Quantitative Economic Analysis of Finland 03122014

An investigation into the main determinants effecting the annual %

change in Finnish inflation from 1980-2014

[email protected]

George Gazzard

12/12/2014

Key words:

Phillips Curve, Economic Turmoil, Quantity Theory of Money

Abstract:

This research paper will determine the effect Finnish Macro-Economic indicators have on influencing %∆∈ Inflation within Finland. Further, we will test whether the recent ‘2007 Global Recession’ proves significant in influencing any change in Finnish Inflation.

Our empirical evidence supports our hypothesis to some extent and suggests that European Interest Rates, Finnish Exchange Rate and Unemployment Rate are all significant. However, the money supply is insignificant and the inclusion of our dummy variable has little significance on determining inflation statistical significant. However, BVC are not

mailto:[email protected]

George Gazzard Determinants of Finnish Inflation ∆I7953605

ContentsIntroduction.......................................................................................................................................2

Methodology.....................................................................................................................................2

Theory and Literature Supporting Variables......................................................................................2

Dependent Variable –....................................................................................................................2

Independent Variables –................................................................................................................3

Hypothesis.........................................................................................................................................5

Assumptions......................................................................................................................................5

Ordinary Least Squares Model:.........................................................................................................5

Gauss Markov Conditions:.............................................................................................................5

Estimation of the Parameters of the Model......................................................................................9

Limitations.......................................................................................................................................10

Data Analysis...................................................................................................................................11

References.......................................................................................................................................12

Appendix (A):...................................................................................................................................13

Appendix (B):...................................................................................................................................14

Appendix (C):...............................................................................................................................15

1


Introduction

As Roger (2009) 1 highlights, Finland had ceased Inflation Targeting when they entered the euro in January 1999. Nevertheless, Banerjee (2005)2 argues Inflation will still be regarded as one of the most reflective macro-economic indicators for Finland and is subsequently a major focus point of Finnish/Euro economic policy. We will examine variables which contribute towards inflation, including Exogenous Shocks, Finnish Demand-Pull Inflation (excess aggregate demand relative to aggregate supply) and the Keynesian developed Cost-Push Inflation (derived by rising prices in goods and services).

In this study, I will be evaluating which macroeconomic variables within Finland have the most significant relationship upon Finnish%∆∈ Inflation, as measured in this study through the Consumer Price Index (CPI). Historically, Muhleisen (2006) found that monetary conditions index, stumpage prices and effective exchange rate were amongst the strongest indicators.

Methodology

In this empirical study all collated data has been based upon annual averages from January 1980 – January 2014, providing a total of c33 observations for each variable. Further, to assist our choice of explanatory variables in the model we will identify macroeconomic indicators in which we believe, have the most significance on Finnish Inflation and which can subsequently be supported by existing literature. Our Explanatory variables in the model are:

Euro Interest Rates, % Change in Quasi Money Supply (M2), Exchange Rate, Unemployment Rate, and Economic Turmoil (Dummy variable)

Theory and Literature Supporting Variables

Dependent Variable –

%∆∈Finnish Inflation– Measured through by Consumer Price Index (CPI) : Juselius (2006)3 argues that no single consensus theory of inflation has emerged of yet. Further, evidence from other inflation studies, such as Hendry (2001)4, generally suggests that that the causes of Inflation are

1 Roger, S. (2009). IMF Working Paper. Inflation Targetting at 20. 09 (1), 2362 Banerjee, A. (2005). Leading indicators for Euro area inflation and GDP growth. Oxford Bulletin of Economics and Statistics. 67 (3), 785-813.

3 Juselius, M. (2006). Estimating the determinants of finnish inflation.Discussion Papers. 133 (133), 2. –

4 Hendry, D. (2001), ‘Modelling UK Inflation, 1875-1991’, Journal of Applied

Econometrics 16, 255–275.

2

George Gazzard Determinants of Finnish Inflation ∆I7953605numerous and varied. We will try to determine an OLS regression model which will provide unbiased explanatory value for the inflation change through our independents.

Independent Variables –

Change in Quasi Money Supply (M2): Given ceteris paribus, M2 will effect inflation if circulation velocity5 of money is low and growth of Real Output is slower than growth in M2. Urich (1984)6 states that in a Neoclassical simplified model, an increase in money supply, would lead to an increase in Finnish Aggregate Demand (AD). If this was not met with an increase in Long Run Aggregate Supply there would be no increase in Real Output, only inflation. Further, Thomas (2012)7 justifies my choice in arguing that inflation actually follows M2 growth, even with a fall in the velocity of money. Our inclusion of Quasi Money Supply accounts for Saving, Bonds and Deposits and is measured through an annual average.

Unemployment Rate (Annual CPS Measurement): Milton Friedman’s (1958) Phillips curve in Figure 1, suggests the presence of an inverse relationship between Unemployment rates and Corresponding Inflation rates in the short run8. This therefore justifies the inclusion of Unemployment rates within our model for determining Finnish Inflation. Figure 1 shows how Keynesian interaction in stimulating an economy could lower Unemployment (Pe 8% - 5%) however, increase inflation through shift in equilibrium from (Y-W) and subsequently effect Inflation (8%-5). This is due to increase in wage rates, in competition for new labor. Subsequently they have to increase price levels to cover higher wages causing a spiral effect. (Economics Online, 2013)9

5 Quantity Theory of Money: MV=PY6 Urich, T. (1984). The effects of inflation and money supply announcements on interest rates. The journal of finance. 39 (4), 4-77 Thomas, C. (2012). On Inflation, M2 and the velocity of money. Available: http://www.zerohedge.com/contributed/2012-08-10/inflation-m2-and-velocity-money. Last accessed 12/12/2014.8 Phelan, J. (2012). Milton Friedman and the rise and fall of the Phillips Curve. Available: http://www.thecommentator.com/article/1895/milton_friedman_and_the_rise_and_fall_of_the_phillips_curve. Last accessed 11/12/20149

Economics Online, 2013. The Phillips Curve [online]. Economics Online. Available from:

http://www.economicsonline.co.uk/Global_economics/Phillips_curve.html [Accessed 8 December 2014].

3

George Gazzard Determinants of Finnish Inflation ∆I7953605 Euro Interest Rates: The 2007 ‘Economic Crisis’ saw a fierce cut in EU interest rates (5%-0.5%)10 to stimulate Aggregate Demand within the Eurozone –this cut saw minor increases in AD subsequently increase the inflation rate. Smith (2004)11 states the presence of a clear relationship between inflation and interest rates is due to the awareness of effects, an interest rate increase can have on curbing inflation in a period of strong growth and vice versa. Taylor (1993)12 also went as far in saying that for every 1% increase in inflation, interest rates should rise ≥1 adding foundation to the belief in a strong relationship between the two variables.

Finnish Exchange Rate (Euro € v US $): Historically, Muhleisen (2006)13 argued monetary conditions index, stumpage prices and effective exchange rate were amongst the strongest indicators for Finnish inflation. CNB (2005) analysis of exchange rates influencing Inflation rates through ‘direct channel’ & ‘indirect channel’ enforces Muhleisen’s suggestion, whilst outlining time lags exist within this relationship.

Economic Turmoil: Further, presence of Economic Turmoil data through creation of a Dummy Variable, will allow for investigation into the effect of Economic ‘downturn’ (2007-2014) on Finnish Inflation ∆ . Neoclassical theory suggests it is more common to experience inflation with increased AD and limited AS growth.14

10 Bank of England. (2014). Economy Tracker: Interest Rates. Available: http://www.bbc.co.uk/news/business-11013715. Last accessed 11/12/2014.11 Smith, D. (2004). Inflation and Interest Rates. Available: http://www.economicsuk.com/blog/000065.html. Last accessed 11/12/2014.12 Taylor, J. (1993). Taylors Rule. Taylor Principle. 1 (3), 4-6.13 Juselius, M. (2006). Estimating the determinants of finnish inflation.Discussion Papers. 133 (133), 2. 14 Bruno, M. (1979). Supply vs. Demand Approaches to the Problem of Stagflation. The National Bureau of Economic Research. 382 (2), 382

4

‘1’ – Period of Economic Turmoil

‘0’ – Period of no significant Economic Downturn


Hypothesis

H 0 : β1=β2=β3=β4=β5=0

Assumptions

In my model I assume that my independents are the most significant determinants of Finnish Inflation. Further, I assume there are no other exogenous variables influencing the fluctuation in Finnish Inflation. Moreover, Assumption over inclusion of all demand and supply side variables

We will conduct an OLS regression. For our regression to be the best linear unbiased estimator for my parameters my data will have to meet some critical assumptions:

Ordinary Least Squares Model:

Gauss Markov Conditions:

1 s t Assumption that the data is linear (and Y is found by following the process of regression below) can be seen through inspection of our residuals versus predicted values matrix (Appendix A) which shows presence of symmetrical distribution around regression line.

ikikii XXY εβ...ββ 110

I therefore propose my model as:

%∆ Inflation = β0 + β1 ( Economic turmoil ) +β2 (Euro Interest Rates )+β3 (Exchange Rate )+β4 (Unemployment Rate )+β5

(QuasiChange∈Money SupplyM 2)

5

George Gazzard Determinants of Finnish Inflation ∆I79536052 n d To prove my independent variables have no effect on disturbance within my model; the standardised residuals should have a mean of 0 (See Descriptive below)

Taking into account skewness and kurtosis, the Jarque-Bera normality test score of (1.183), coupled with a visual inspection of Figure 2’s plot of residuals has provided us with basis to believe residuals within the model are normally distributed. Test statistic (1.183) is less than chi-squared table critical value.15

The provision of our Mean at (7.96e-18) indicates that our mean lies close to 0. A statistical rounding error in our findings is the reason distorted this descriptive. These results provides us with confidence that are residuals are normally distributed around the mean = 0.

Error Term AssumptionsMy disturbance terms are random (Please see Figure 1), shown by:

E (ε )≈0

15 x(2)2 =5.99 at 5% level,

6

0]ε,Cov[]εE[ ii iX

0

1

2

3

4

5

6

7

8

9

-0.015 -0.010 -0.005 0.000 0.005 0.010 0.015 0.020

Series: ResidualsSample 1981 2013Observations 33

Mean 7.96e-18Median -0.001127Maximum 0.019326Minimum -0.014380Std. Dev. 0.007581Skewness 0.445510Kurtosis 3.257929

Jarque-Bera 1.183109Probability 0.553466

Figure 1.

George Gazzard Determinants of Finnish Inflation ∆I79536053 rd Independent variables have no correlation with the error term

Dependent Variable: INFLATIONCHANGEMethod: Least SquaresDate: 12/12/14 Time: 07:41Sample (adjusted): 1981 2013Included observations: 33 after adjustmentsWhite heteroskedasticity-consistent standard errors & covariance

Variable Coefficient Std. Error t-Statistic Prob.

C 0.134379 0.027702 4.850818 0.0000EURO_INTEREST_RATE 0.008835 0.000945 9.349836 0.0000

EX__RATE -0.001171 0.000288 -4.061810 0.0004UR -0.004440 0.000396 -11.19837 0.0000

P_CHANGE_M2 0.003380 0.006468 0.522553 0.6055ECTUR 0.009086 0.004099 2.216676 0.0353

R-squared 0.922290 Mean dependent var 0.033654Adjusted R-squared 0.907899 S.D. dependent var 0.027453S.E. of regression 0.008331 Akaike info criterion -6.574603Sum squared resid 0.001874 Schwarz criterion -6.302511Log likelihood 114.4810 Hannan-Quinn criter. -6.483052F-statistic 64.08909 Durbin-Watson stat 1.564837Prob(F-statistic) 0.000000 Wald F-statistic 48.12569Prob(Wald F-statistic) 0.000000

7

Dependent Variable: INFLATIONCHANGEMethod: Least SquaresDate: 12/12/14 Time: 08:13Sample (adjusted): 1981 2013Included observations: 33 after adjustmentsWhite heteroskedasticity-consistent standard errors & covariance


C 0.158095 0.030001 5.269603 0.0000EURO_INTEREST_RATE 0.008454 0.000925 9.137048 0.0000

EX__RATE -0.001296 0.000298 -4.355977 0.0002UR -0.004979 0.000450 -11.05578 0.0000

P_CHANGE_M2 -0.003689 0.006021 -0.612745 0.5450

R-squared 0.911417 Mean dependent var 0.033654Adjusted R-squared 0.898762 S.D. dependent var 0.027453S.E. of regression 0.008735 Akaike info criterion -6.504255Sum squared resid 0.002136 Schwarz criterion -6.277512Log likelihood 112.3202 Hannan-Quinn criter. -6.427963F-statistic 72.02204 Durbin-Watson stat 1.282911Prob(F-statistic) 0.000000 Wald F-statistic 57.83461Prob(Wald F-statistic) 0.000000


Both Durbin-Watson scores, which test for heteroskedasticity and serial correlation for models including (1.564DW) and excluding (1.282DW) Economic Turmoil, provide Durbin-Watson statistics which lie between dL and dU on the Savin and White tables. This renders the test inclusive for any findings, as we can neither reject nor accept the null hypothesis. This will be a limitation of our model results; however this is not a fundamental floor to the model.

4 th Testing for Multicollinearity :

Inspection of my first models correlation matrix and VIF score shows that my Money Supply Variable has significant levels of correlation with other independent variables. (See Appendix B)

Column1Economic Turmoil

P_CHANGE_M2

Unemploy_Rate

Exchange_Rate

EU_Interest_Rate

Iflation_Change

Economic Turmoil 1 -0.130623 -0.117053 -0.388956 -0.518066 -0.165529

P_CHANGE_M2-

0.130623 1 -0.287714 0.115637 0.078274 0.188467Unemploy_Rate

-0.117053 -0.287714 1 -0.584562 -0.288778 -0.613931

Exchange_Rate-

0.388956 0.115637 -0.584562 1 0.748696 0.592171EU_Interest_Rate

-0.518066 0.078274 -0.288778 0.748696 1 0.810891

Iflation_Change-

0.165529 0.188467 -0.613931 0.592171 0.810891 1Our revised model (taking the percentage change in money supply) shows the correlation matrix without any multicollinearity. This means that my coefficient is now - non biased.

Further, our VIF scores for my independent variables indicate absence of multicollinearity.

8


5 t h The Gauss-Markov Theorem further assumes that variance of the error term is a constant for all observations and in all time

periods. Formally, this assumption implies that the error is homoskedastic. If the variance of the error term is not constant, then the error terms are said to be heteroskedastic. Our figure for Chi-Square (19) White Test is (0.3854), suggesting our error terms to be heteroskedastic.

Estimation of the Parameters of the ModelEstimation Output Showing Correlation Coefficients

%∆ Inflation = 0.134 + 0.009 (Economic turmoil ) + 0.009 (Euro Interest Rates )−0.001 (Exchange Rate )−0.004 (Unemployment Rate )+0.003(QuasiChange∈Money SupplyM 2)

9

Model

Collinearity

Statistics

Tolerance VIF

1 (Constant)

Ex. Rate .267 3.750

UR .446 2.243

EcTur .576 1.736

Euro Interest

Rate.373 2.684

P_Change_M

S.853 1.173

a. Dependent Variable: Inflation Change


Analysis

Further, the R squared value for both models is high at (0.9223 & 0.9114). This means that around 92% of the model is explained by my independent variables. The coefficient for our European interest rate is (0.0088). This means that an increase by 1 to the Euro Interest Rate will lead to an increase in the % change of inflation by (0.0088). This positive association is unexpected as you would normally associate rises in inflation with reductions in the rate of interest. This contradicts typical monetarists understanding of the typical inverse relationship between these two variables and confirms Hendry (2001)16 to be correct in his perception in difficulty of establishing Finnish Determinants. Moreover, when the dummy variable is added to the model, the coefficient reduces marginally; this may be explained through the violent house price cycle Finland witness from 2001 - 2008. The volatility was mainly due to strong economic growth, wage growth and changes in tax system.

In my model Exchange Rate has a negative coefficient (-0.0011) this means an increase by 1 to the Finnish exchange rate will lead to a fall in the % change of inflation. This can be explained by Aurikko (1982)17 on the Finnish economies importation of inflation, leading to an increase in annual change. When the dummy variable is added to my model, this value reduces marginally to -0.0012. This increase can be explained by small deviations in Finnish current account through Imports and Exports.

16 Hendry, D. (2001), ‘Modelling UK Inflation, 1875-1991’, Journal of Applied

Econometrics 16, 255–275.17 Aurikko, E. (1982). Exchange Rate policy and imported inflation in a credit rationing economy. Empirical Economics. 7 (3), 45-60

10

% Change Inflation Coefficient Coefficient Prob.With Economic

TurmoilWithout Economic

Turmoil

Euro Interest Rate 0.0088 0.0084 0.0000

Exchange Rate -0.0011 -0.0012 0.0002

Unemployment Rate -0.0044 -0.0049 0.0000

Change Money Supply 0.0033 -0.0036 0.5450

(Economic Turmoil) 0.009 N/A

Durbin Watson 1.5648 1.2829R Squared 0.9223 0.9114

George Gazzard Determinants of Finnish Inflation ∆I7953605In my model the Unemployment rate Coefficient is -0.0044, this means an increase in the unemployment rate by 1 will lead to a decrease in the % change in inflation. This falls in line with the Philips curve theory highlighted in my literature review. When the economic turmoil dummy variable is added to my model, the coefficient reduces to -0.0049 this means that the responsiveness of the change in the % change of inflation has increased, however only marginally.

The Change in the money supply (m2) however is statistically insignificant. This means that for this time period the quantity theory of money does not hold for this regression. Taylor (1993)18 argument that interest rates should rise in line with inflation is contradicted through my findings. The Euro Interest Rate moves from (0.0088 – 0.0084) displaying a small reduction in the Euro Interest Rate in line with a theoretical increase in inflation. Urich (1984)19 and Thomas (2012)20 evaluation of Quasi Money Supply in being significant in determining inflation is incorrect within our model. Our findings show the change in Money Supply to be insignificant in influencing %∆ Inflation, with or without Economic Turmoil.

When you add the economic turmoil dummy to the model, we can see the coefficient for Exchange rate reduces. This means that the impact on the change of inflation is reduced. This can be explained by the reduction in value of the Euro in a period of economic downturn - As Governments Monetary policy in reducing Interest Rates to stimulate Aggregate Demand, see’s outflow of Capital and thus depreciation of Euro.

To conclude, Economic turmoil is not significant in effecting Finnish Inflation as seen through insignificant difference from Eco turmoil inclusion/ exclusion.

Limitations

I believe the absence of historical Housing prices may have limited the validity of the model. Housing constitutes for the largest part of household wealth in Finland, changes in house prices probably have effects that extend beyond the housing market, arguably a significant effect on the percentage change in inflation rate. (Barot 1998)21

Data Analysis

All data sourced from:

http://data.worldbank.org/country/finland#cp_wdi

http://www.ons.gov.uk/ons/guide-method/method-quality/general-methodology/data-collection-methodology/index.html

18 Taylor, J. (1993). Taylors Rule. Taylor Principle. 1 (3), 4-6.19 Urich, T. (1984). The effects of inflation and money supply announcements on interest rates. The journal of finance. 39 (4), 4-720 Thomas, C. (2012). On Inflation, M2 and the velocity of money. Available: http://www.zerohedge.com/contributed/2012-08-10/inflation-m2-and-velocity-money. Last accessed 12/12/2014.21 Barot, B. (1998). House Prices and Inflation. A cointegration Analysis for Finland and Sweden. 1 (12), 12/98

11


ReferencesAurikko, E. (1982). Exchange Rate policy and imported inflation in a credit rationing economy. Empirical Economics. 7 (3), 45-60

Banerjee, A. (2005). Leading indicators for Euro area inflation and GDP growth. Oxford Bulletin of Economics and Statistics. 67 (3), 785-813.

12

George Gazzard Determinants of Finnish Inflation ∆I7953605Barot, B. (1998). House Prices and Inflation. A cointegration Analysis for Finland and Sweden. 1 (12), 12/98

Bank of England. (2014). Economy Tracker: Interest Rates. Available: http://www.bbc.co.uk/news/business-11013715. Last accessed 11/12/2014.

Bruno, M. (1979). Supply vs. Demand Approaches to the Problem of Stagflation. The National Bureau of Economic Research. 382 (2), 382

Economics Online, 2013. The Phillips Curve [online]. Economics Online. Available from: http://www.economicsonline.co.uk/Global_economics/Phillips_curve.html [Accessed 8 December 2014].

Hendry, D. (2001), ‘Modelling UK Inflation, 1875-1991’, Journal of AppliedEconometrics 16, 255–275.

Juselius, M. (2006). Estimating the determinants of finnish inflation.Discussion Papers. 133 (133), 2.

Juselius, M. (2006). Estimating the determinants of finnish inflation.Discussion Papers. 133 (133), 2. –

Phelan, J. (2012). Milton Friedman and the rise and fall of the Phillips Curve. Available: http://www.thecommentator.com/article/1895/milton_friedman_and_the_rise_and_fall_of_the_phillips_curve. Last accessed 11/12/2014

Roger, S. (2009). IMF Working Paper. Inflation Targeting at 20. 09 (1), 236

Smith, D. (2004). Inflation and Interest Rates. Available: http://www.economicsuk.com/blog/000065.html. Last accessed 11/12/2014.

Thomas, C. (2012). On Inflation, M2 and the velocity of money. Available: http://www.zerohedge.com/contributed/2012-08-10/inflation-m2-and-velocity-money. Last accessed 12/12/2014.

Urich, T. (1984). The effects of inflation and money supply announcements on interest rates. The journal of finance. 39 (4), 4-7

Appendix (A):

13

George Gazzard Determinants of Finnish Inflation ∆I7953605Serial Correlation of Independents against Dependent Variable (%∆ Inflation):

Appendix A: represents the linearity between individual independents and my dependent, %∆∈ Inflation. From inspection of residuals versus predicted values matrix the model meets the first criteria in showing that to some extent my independent variables are linear, in comparison with %∆ Inflation due to the presence of symmetrical distribution around the regression line.

14

0

1

EC

TU

R

0

4

8

12

16

Eur

o In

tere

st R

ate

80

100

120

140

Ex.

Rat

e

0E+00

1E+11

2E+11

3E+11

Qua

si M

oney

Sup

ply

M2

Cur

rent

LC

U

0

5

10

15

20

UR

.00

.04

.08

.12

0 1

ECTUR

INF

LAT

ION

CH

AN

GE

0 4 8 12 16

Euro Interest Rate

80 100 120 140

Ex. Rate

0E+00 1E+11 2E+11 3E+11

Quasi Money Supply M2 Current LCU

0 5 10 15 20

UR

.00 .04 .08 .12

INFLATIONCHANGE

George Gazzard Determinants of Finnish Inflation ∆I7953605Appendix (B):

Column1Economic Turmoil

EU_Interest_Rate

Exchange_Rate

Iflation_Change Q_MS

Unemploy_Rate

Economic Turmoil 1 -0.518066 -0.388956 -0.165529

0.861177 -0.117053

EU_Interest_Rate -0.518066 1 0.748696 0.810891

-0.81436 -0.288778

Exchange_Rate -0.388956 0.748696 1 0.592171

-0.62446

1 -0.584562

Iflation_Change -0.165529 0.810891 0.592171 1

-0.45338

4 -0.613931Q_MS 0.861177 -0.81436 -0.624461 -0.453384 1 0.012403Unemploy_Rate -0.117053 -0.288778 -0.584562 -0.613931

0.012403 1

Appendix B: demonstrates my regression model prior to taking the percentage change for Quasi Money Supply M2. The Highlighted Red area indicates the presence of multicollinearity.

Appendix (C):

15


16

Heteroskedasticity Test: White

F-statistic 1.073386 Prob. F(19,13) 0.4584Obs*R-squared 20.15351 Prob. Chi-Square(19) 0.3854Scaled explained SS 17.78806 Prob. Chi-Square(19) 0.5366

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 12/12/14 Time: 07:42Sample: 1981 2013Included observations: 33White heteroskedasticity-consistent standard errors & covarianceCollinear test regressors dropped from specification


C -0.000205 0.006166 -0.033329 0.9739EURO_INTEREST_RATE^2 8.11E-06 4.16E-06 1.949447 0.0731

EURO_INTEREST_RATE*EX__RATE -8.48E-06 3.15E-06 -2.694296 0.0184EURO_INTEREST_RATE*UR 8.37E-06 1.24E-05 0.674681 0.5117

EURO_INTEREST_RATE*P_CHANGE_M2 0.000785 0.000601 1.305517 0.2143EURO_INTEREST_RATE*ECTUR -5.46E-05 0.000229 -0.238058 0.8155

EURO_INTEREST_RATE 0.000656 0.000356 1.841494 0.0885EX__RATE^2 5.72E-07 5.33E-07 1.074497 0.3021

EX__RATE*UR -5.21E-07 2.16E-06 -0.241031 0.8133EX__RATE*P_CHANGE_M2 -0.000138 9.31E-05 -1.479817 0.1627

EX__RATE*ECTUR -2.53E-05 3.86E-05 -0.656041 0.5232EX__RATE -4.23E-05 0.000116 -0.362870 0.7225

UR^2 5.47E-07 3.85E-06 0.142161 0.8891UR*P_CHANGE_M2 7.03E-06 0.000127 0.055137 0.9569

UR*ECTUR 2.37E-05 8.14E-05 0.291161 0.7755UR -4.23E-05 0.000209 -0.202524 0.8426

P_CHANGE_M2^2 0.003089 0.002249 1.373472 0.1928P_CHANGE_M2*ECTUR 0.003532 0.003060 1.154322 0.2691

P_CHANGE_M2 0.008332 0.006884 1.210252 0.2477ECTUR^2 0.002294 0.003593 0.638295 0.5344

R-squared 0.610712 Mean dependent var 5.68E-05Adjusted R-squared 0.041753 S.D. dependent var 9.37E-05S.E. of regression 9.17E-05 Akaike info criterion -15.47603Sum squared resid 1.09E-07 Schwarz criterion -14.56906Log likelihood 275.3546 Hannan-Quinn criter. -15.17086F-statistic 1.073386 Durbin-Watson stat 2.828613Prob(F-statistic) 0.458375

Quantitative Economic Analysis of Finland 03122014

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