wage educ tenure marriedcomputes the summary statistics for the four variables listed. Since married is a binary variable, its minimum and maximum value are not very interesting. The average value reported is simply the proportion of people in the sample who are married.I can also obtain summary statistics for any subgroup of the sample by adding on a logical statement.sum wage educ tenure married if black | hispaniccomputes the summary statistics for all blacks and hispanics (I assume these are binary variables in my data set). If I have a pooled cross section or a panel data set, and I want to summarize for 1990, I typesum wage educ tenure married if == 1990I can restrict the sample to certain observation ranges using the command in m/n, just as illustrated in the list command.For variables that take on a relatively small number of values -- such as number of children or number of times an individual was arrested during a year -- I can use the tab command to get a frequency tabulation. The commandtab arrestswill report the frequency associated with each value of arrests in the sample. I can also combine tab with logical statements or restrict the range of observations.Sometimes, we want to restrict all subsequent analysis to a particular subset of the data. In such cases it is useful to drop the data that will not be used at the start. This can be done using the drop or keep commands.For example, if we want to analyze only blacks in a wage equation, then we can typedrop if ~blackThis drops everyone in the sample who is not black. Or, to analyze only the years between 1986 and 1990 (inclusive), we can typekeep if (year >= 1986) & (year <= 1990)It is important to know that the data dropped are gone from the current Stata session. If you wantto get them back, you must reread the original data file. Along these lines, do not make the mistake of saving the smaller data set over the original one, or you will lose a chunk of your data. (More on this below under "Leaving Stata.")Defining New VariablesIt is easy to create variables that are functions of existing variables. In Stata, this is accomplished using the gen command (short for generate). For example, to create the square of experience, I can typegen expersq = exper^2The new variable, expersq, can be used in regression or any place else Stata variables are used. (Stata does not allow us to put expressions into things like regression commands; we must create the variables first.) If an obervation had a missing value for exper then, naturally, expersq will also be missing for that observation. In fact, Stata will tell you how many missing observations were created after every gen command. If it reports nothing, then no missing observations were generated.In creating new variables, you should be aware of the fact that the names of variables must be no longer than eight characters. Stata will refuse to accept names longer than eight characters in the gen command (and all other commands).If I have the variable saving and would like to compute the natural log of saving, I can typegen lsaving = log(saving)If saving is missing then lsaving will also be missing. For functions such as the natural log, there is an additional issue: log(saving) is not defined for saving≤ 0. When a function is not defined for particular values of the variable, Stata sets the result to missing. If negative or zero saving is a legitimate outcome, you probably do not want to use its natural log.Logical commands can be used to restrict observations used for generating new variables. For example,gen lwage = log(wage) if hours > 0creates log(wage) for people who work (and therefor whose wage can be observed). Using the gen command without the statement if hours > 0 has the same effect in this example.Creating interaction terms is easy:gen blckeduc = black*educwhere "*" denotes multiplication; the division operator is "/". Addition is "+" while subtraction is "-".The gen command can also be used to create binary variables. For example, if fratio is the funding ratio of a firm's pension plan, I can create an "overfunded" dummy variable which is unity when fratio > 1 and zero otherwise:gen overfund = fratio > 1The way this works is that the logical statement on the right hand side is evaluated to be true or false; true is assigned the value unity, and false assigned the value zero. So overfund is unity if fratio > 1 and overfund is zero if fratio≤ 1. Because of the way Stata treats missing values, we must be somewhat careful. In particular, a missing value is treated as being greater than any number. Therefore, "fratio > 1" will be true whenever fratio is missing. We clearlydo not want to set overfund to unity when fratio is missing. So if there are missing data, we should add the commandreplace overfund = . if fratio == .The replace command is generally useful for redefining certain values of a variable.As another example, we can create year dummies using a command such asgen y85 = (year == 1985)where year is assumed to be a variable defined in the data set. The variable y85 is unity for observations corresponding to 1985, and zero otherwise. We can do this for each year in our sample to created a full set of year dummies.The gen command can also be used to difference data across different years. Suppose that, for a sample of cities, we have two years of data for each city (say 1982 and 1987). The data are stored so that the two years for each city are adjacent, with 1982 preceding 1987. To eliminate observed effects, say in relating city crime rates to expenditures on crimes and other city characteristics, we can use changes over time. The changes will be stored alongside the 1987 data. It is important to remember that for 1982 there is no change from a previous time period because we do not have data on a previous time period. Thus, we should define the change data so that it is missing in 1982. This is easily done. For example,gen ccrime = crime - crime[_n-1] if year == 1987gen cexpend = expend - expend[_n-1] if year == 1987The variable "_n" is the reserved Stata sybol for the current observation; thus, _n-1 is the variable lagged once. The variable ccrime is the change in crime from 1982 to 1987; cexpend is the change in expend from 1982 to 1987. These are stored in 1987, and the corresponding variables for 1982 will be missing. We can then use these changes in a regression analysis, or some other analysis. As we saw above, the replace command is useful for correcting mistakes in definitions and redefining variables after values of other variables have changed. Suppose, for example, that in creating the variable expersq, I mistakenly type gen expersq = exper^3. One possibility is to drop the variable expersq and try again:drop expersqgen expersq = exper^2(The drop command can be used for two different purposes: to drop a variable entirely from the data set, as above, or to drop some certain observations from the data set.)A faster route is to use the replace command:replace expersq = exper^2Stata explicitly requires the replace command to write over the contents in a previously defined variable.Basic Estimation CommandsStata makes estimating a variety of models by a variety of methods straightforward. These notes cover the basic ones. We will encounter additional commands for panel data estimation, probit, tobit, and some other models later on.Ordinary Least SquaresFor OLS regression, we use the command reg. Immediately following reg is the dependent variable, and after that, all of the independent variables (order of the independent variables is not, of course, important). An example isreg lwage educ exper expersq married blackThis produces OLS estimates, standard errors, t statistics, confidence intervals, and a variety of other statistics usually reported with OLS. Unless a specific range of observations or a logical statement is included, Stata uses all possible observations in obtaining estimates. It does not use observations for which data on the dependent variable or any of the independent variables is missing. Thus, you must be aware of the fact that adding another explanatory variable can result in fewer observations used in the regression if some observations do not contain a value for that variable. If I add to the above regression a variable called motheduc (mother's education), and this is missing for certain individuals, then the sample size will be decreased accordingly. Sometimes we wish to restrict our regression based on the size of one or more of the explanatory variables. In the regressionreg contribs mtchrate size sizesq if size <= 5000where size is number of employees of a firm, the analysis is restricted to firms with no more than 5,000 employees. I can also restrict the regression to a particular year using a similar "if" statement, or to a particular observation range using the command in m/n.Predicted values are obtained using the predict command. Thus, if I have run a regression with lwage as the dependent variable, I get the fitted values by typingpredict lwagehatThe choice of the name lwagehat is mine, subject to its being no more than eight characters and its not already being used. Note that the predict command saves the fitted values for the most recently run regression.The residuals can be obtained bypredict uhat, residwhere again the name uhat is my choice.Tests of multiple linear restrictions can be obtained after an OLS regression using the test command. For exclusion restrictions, just list the variables hypothesize to have no effect:test north south eastjointly tests whether the three regional indicators can be excluded from the previously estimated model. Along with the value of the F statistic you also get a p-value. As with the predict command, test is applied to the most recently estimated model. More general linear hypotheses can be tested, but I will not cover those here. (These can always be rewritten as exclusion restrictions, anyway.)OLS with heteroskedasticity-robust standard errors and t statistics is obtained using the reg command but adding robust at the end of the command line, preceded by a comma. Soreg lwage educ exper expersq married black, robuststill obtains the OLS estimates but reports heteroskedasticity-robust standard errors. The robust option is useful in other setups, too, including cluster samples and panel data. Any command that can be used after reg can be used after reg with the robust option. For example, we can test multiple exclusion restrictions in a heteroskedasticity-robust fashion by using the test command. Two Stage Least SquaresThe reg command can also be used to estimate models by 2SLS. After specifying the dependent variable and the explanatory variables -- which presumably include at least one explanatory variable that is correlated with the error -- we then list all of the exogenous variables as instruments in parentheses. Naturally, the list of instruments does not contain any endogenous variables.An example of a 2SLS command isreg lwage educ exper expersq married (motheduc fatheduc exper expersq married)This produces 2SLS esimates, standard errors, t statistics, and so on. By looking at this command, we see that educ is an endogenous explanatory variable in the log(wage) equation while exper, expersq, and married are assumed to be exogenous explanatory variables. The variables motheduc and fatheduc are assumed to be additional exogenous variables that do not appear in the log(wage) structural equation but should have some correlation with educ. These appear in the instrument list along with the exogenous explanatory variables. The order in which we order the instruments isnot important. The necessary condition for the model to be identified is that the number of terms in parentheses is at least as large as the total number of explanatory variables. In this example, the count is five to four, and so the order condition holds.Allowing for more than one endogenous explanatory variable is also easy. Suppose caloric consumption (calories) and protein consumption (protein) are endogenous in a wage equation for people in developing countries. However, we have regional prices on five commodity groups, say price1, ..., price5, to use as instruments. The Stata command for 2SLS might look likereg lwage educ exper male protein calories (educ exper male price1 price2 price3 price4 price5) if year == 1990if the analysis is restricted to data for 1990. Note that educ, exper, and male are taken to be exogenous here. The order condition easily holds (8 > 5).After 2SLS, we can test multiple restrictions using the test command, just as with OLS.Editing the Command LineStata has several shortcuts for entering commands. Two useful keys are Page Up and Page Down. If at any point you hit Page Up, the previously executed command appears on the command line. This can save on a lot of typing because you can hit Page Up and edit the previous command. Among other things, this makes adding an independent variable to a regression, or expanding an instrument list, fairly easy. Hitting Page Up repeatedly allows you to traverse through previously executed commands until you find the one you want. Hitting Page Down takes you back down through all of the commands.It is easy to edit the command line. Hitting Home takes the cursor to the beginning of the line; hitting End moves the cursor to the end of the line. The key Delete deletes a single character to the right of the cursor; holding it down will delete many characters. The Backspace key (a left arrow on many keyboards) deletes a character to the left of the cursor. Hitting the left arrow (J) moves you one character to the left, and the right arrow (L) takes you one character to the right. You can hold down J and L to move several characters.The key Ins allows you to toggle between insert and overwrite model. Both of these modes are useful for editing commands.Recording Your Work: Creating a Log FileFor involved projects, it is a good idea to create a record of what you have done (data transformations, regressions, and so on). To do this you can create a log file. Suppose I have a diskette in the A: drive and I would like to create a log file on this diskette. Before doing any analysis (but maybe after reading in my data set), I can typelog using a:ps4This will create the file PS4.LOG on the diskette in the A: drive. Note that, unless you specify suffix explicitly, Stata adds ".LOG" to the end of the file name. Of course, if I want the log file on the default drive (usually C:) I would omit a: in the command.Often, you might wish to temporarily close your log file while you look at the data, or run regressions that you are not sure you wish to keep. Since log files can get big in a hurry, it is useful to know that they can be turned off and on at any time. The commands arelog offlog onThese commands require that a log file has been opened (note that the name of the log file is not needed in either case). If you use the log off command, remember to type log on before doing anything you wish to keep.When you are finished, you can close the log file for good:log closeAfter typing this command, log on will not open the log file. If I decided a want to add onto the end of an existing log file, say PS4.LOG on the A: drive, I must typelog using a:ps4, appendAny subsequent Stata commands and output will be added to the end of PS4.LOG. If I omit the append command, Stata will not allow me to open a file called PS4.LOG. If I decide that what is currently in PS4.LOG is no longer needed, but I want to use the same file name, I would typelog using a:ps4, replaceYou should use this with caution, because you will lose the old contents of PS4.LOG.Stata log files are just standard ASCII files, and can be looked at using standard DOS commands (such as type and more). They can also be directly sent to a printer.Leaving StataIf I have made no changes to my data file, then to leave Stata (after closing my log file, if I have opened one up), I simply typeexitStata will not let me exit if new variables have been created, or if I have dropped part of the sample. Generally, if the data set is at all different from the one I initially read in, Stata will tell you this and refuse to let you exit. If you do not wish to save any changes you have made then typeexit, clearThis is especially useful if, after reading in the initial file, you dropped some observations before undertaking your analysis. In most cases you do not want to save the smaller data set over the original one.The Help CommandYou can learn more about the previous commands, as well as many other Stata commands, using the on-line help feature. To get a listing of the topics available using the help command, type help contentsInformation on specific commands can be obtained by typing help followed by the command name. You must use the full command name, not the abbreviations. Below are some examples: help regresshelp generatehelp testUsing Stata as a Calculator and Computing p-valuesStata can be used to compute a variety of expressions, including certain functions that are not available on a standard calculator. The command to compute an expression is display, or di for short. For example, the commanddi .048/(2*.0016)will return "15." We can use di to compute natural logs, exponentials, squares, and so on. For example,di exp(3.5 + 4*.06)returns the value 42.098 (approximately). These previous examples can be done on most calculators. More importantly, we can use di to compute p-values after computing a test statistic. The commanddi normprob(1.58)This gives the probability that a standard normal random variable is greater than the value 1.58 (about .943). Thus, if a standard normal test statistic takes on the value 1.58, the p-value is 1 - .943 = .057. Other functions are defined to give the p-value directly.di tprob(df,t)returns the the p-value for a t test against a two-sided alternative (t is the absolute value of the t statistic and df is the degrees of freedom). For example, with df = 31 and t = 1.32, the command returns the value .196. To obtain the p-value for an F test, the command isdi fprob(df1,df2,F)where df1 is the numerator degrees of freedom, df2 is the denominator df, and F is the value of the F statistic. As an example,di fprob(3,142,2.18)returns the p-value .093.11。

Microeconometrics Using StataContentsList of tables xxxv List of figures xxxvii Preface xxxix 1Stata basics1............................................................................................1.1Interactive use 1..............................................................................................1.2 Documentation 2..........................................................................1.2.1Stata manuals 2...........................................................1.2.2Additional Stata resources 3.......................................................................1.2.3The help command 3................................1.2.4The search, findit, and hsearch commands 41.3 Command syntax and operators 5...................................................................................................................................1.3.1Basic command syntax 5................................................1.3.2 Example: The summarize command 61.3.3Example: The regress command 7..............................................................................1.3.4Abbreviations, case sensitivity, and wildcards 9................................1.3.5Arithmetic, relational, and logical operators 9.........................................................................1.3.6Error messages 10........................................................................................1.4 Do-files and log files 10.............................................................................1.4.1Writing a do-file 101.4.2Running do-files 11.........................................................................................................................................................................1.4.3Log files 12..................................................................1.4.4 A three-step process 131.4.5Comments and long lines 13......................................................................................................1.4.6Different implementations of Stata 141.5Scalars and matrices (15)1.5.1Scalars (15)1.5.2Matrices (15)1.6 Using results from Stata commands (16)1.6.1Using results from the r-class command summarize (16)1.6.2Using results from the e-class command regress (17)1.7 Global and local macros (19)1.7.1Global macros (19)1.7.2Local macros (20)1.7.3Scalar or macro? (21)1.8 Looping commands (22)1.8.1The foreach loop (23)1.8.2The forvalues loop (23)1.8.3The while loop (24)1.8.4The continue command (24)1.9 Some useful commands (24)1.10 Template do-file (25)1.11 User-written commands (25)1.12 Stata resources (26)1.13 Exercises (26)2 Data management and graphics292.1Introduction (29)2.2 Types of data (29)2.2.1Text or ASCII data (30)2.2.2Internal numeric data (30)2.2.3String data (31)2.2.4Formats for displaying numeric data (31)2.3Inputting data (32)2.3.1General principles (32)2.3.2Inputting data already in Stata format (33)2.3.3Inputting data from the keyboard (34)2.3.4Inputting nontext data (34)2.3.5Inputting text data from a spreadsheet (35)2.3.6Inputting text data in free format (36)2.3.7Inputting text data in fixed format (36)2.3.8Dictionary files (37)2.3.9Common pitfalls (37)2.4 Data management (38)2.4.1PSID example (38)2.4.2Naming and labeling variables (41)2.4.3Viewing data (42)2.4.4Using original documentation (43)2.4.5Missing values (43)2.4.6Imputing missing data (45)2.4.7Transforming data (generate, replace, egen, recode) (45)The generate and replace commands (46)The egen command (46)The recode command (47)The by prefix (47)Indicator variables (47)Set of indicator variables (48)Interactions (49)Demeaning (50)2.4.8Saving data (51)2.4.9Selecting the sample (51)2.5 Manipulating datasets (53)2.5.1Ordering observations and variables (53)2.5.2Preserving and restoring a dataset (53)2.5.3Wide and long forms for a dataset (54)2.5.4Merging datasets (54)2.5.5Appending datasets (56)2.6 Graphical display of data (57)2.6.1Stata graph commands (57)Example graph commands (57)Saving and exporting graphs (58)Learning how to use graph commands (59)2.6.2Box-and-whisker plot (60)2.6.3Histogram (61)2.6.4Kernel density plot (62)2.6.5Twoway scatterplots and fitted lines (64)2.6.6Lowess, kernel, local linear, and nearest-neighbor regression652.6.7Multiple scatterplots (67)2.7 Stata resources (68)2.8Exercises (68)3Linear regression basics713.1Introduction (71)3.2 Data and data summary (71)3.2.1Data description (71)3.2.2Variable description (72)3.2.3Summary statistics (73)3.2.4More-detailed summary statistics (74)3.2.5Tables for data (75)3.2.6Statistical tests (78)3.2.7Data plots (78)3.3Regression in levels and logs (79)3.3.1Basic regression theory (79)3.3.2OLS regression and matrix algebra (80)3.3.3Properties of the OLS estimator (81)3.3.4Heteroskedasticity-robust standard errors (82)3.3.5Cluster–robust standard errors (82)3.3.6Regression in logs (83)3.4Basic regression analysis (84)3.4.1Correlations (84)3.4.2The regress command (85)3.4.3Hypothesis tests (86)3.4.4Tables of output from several regressions (87)3.4.5Even better tables of regression output (88)3.5Specification analysis (90)3.5.1Specification tests and model diagnostics (90)3.5.2Residual diagnostic plots (91)3.5.3Influential observations (92)3.5.4Specification tests (93)Test of omitted variables (93)Test of the Box–Cox model (94)Test of the functional form of the conditional mean (95)Heteroskedasticity test (96)Omnibus test (97)3.5.5Tests have power in more than one direction (98)3.6Prediction (100)3.6.1In-sample prediction (100)3.6.2Marginal effects (102)3.6.3Prediction in logs: The retransformation problem (103)3.6.4Prediction exercise (104)3.7 Sampling weights (105)3.7.1Weights (106)3.7.2Weighted mean (106)3.7.3Weighted regression (107)3.7.4Weighted prediction and MEs (109)3.8 OLS using Mata (109)3.9Stata resources (111)3.10 Exercises (111)4Simulation1134.1Introduction (113)4.2 Pseudorandom-number generators: Introduction (114)4.2.1Uniform random-number generation (114)4.2.2Draws from normal (116)4.2.3Draws from t, chi-squared, F, gamma, and beta (117)4.2.4 Draws from binomial, Poisson, and negative binomial . . . (118)Independent (but not identically distributed) draws frombinomial (118)Independent (but not identically distributed) draws fromPoisson (119)Histograms and density plots (120)4.3 Distribution of the sample mean (121)4.3.1Stata program (122)4.3.2The simulate command (123)4.3.3Central limit theorem simulation (123)4.3.4The postfile command (124)4.3.5Alternative central limit theorem simulation (125)4.4 Pseudorandom-number generators: Further details (125)4.4.1Inverse-probability transformation (126)4.4.2Direct transformation (127)4.4.3Other methods (127)4.4.4Draws from truncated normal (128)4.4.5Draws from multivariate normal (129)Direct draws from multivariate normal (129)Transformation using Cholesky decomposition (130)4.4.6Draws using Markov chain Monte Carlo method (130)4.5 Computing integrals (132)4.5.1Quadrature (133)4.5.2Monte Carlo integration (133)4.5.3Monte Carlo integration using different S (134)4.6Simulation for regression: Introduction (135)4.6.1Simulation example: OLS with X2 errors (135)4.6.2Interpreting simulation output (138)Unbiasedness of estimator (138)Standard errors (138)t statistic (138)Test size (139)Number of simulations (140)4.6.3Variations (140)Different sample size and number of simulations (140)Test power (140)Different error distributions (141)4.6.4Estimator inconsistency (141)4.6.5Simulation with endogenous regressors (142)4.7Stata resources (144)4.8Exercises (144)5GLS regression1475.1Introduction (147)5.2 GLS and FGLS regression (147)5.2.1GLS for heteroskedastic errors (147)5.2.2GLS and FGLS (148)5.2.3Weighted least squares and robust standard errors (149)5.2.4Leading examples (149)5.3 Modeling heteroskedastic data (150)5.3.1Simulated dataset (150)5.3.2OLS estimation (151)5.3.3Detecting heteroskedasticity (152)5.3.4FGLS estimation (154)5.3.5WLS estimation (156)5.4System of linear regressions (156)5.4.1SUR model (156)5.4.2The sureg command (157)5.4.3Application to two categories of expenditures (158)5.4.4Robust standard errors (160)5.4.5Testing cross-equation constraints (161)5.4.6Imposing cross-equation constraints (162)5.5Survey data: Weighting, clustering, and stratification (163)5.5.1Survey design (164)5.5.2Survey mean estimation (167)5.5.3Survey linear regression (167)5.6Stata resources (169)5.7Exercises (169)6Linear instrumental-variables regression1716.1Introduction (171)6.2 IV estimation (171)6.2.1Basic IV theory (171)6.2.2Model setup (173)6.2.3IV estimators: IV, 2SLS, and GMM (174)6.2.4Instrument validity and relevance (175)6.2.5Robust standard-error estimates (176)6.3 IV example (177)6.3.1The ivregress command (177)6.3.2Medical expenditures with one endogenous regressor . . . (178)6.3.3Available instruments (179)6.3.4IV estimation of an exactly identified model (180)6.3.5IV estimation of an overidentified model (181)6.3.6Testing for regressor endogeneity (182)6.3.7Tests of overidentifying restrictions (185)6.3.8IV estimation with a binary endogenous regressor (186)6.4 Weak instruments (188)6.4.1Finite-sample properties of IV estimators (188)6.4.2Weak instruments (189)Diagnostics for weak instruments (189)Formal tests for weak instruments (190)6.4.3The estat firststage command (191)6.4.4Just-identified model (191)6.4.5Overidentified model (193)6.4.6More than one endogenous regressor (195)6.4.7Sensitivity to choice of instruments (195)6.5 Better inference with weak instruments (197)6.5.1Conditional tests and confidence intervals (197)6.5.2LIML estimator (199)6.5.3Jackknife IV estimator (199)6.5.4 Comparison of 2SLS, LIML, JIVE, and GMM (200)6.6 3SLS systems estimation (201)6.7Stata resources (203)6.8Exercises (203)7Quantile regression2057.1Introduction (205)7.2 QR (205)7.2.1Conditional quantiles (206)7.2.2Computation of QR estimates and standard errors (207)7.2.3The qreg, bsqreg, and sqreg commands (207)7.3 QR for medical expenditures data (208)7.3.1Data summary (208)7.3.2QR estimates (209)7.3.3Interpretation of conditional quantile coefficients (210)7.3.4Retransformation (211)7.3.5Comparison of estimates at different quantiles (212)7.3.6Heteroskedasticity test (213)7.3.7Hypothesis tests (214)7.3.8Graphical display of coefficients over quantiles (215)7.4 QR for generated heteroskedastic data (216)7.4.1Simulated dataset (216)7.4.2QR estimates (219)7.5 QR for count data (220)7.5.1Quantile count regression (221)7.5.2The qcount command (222)7.5.3Summary of doctor visits data (222)7.5.4Results from QCR (224)7.6Stata resources (226)7.7Exercises (226)8Linear panel-data models: Basics2298.1Introduction (229)8.2 Panel-data methods overview (229)8.2.1Some basic considerations (230)8.2.2Some basic panel models (231)Individual-effects model (231)Fixed-effects model (231)Random-effects model (232)Pooled model or population-averaged model (232)Two-way-effects model (232)Mixed linear models (233)8.2.3Cluster-robust inference (233)8.2.4The xtreg command (233)8.2.5Stata linear panel-data commands (234)8.3 Panel-data summary (234)8.3.1Data description and summary statistics (234)8.3.2Panel-data organization (236)8.3.3Panel-data description (237)8.3.4Within and between variation (238)8.3.5Time-series plots for each individual (241)8.3.6Overall scatterplot (242)8.3.7Within scatterplot (243)8.3.8Pooled OLS regression with cluster—robust standard errors ..2448.3.9Time-series autocorrelations for panel data (245)8.3.10 Error correlation in the RE model (247)8.4 Pooled or population-averaged estimators (248)8.4.1Pooled OLS estimator (248)8.4.2Pooled FGLS estimator or population-averaged estimator (248)8.4.3The xtreg, pa command (249)8.4.4Application of the xtreg, pa command (250)8.5 Within estimator (251)8.5.1Within estimator (251)8.5.2The xtreg, fe command (251)8.5.3Application of the xtreg, fe command (252)8.5.4Least-squares dummy-variables regression (253)8.6 Between estimator (254)8.6.1Between estimator (254)8.6.2Application of the xtreg, be command (255)8.7 RE estimator (255)8.7.1RE estimator (255)8.7.2The xtreg, re command (256)8.7.3Application of the xtreg, re command (256)8.8 Comparison of estimators (257)8.8.1Estimates of variance components (257)8.8.2Within and between R-squared (258)8.8.3Estimator comparison (258)8.8.4Fixed effects versus random effects (259)8.8.5Hausman test for fixed effects (260)The hausman command (260)Robust Hausman test (261)8.8.6Prediction (262)8.9 First-difference estimator (263)8.9.1First-difference estimator (263)8.9.2Strict and weak exogeneity (264)8.10 Long panels (265)8.10.1 Long-panel dataset (265)8.10.2 Pooled OLS and PFGLS (266)8.10.3 The xtpcse and xtgls commands (267)8.10.4 Application of the xtgls, xtpcse, and xtscc commands . . . (268)8.10.5 Separate regressions (270)8.10.6 FE and RE models (271)8.10.7 Unit roots and cointegration (272)8.11 Panel-data management (274)8.11.1 Wide-form data (274)8.11.2 Convert wide form to long form (274)8.11.3 Convert long form to wide form (275)8.11.4 An alternative wide-form data (276)8.12 Stata resources (278)8.13 Exercises (278)9Linear panel-data models: Extensions2819.1Introduction (281)9.2 Panel IV estimation (281)9.2.1Panel IV (281)9.2.2The xtivreg command (282)9.2.3Application of the xtivreg command (282)9.2.4Panel IV extensions (284)9.3 Hausman-Taylor estimator (284)9.3.1Hausman-Taylor estimator (284)9.3.2The xthtaylor command (285)9.3.3Application of the xthtaylor command (285)9.4 Arellano-Bond estimator (287)9.4.1Dynamic model (287)9.4.2IV estimation in the FD model (288)9.4.3 The xtabond command (289)9.4.4Arellano-Bond estimator: Pure time series (290)9.4.5Arellano-Bond estimator: Additional regressors (292)9.4.6Specification tests (294)9.4.7 The xtdpdsys command (295)9.4.8 The xtdpd command (297)9.5 Mixed linear models (298)9.5.1Mixed linear model (298)9.5.2 The xtmixed command (299)9.5.3Random-intercept model (300)9.5.4Cluster-robust standard errors (301)9.5.5Random-slopes model (302)9.5.6Random-coefficients model (303)9.5.7Two-way random-effects model (304)9.6 Clustered data (306)9.6.1Clustered dataset (306)9.6.2Clustered data using nonpanel commands (306)9.6.3Clustered data using panel commands (307)9.6.4Hierarchical linear models (310)9.7Stata resources (311)9.8Exercises (311)10 Nonlinear regression methods31310.1 Introduction (313)10.2 Nonlinear example: Doctor visits (314)10.2.1 Data description (314)10.2.2 Poisson model description (315)10.3 Nonlinear regression methods (316)10.3.1 MLE (316)10.3.2 The poisson command (317)10.3.3 Postestimation commands (318)10.3.4 NLS (319)10.3.5 The nl command (319)10.3.6 GLM (321)10.3.7 The glm command (321)10.3.8 Other estimators (322)10.4 Different estimates of the VCE (323)10.4.1 General framework (323)10.4.2 The vce() option (324)10.4.3 Application of the vce() option (324)10.4.4 Default estimate of the VCE (326)10.4.5 Robust estimate of the VCE (326)10.4.6 Cluster–robust estimate of the VCE (327)10.4.7 Heteroskedasticity- and autocorrelation-consistent estimateof the VCE (328)10.4.8 Bootstrap standard errors (328)10.4.9 Statistical inference (329)10.5 Prediction (329)10.5.1 The predict and predictnl commands (329)10.5.2 Application of predict and predictnl (330)10.5.3 Out-of-sample prediction (331)10.5.4 Prediction at a specified value of one of the regressors (321)10.5.5 Prediction at a specified value of all the regressors (332)10.5.6 Prediction of other quantities (333)10.6 Marginal effects (333)10.6.1 Calculus and finite-difference methods (334)10.6.2 MEs estimates AME, MEM, and MER (334)10.6.3 Elasticities and semielasticities (335)10.6.4 Simple interpretations of coefficients in single-index models (336)10.6.5 The mfx command (337)10.6.6 MEM: Marginal effect at mean (337)Comparison of calculus and finite-difference methods . . . (338)10.6.7 MER: Marginal effect at representative value (338)10.6.8 AME: Average marginal effect (339)10.6.9 Elasticities and semielasticities (340)10.6.10 AME computed manually (342)10.6.11 Polynomial regressors (343)10.6.12 Interacted regressors (344)10.6.13 Complex interactions and nonlinearities (344)10.7 Model diagnostics (345)10.7.1 Goodness-of-fit measures (345)10.7.2 Information criteria for model comparison (346)10.7.3 Residuals (347)10.7.4 Model-specification tests (348)10.8 Stata resources (349)10.9 Exercises (349)11 Nonlinear optimization methods35111.1 Introduction (351)11.2 Newton–Raphson method (351)11.2.1 NR method (351)11.2.2 NR method for Poisson (352)11.2.3 Poisson NR example using Mata (353)Core Mata code for Poisson NR iterations (353)Complete Stata and Mata code for Poisson NR iterations (353)11.3 Gradient methods (355)11.3.1 Maximization options (355)11.3.2 Gradient methods (356)11.3.3 Messages during iterations (357)11.3.4 Stopping criteria (357)11.3.5 Multiple maximums (357)11.3.6 Numerical derivatives (358)11.4 The ml command: if method (359)11.4.1 The ml command (360)11.4.2 The If method (360)11.4.3 Poisson example: Single-index model (361)11.4.4 Negative binomial example: Two-index model (362)11.4.5 NLS example: Nonlikelihood model (363)11.5 Checking the program (364)11.5.1 Program debugging using ml check and ml trace (365)11.5.2 Getting the program to run (366)11.5.3 Checking the data (366)11.5.4 Multicollinearity and near coilinearity (367)11.5.5 Multiple optimums (368)11.5.6 Checking parameter estimation (369)11.5.7 Checking standard-error estimation (370)11.6 The ml command: d0, dl, and d2 methods (371)11.6.1 Evaluator functions (371)11.6.2 The d0 method (373)11.6.3 The dl method (374)11.6.4 The dl method with the robust estimate of the VCE (374)11.6.5 The d2 method (375)11.7 The Mata optimize() function (376)11.7.1 Type d and v evaluators (376)11.7.2 Optimize functions (377)11.7.3 Poisson example (377)Evaluator program for Poisson MLE (377)The optimize() function for Poisson MLE (378)11.8 Generalized method of moments (379)11.8.1 Definition (380)11.8.2 Nonlinear IV example (380)11.8.3 GMM using the Mata optimize() function (381)11.9 Stata resources (383)11.10 Exercises (383)12 Testing methods38512.1 Introduction (385)12.2 Critical values and p-values (385)12.2.1 Standard normal compared with Student's t (386)12.2.2 Chi-squared compared with F (386)12.2.3 Plotting densities (386)12.2.4 Computing p-values and critical values (388)12.2.5 Which distributions does Stata use? (389)12.3 Wald tests and confidence intervals (389)12.3.1 Wald test of linear hypotheses (389)12.3.2 The test command (391)Test single coefficient (392)Test several hypotheses (392)Test of overall significance (393)Test calculated from retrieved coefficients and VCE (393)12.3.3 One-sided Wald tests (394)12.3.4 Wald test of nonlinear hypotheses (delta method) (395)12.3.5 The testnl command (395)12.3.6 Wald confidence intervals (396)12.3.7 The lincom command (396)12.3.8 The nlcom command (delta method) (397)12.3.9 Asymmetric confidence intervals (398)12.4 Likelihood-ratio tests (399)12.4.1 Likelihood-ratio tests (399)12.4.2 The lrtest command (401)12.4.3 Direct computation of LR tests (401)12.5 Lagrange multiplier test (or score test) (402)12.5.1 LM tests (402)12.5.2 The estat command (403)12.5.3 LM test by auxiliary regression (403)12.6 Test size and power (405)12.6.1 Simulation DGP: OLS with chi-squared errors (405)12.6.2 Test size (406)12.6.3 Test power (407)12.6.4 Asymptotic test power (410)12.7 Specification tests (411)12.7.1 Moment-based tests (411)12.7.2 Information matrix test (411)12.7.3 Chi-squared goodness-of-fit test (412)12.7.4 Overidentifying restrictions test (412)12.7.5 Hausman test (412)12.7.6 Other tests (413)12.8 Stata resources (413)12.9 Exercises (413)13 Bootstrap methods41513.1 Introduction (415)13.2 Bootstrap methods (415)13.2.1 Bootstrap estimate of standard error (415)13.2.2 Bootstrap methods (416)13.2.3 Asymptotic refinement (416)13.2.4 Use the bootstrap with caution (416)13.3 Bootstrap pairs using the vce(bootstrap) option (417)13.3.1 Bootstrap-pairs method to estimate VCE (417)13.3.2 The vce(bootstrap) option (418)13.3.3 Bootstrap standard-errors example (418)13.3.4 How many bootstraps? (419)13.3.5 Clustered bootstraps (420)13.3.6 Bootstrap confidence intervals (421)13.3.7 The postestimation estat bootstrap command (422)13.3.8 Bootstrap confidence-intervals example (423)13.3.9 Bootstrap estimate of bias (423)13.4 Bootstrap pairs using the bootstrap command (424)13.4.1 The bootstrap command (424)13.4.2 Bootstrap parameter estimate from a Stata estimationcommand (425)13.4.3 Bootstrap standard error from a Stata estimation command (426)13.4.4 Bootstrap standard error from a user-written estimationcommand (426)13.4.5 Bootstrap two-step estimator (427)13.4.6 Bootstrap Hausman test (429)13.4.7 Bootstrap standard error of the coefficient of variation . . (430)13.5 Bootstraps with asymptotic refinement (431)13.5.1 Percentile-t method (431)13.5.2 Percentile-t Wald test (432)13.5.3 Percentile-t Wald confidence interval (433)13.6 Bootstrap pairs using bsample and simulate (434)13.6.1 The bsample command (434)13.6.2 The bsample command with simulate (434)13.6.3 Bootstrap Monte Carlo exercise (436)13.7 Alternative resampling schemes (436)13.7.1 Bootstrap pairs (437)13.7.2 Parametric bootstrap (437)13.7.3 Residual bootstrap (439)13.7.4 Wild bootstrap (440)13.7.5 Subsampling (441)13.8 The jackknife (441)13.8.1 Jackknife method (441)13.8.2 The vice(jackknife) option and the jackknife command . . (442)13.9 Stata resources (442)13.10 Exercises (442)14 Binary outcome models44514.1 Introduction (445)14.2 Some parametric models (445)14.2.1 Basic model (445)14.2.2 Logit, probit, linear probability, and clog-log models . . . (446)14.3 Estimation (446)14.3.1 Latent-variable interpretation and identification (447)14.3.2 ML estimation (447)14.3.3 The logit and probit commands (448)14.3.4 Robust estimate of the VCE (448)14.3.5 OLS estimation of LPM (448)14.4 Example (449)14.4.1 Data description (449)14.4.2 Logit regression (450)14.4.3 Comparison of binary models and parameter estimates . (451)14.5 Hypothesis and specification tests (452)14.5.1 Wald tests (453)14.5.2 Likelihood-ratio tests (453)14.5.3 Additional model-specification tests (454)Lagrange multiplier test of generalized logit (454)Heteroskedastic probit regression (455)14.5.4 Model comparison (456)14.6 Goodness of fit and prediction (457)14.6.1 Pseudo-R2 measure (457)14.6.2 Comparing predicted probabilities with sample frequencies (457)14.6.3 Comparing predicted outcomes with actual outcomes . . . (459)14.6.4 The predict command for fitted probabilities (460)14.6.5 The prvalue command for fitted probabilities (461)14.7 Marginal effects (462)14.7.1 Marginal effect at a representative value (MER) (462)14.7.2 Marginal effect at the mean (MEM) (463)14.7.3 Average marginal effect (AME) (464)14.7.4 The prchange command (464)14.8 Endogenous regressors (465)14.8.1 Example (465)14.8.2 Model assumptions (466)14.8.3 Structural-model approach (467)The ivprobit command (467)Maximum likelihood estimates (468)Two-step sequential estimates (469)14.8.4 IVs approach (471)14.9 Grouped data (472)14.9.1 Estimation with aggregate data (473)14.9.2 Grouped-data application (473)14.10 Stata resources (475)14.11 Exercises (475)15 Multinomial models47715.1 Introduction (477)15.2 Multinomial models overview (477)15.2.1 Probabilities and MEs (477)15.2.2 Maximum likelihood estimation (478)15.2.3 Case-specific and alternative-specific regressors (479)15.2.4 Additive random-utility model (479)15.2.5 Stata multinomial model commands (480)15.3 Multinomial example: Choice of fishing mode (480)15.3.1 Data description (480)15.3.2 Case-specific regressors (483)15.3.3 Alternative-specific regressors (483)15.4 Multinomial logit model (484)15.4.1 The mlogit command (484)15.4.2 Application of the mlogit command (485)15.4.3 Coefficient interpretation (486)15.4.4 Predicted probabilities (487)15.4.5 MEs (488)15.5 Conditional logit model (489)15.5.1 Creating long-form data from wide-form data (489)15.5.2 The asclogit command (491)15.5.3 The clogit command (491)15.5.4 Application of the asclogit command (492)15.5.5 Relationship to multinomial logit model (493)15.5.6 Coefficient interpretation (493)15.5.7 Predicted probabilities (494)15.5.8 MEs (494)15.6 Nested logit model (496)15.6.1 Relaxing the independence of irrelevant alternatives as-sumption (497)15.6.2 NL model (497)15.6.3 The nlogit command (498)15.6.4 Model estimates (499)15.6.5 Predicted probabilities (501)15.6.6 MEs (501)15.6.7 Comparison of logit models (502)15.7 Multinomial probit model (503)15.7.1 MNP (503)15.7.2 The mprobit command (503)15.7.3 Maximum simulated likelihood (504)15.7.4 The asmprobit command (505)15.7.5 Application of the asmprobit command (505)15.7.6 Predicted probabilities and MEs (507)15.8 Random-parameters logit (508)15.8.1 Random-parameters logit (508)15.8.2 The mixlogit command (508)15.8.3 Data preparation for mixlogit (509)15.8.4 Application of the mixlogit command (509)15.9 Ordered outcome models (510)15.9.1 Data summary (511)15.9.2 Ordered outcomes (512)15.9.3 Application of the ologit command (512)15.9.4 Predicted probabilities (513)15.9.5 MEs (513)15.9.6 Other ordered models (514)15.10 Multivariate outcomes (514)15.10.1 Bivariate probit (515)15.10.2 Nonlinear SUR (517)15.11 Stata resources (518)15.12 Exercises (518)16 Tobit and selection models52116.1 Introduction (521)16.2 Tobit model (521)16.2.1 Regression with censored data (521)16.2.2 Tobit model setup (522)16.2.3 Unknown censoring point (523)。

计量经济学论文范文2篇计量经济学论文范文一：形成性评价计量经济学论文1形成性评价的可行性及必要性我国医学类院校最早成立统计学本科专业的是第四军医大学,随后中山大学、潍坊医学院、滨州医学院等院校也相继成立了统计学本科专业。

该专业培养目标是培养适应未来经济社会与科技发展需要,德、智、体、美等全面和i皆发展,掌握统计学的基本理论和方法,可熟练运用计算机分析数据,能在卫生行政机关、卫生防疫及医药相关部门从事统计调査、统计分析工作,或在医药卫生、教育机构从事科研与教学等工作的应用型专门人才。

我院统计学专业本科(卫生统计方向)自2006年开始招生,其培养友案涉及的主干课程可分为医学类(含基础医学_、临床医学和预防医学)、统计类、数学类、经济管理类、计算机类、外语及人文社会科学7类课程。

其中计量经济学课程作为经济管理类的核心课程之一,属于统计学专业的必修课程。

本课程的学习使学生在已经学习的统计学和经济学的基础上进一步理解、掌握计量经济分析的方法和基础理论,通过模型研究经济问题的数量规律,对经济问题的前景做出正确的预测,提高学生发现问题、分析问题、解决问题的能力以及运用统计学理论与方法分析、解决相关领域实际问题的能力。

传统的计量经济学课程评价采用的是终结性评价,即学生成绩由期末考试卷面成绩和平时成绩(含考勤、作业)组成。

多年的教学实践表明,终结性评价存在重视结果而忽略过程、评价主体单一化、评价内容缺乏全面性等诸多缺陷,而“一考定乾坤“的不公平评价方式也给学生带来了负面的影响,造成一定的考前突击、考试作弊现象ra,不利于教学质量和学生素质的提高。

迄今为止,尚没有形成性评价在计量经济学课程中应用的文献,但形成性评价在其他学科教学中的广泛应用表明,它对学生成绩的提高具有明显效果,使学生的学习动机和学习自信心得到增强m。

因此,有必要对计量经济学课程应用形成性评价的具体方案进行探讨。

2调查结果分析自制“计量经济学课程形成性评价调查问卷”调查学生对形成性评价的认识、态度等,以便改进。

Graduates to apply for the quantitative analysis of changes in number ofgraduate students一Topics raisedIn this paper, the total number of students from graduate students (variable) multivariate analysis (see below) specific analysis, and collect relevant data, model building, this quantitative analysis. The number of relations between the school the total number of graduate students with the major factors, according to the size of the various factors in the coefficient in the model equations, analyze the importance of various factors, exactly what factors in changes in the number of graduate students aspects play a key role in and changes in the trend for future graduate students to our proposal.The main factors affect changes in the total number of graduate students for students are as follows：Per capita GDP - which is affecting an important factor to the total number of students in the graduate students (graduate school is not a small cost, and only have a certain economic base have more opportunities for post-graduate) The total population - it will affect the total number of students in graduate students is an important factor (it can be said to affect it is based on source) The number of unemployed persons - this is the impact of a direct factor of the total number of students in the graduate students (it is precisely because of the high unemployment rate, will more people choose Kaoyan will be their own employment weights)Number of colleges and universities - which is to influence precisely because of the emergence of more institutions of higher learning in the school the total number of graduate students is not a small factor (to allow more people to participate in Kaoyan)二 Establish ModelY=α+β1X1+β2X2+β3X3+β4X4 +uAmong them, theY-in the total number of graduate students (variable)X1 - per capita GDP (explanatory variables)X2 - the total population (explanatory variables)X3 - the number of unemployed persons (explanatory variables) X4 - the number of colleges and universities (explanatory variables)三、Data collection1.date ExplainHere, using the same area (ie, China) time-series data were fitted2.Data collectionTime series data from 1986 to 2005, the specific circumstances are shown in Table 1Table 1：Y X1 X2 X3 X41986 110371 963 107507 264.4 10541987 120191 1112 109300 276.6 10631988 112776 1366 111026 296.2 10751989 101339 1519 112704 377.9 10751990 93018 1644 114333 383.2 10751991 88128 1893 115823 352.2 10751992 94164 2311 117171 363.9 10531993 106771 2998 118517 420.1 10651994 127935 4044 119850 476.4 10801995 145443 5046 121121 519.6 10541996 163322 5846 122389 552.8 10321997 176353 6420 123626 576.8 10201998 198885 6796 124761 571 10221999 233513 7159 125786 575 10712000 301239 7858 126743 595 10412001 393256 8622 127627 681 12252002 500980 9398 128453 770 13962003 651260 10542 129227 800 15522004 819896 12336 129988 827 17312005 978610 14040 130756 839 1792四、Model parameter estimation, inspection and correction1.Model parameter estimation and its economic significance, statistical inference test. twoway(scatter Y X1)2000004000006000008000001.0e +06twoway(scatter Y X2)2000004000006000008000001.0e +06twoway(scatter Y X3)2000004000006000008000001.0e +06twoway(scatter Y X4)2000004000006000008000001.0e +06graph twoway lfit y X1200000400000600000800000F i t t e d v a l u e sgraph twoway lfit y X2 -200000200000400000600000F i t t e d v a l u e sgraph twoway lfit y X3200000400000600000800000F i t t e d v a l u e sgraph twoway lfit y X42000004000006000008000001000000F i t t e d v a l u e s. reg Y X1 X2 X3 X4Source SS df MS Number of obs = 20F( 4, 15) = 945.14Model 1.2988e+12 4 3.2471e+11 Prob > F = 0.0000Residual 5.1533e+09 15 343556320 R-squared = 0.9960Adj R-squared = 0.9950Total 1.3040e+12 19 6.8631e+10 Root MSE = 18535Y Coef. Std. Err. t P>|t| [95% Conf. Interval]X1 59.22455 6.352288 9.32 0.000 45.68496 72.76413X2 -7.158603 3.257541 -2.20 0.044 -14.10189 -.2153182X3 -366.8774 157.9402 -2.32 0.035 -703.5189 -30.23585X4 621.3348 46.72257 13.30 0.000 521.748 720.9216_cons 270775.2 369252.9 0.73 0.475 -516268.7 1057819Y = 59.22454816*X1- 7.158602346*X2- 366.8774279*X3+621.3347694*X4 （6.352288）（3.257541）（157.9402）（46.72256）t= （9.323341）（-2.197548）（-2.322889）（13.29839）+ 270775.151（369252.8）（0.733306）R2=0.996048 Adjusted R-squared=0.994994F=945.1415 DW=1.596173Visible, X1, X2, X3, X4 t values are significant, indicating that the per capita GDP, the total population of registered urban unemployed population, the number of colleges and universities are the main factors affecting the total number of graduate students in school.Model coefficient of determination for 0.996048 amendments coefficient of determination of 0.994994, was relatively large, indicating high degree of model fit, while the F value of 945.1415, indicating that the model overall is significant。

国民消费需求与GDP、人均GDP的相关研究分析摘要：GDP是体现国民经济增长状况和人民群众客观生活质量的重要指标。

当今我国的GDP总额排名已经跃居世界第二，仅次于美国之后，经过这些年经济的快速发展已经超过了日本、德国等一众经济强国，而中国的这个第二名是仅根据GDP，即国内生产总值来判定的。

GDP是宏观经济中最受关注的经济统计数字，因为它被认为是衡量国民经济发展情况最重要的一个指标。

而国民消费需求则影响着宏观经济的均衡发展。

本文利用stata软件研究了影响国民消费的因素以及利用1994年至2008年这15年的相关数据针对与GDP有关的因素进行分析说明，判定哪些社会指标、经济指标与GDP存在相关性，并对模型的分析结果进行了经济意义检验，以及统计推断检验。

关键词：GDP、国民经济发展、国民消费需求、stata软件、相关性引言：按照经济学的分析，社会需求包括消费需求，投资需求和净出口。

消费需求作为其中很重要的一部分，对总需求具有很重要的影响，进而对总需求政策的制定也有明显的影响，它影响着宏观经济的均衡发展。

如果说GDP、GNP、人均GDP、人均GNP是衡量国强、民富的重要指标，那么，国民消费需求指数则是衡量国稳、民福的重要指标。

GDP并不是评价一国社会发展的唯一标准，它不能提供大众福利状况的全部真实信息，不能反映民生状况,甚至可能以GDP增长的强势掩盖一个国家发展及社会变革方面的劣势。

我国处于居民消费结构优化升级的发展阶段，较高的国民储蓄率和巨大的国内市场潜力为拉动需求增长提供了物质条件。

相对于GDP，以国民消费需求指数作为衡量经济福利的指标，最大的优点在于引导政府的公共政策应当从追求经济总量的增长，转到追求建立并维系一个健康、公平、正义的宏观制度安排。

虽然中国的经济总量不断扩大，但仍存在发展方式粗放、人均国民收入不高等问题，需要冷静客观地对待。

”所以说，我们应当更客观更理性地根据我国的GDP水平和国民消费指数来评价我国的综合经济实力。

我国限额以上餐饮企业营业额的影响因素分析班级：姓名：学号：指导老师:我国限额以上餐饮企业营业额的影响因素分析摘要：本文收集了1999 —2009共11年的相关数据，选取餐饮企业的数量、城镇居民人均年消费性支出、全国城镇人口数以及公路里程数作为解释变量构建模型，对我国限额以上餐饮企业营业额的影响因素进行分析。

并利用Eviews软件对模型进行参数估计和检验，且加以修正，最后根据模型的最终结果进行经济意义分析，然后提出自己的看法。

关键词：餐饮企业营业额、影响因素、计量分析一、研究背景近十年来，投资者进入餐饮企业的数量一直持递增趋势。

在他们进入一个行业之前，势必要对该行业的营业额、营业利润等进行估计，当这些因素的估计值能够达到他们的预期的时候，他们才会对其进行投资。

由于餐饮企业的营业额是影响投资者是否进入餐饮业的一个重要因素，那么对于我国餐饮企业的营业额问题的深入研究就相当的有必要，这有助于投资者作出合理的决策。

下面即进行了对我国限额以上餐饮企业营业额的计量模型研究。

二、变量的选取影响餐饮企业营业额的因素有很多，包括餐饮企业的数量、营业面积、从业人员、城镇居民人均年消费性支出、全国城镇人口数、餐饮企业的平均价格水平及公路里程数（表示交通状况），但综合考虑后，选取了其中的一部分变量（企业数、城镇居民人均年消费性支出、全国城镇人口数、公路里程数）进行研究，并对各个变量对餐饮企业营业额的影响进行预测。

1. 企业数本文认为餐饮企业营业额与餐饮企业的数量有关，并预测两者之间呈正相关2. 城镇居民人均年消费性支出本文认为餐饮企业营业额与城镇居民人均年消费性支出有关，并预测两者之间呈正相关3. 全国城镇人口数本文认为餐饮企业营业额与全国城镇人口数有关，并预测两者之间呈正相关4. 公路里程数本文认为餐饮企业营业额与公路里程数有关，并预测两者之间呈正相关三、相关数据：其中营业额（单位：亿元），企业数（单位：个），人均年消费性支出（单位：元），全国城镇人口数（单位：万人），公路里程数（单位：万公里）年度营业额企业数人均年消费性全国城镇人口公路里程（丫）（x1）支出（x2）数（x3） 数（x4）1999 3519559 3266 4615.9143748 135.2 2000 4052445 3508 4998 45906 140.3 2001 4898943 4132 5309.01 48064 169.8 2002 6242471 5021 6029.88 50212 176.5 200374700005935 6510.94 52376 181 2004 11605000 10067 7182.1 54283 187.1 2005 12602000 9922 7942.88 56212 334.5 2006 15736000 11822 8696.55 57706 345.7 2007 19072000 14070 9997.47 59379 358.4 2008 25928000 22523 11242.85 60667 373 2009 268640002069412264.5562186386.1四、模型的设定 先查看其散点图：:灿1MM-23000- □11000- u1W0Q*1DOOO-□0 KOT12000-0 X KM*Q008000 •TOOthfk0 05000u 0 g0 y 01------------------------- 1 ----------------------- 1 ----------------------- XJE+OC1S4T20E+O7:]E+O0根据散点图，认为这四个解释变量基本和营业额 （丫）呈现线性关系，所以 假设模型为：丫=刃+ B 1*x1+ 仪*x2+ g*x3+ B 4*X 4+ 卩五、模型的估计根据相关数据,利用统计软件Eviews5对上述设定的模型进行最小二乘估 计，结果如下:Dependent Variable: Y Method Least SquaresDate ： 12/06/11 Time: 21 06Sample 1999 2009 ncluded observations 11VariableCoefficient Std Error t-Statistic Prob.593.3221 75.15516 7.394629 0.00021834.992 299 6427 6.1123934 0.0009-98.66568 68.52235 -1439905 0.20003619.168 3080.442 1 174886 0.2B46-32033592444694 -1 3103310.2380^-squared 0 999109 Mean dependent var12544583Adjusted R^squared 0 998515 S.D. dependent var 8457726 S E. of regression 3259754 Akaike info criterion 28 52999 Sum squared resid 6 38E+11 Schwarz criterion 28 71085 _og likelihood -151.9149 F -statistic1681.475 Durbin-Watson stat 2 715880Prob(F-statistic) 0 000000由上述结果，可得初始的模型为：Y= — 3203359+593.3*x1+1835*x2— 98.7*x3+3619.2*x4六、模型的检验1.拟合优度和模型估计效果检验：n 52OX-xao ooU0X'OC£+«J 1CC+0?20E 神73X+O7OOE+09 10E+U7：0E+<J730E+<J7从回归的结果来看，模型拟合较好，丫变化的99.9%可由其他四个变量的变化来解释。

1© 陈强，《计量经济学及Stata 应用》，2014年。

请勿上传或散发。

第7章 自 相 关 7.1 自相关的后果如果存在i j ≠，使得E(|)0i j εε≠X ，即Var(|)εX 的非主对角线元素不全为0，则存在“自相关”(autocorrelation)或“序列相关”(serial correlation)。

2在有自相关的情况下：(1) OLS 估计量依然无偏且一致，因为在证明这些性质时，并未用到“无自相关”的假定；(2) OLS 估计量依然服从渐近正态分布；(3) OLS 估计量方差Var(|)b X 的表达式不再是21()σ-'X X ，因为2Var(|)σ≠εX I ，通常的t 检验、F 检验也失效了；(4) 高斯-马尔可夫定理不再成立，OLS 不再是BLUE 。

3假设扰动项存在正自相关，即E(|)0i j εε>X ，参见图7.1。

图7.1 自相关的后果4在图7.1中，实线表示真实的总体回归线。

如果10ε>，由于扰动项正自相关，则20ε>的可能性也很大。

如果10n ε-<，则0n ε<的可能性也很大。

此时，样本回归线(虚线)很可能左侧翘起、右侧下垂，使得对回归线斜率的估计过小。

反之，如果10ε<，由于扰动项正自相关，故20ε<的可能性也很大。

如果10n ε->，则0n ε>的可能性也就很大。

此时，样本回归线(虚线)很可能左侧下垂、右侧翘起，使得对回归线斜率的估计过大。

由于自相关的存在，使根据样本数据估计的回归线上下摆动幅度增大，导致参数估计变得不准确。

OLS估计忽略了扰动项自相关的信息，故不是最有效的估计方法。

7.2 自相关的例子(1) 时间序列的自相关：经济活动通常具有某种连续性或持久性。

比如，相邻两年的GDP增长率、通货膨胀率。

又比如，某意外事件或新政策的效应需逐步释放出来。

再比如，最优资本存量需通过若干年的投资才能达到(滞后的调整过程)。

计量经济学课程论文论文题目：影响中国税收收入增长的主要原因姓名：***学号：***********专业:劳动与社会保障指导教师：2016-12-07影响中国税收收入增长的主要原因摘要：本文基于1978-2005年的财政支出状况,对税收收入的影响主要因素做了回归分析，启用了stata软件，用最小二乘法得出了相关系数,并对回归结果进行实证检验,结果证实：影响税收收入的主要因素，国内生产总值，财政支出、商品零售物价指数，对税收收入有显著性影响，这与理论分析和经验判断相一致。

关键词：财政收入回归分析影响因素验证一致一. 问题的提出税收对社会经济、生活等产生的各种影响或效果。

这也是税收内在职能的外在表现，常常受社会客观条件以及税收具体制度的制约，不同的社会制度，不同的生产力发展水平，不同的经济运行模式,以及人们对税收作用的主观认识，都会影响税收作用的发挥，造成税收的具体作用在广度和深度上存在差异，改革开放以来，随着经济体制改革的深化和经济的快速增长，中国的财政收支状况发生很大变化，中央和地方的税收收入1978年为519.28亿元，到2005年已增长到28778.54亿元，税收收入大大增加，表明我国经济发展良好，为了研究影响中国税收收入增长的主要原因，我们在此建立计量回归模型进行分析，这就是本项目研究的主要目的二.理论分析为了全面反映中国税收增长的全貌,选择包括中央和地方税收的“国家财政收入"中的“各项税收”（简称“税收收入”）作为被解释变量；选择国内生产总值（GDP）作为经济整体增长水平的代表；选择中央和地方“财政支出" 作为公共财政需求的代表；选择“商品零售价格指数"作为物价水平的代表.三。

模型的设定1.我国税收的变化状况改革开放以来，随着经济体制改革的深化和经济的快速增长，中国的财政收支状况发生很大变化,中央和地方的税收收入1978年为519.28亿元，到2002年已增长到17636.45亿元，25年间增长了33倍，为了研究影响中国税收收入增长的主要原因,分析中央和地方税收收入的增长规律，预测中国税收未来的增长趋势,需要建立计量经济模型！2。